👋 Hey, Sahil here - Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

Big idea + report of the week :

• Do you really need revenue to raise a seed round, or are founders overthinking it?

• The AI categories VCs are overpaying for right now.

Frameworks & insightful posts :

• The Billion-Dollar Founder Study: What 20 years of unicorns say about who you should build with.

• Are you overpaying your first hire with equity without realising it?

• Are waitlists still worth building in 2026: a16z Guide?

FROM OUR PARTNER - FRAMER

🚀 Launch fast. Design beautifully. Build your startup on Framer — free for your first year.

First impressions matter. With Framer, early-stage founders can launch a beautiful, production-ready site in hours. No dev team, no hassle. Join hundreds of YC-backed startups that launched here and never looked back.

Pre-seed and seed-stage startups new to Framer can enjoy:

• One year free: Save $360 with a full year of Framer Pro, free for early-stage startups.

• No code, no delays: Launch a polished site in hours, not weeks, without hiring developers.

• Built to grow: Scale your site from MVP to full product with CMS, analytics, and AI localisation.

Join YC-backed founders: Hundreds of top startups are already building on Framer.

Apply to claim your free year →

🤝 PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

START WITH

🧠 Big idea + report of the week

Do you really need revenue to raise a seed round, or are founders overthinking it?

Most founders assume there’s a clear benchmark.

• Hit $1M ARR → raise

• Hit $500K ARR → maybe raise

• Below that → not ready

But this data shows something very different.

Silicon Valley Bank’s latest report (2024–2025 U.S. tech companies) puts the median seed-stage revenue at ~$220K. Sounds reasonable, until you look deeper.

Because there is no single number. Revenue at seed is completely spread out:

• Some companies start with $0 revenue

• Many sit around $200K–$300K

• Strong ones push toward $700K+

• Outliers go all the way to $3M+

Same round. Completely different starting points.

That’s the first shift founders need to understand:

You’re not being evaluated on a fixed threshold. You’re being evaluated on trajectory and belief.

And that’s why comparing yourself to what you see online quietly kills momentum. Most of the companies you hear about sit in the top 10%. They are not the market; they are the exception.

The second, more important insight shows up when you track what happens after the seed.

The jump required between rounds is where things get real:

• Seed median → ~$220k

• Series A median → ~$2.5M

• Series B median → ~$6M

This is not gradual growth.

It’s a sharp expectation of acceleration. Which means:

You can raise seed without much revenue, but you cannot drift into Series A. The real pressure starts the moment you raise.

Especially in AI, this gap is becoming even more intense. A lot of seed rounds today are going to:

• Pre-revenue founders

• Fast-moving technical teams

• Products with early signals but no strong monetisation yet

But that also compresses time. You’re expected to go from: idea → signal → revenue engine

…much faster than before. And this is exactly where most companies struggle.

Not at raising. But at catching up. So, isn’t “revenue doesn’t matter.”

It’s this:

• There is no magic number that unlocks seed

• Investors care more about how fast you’re moving than where you are

• The real game starts after you raise, not before

Because today, raising a seed round is increasingly accessible.

Earning your Series A is not.

The AI categories VCs are overpaying for right now.

PitchBook’s 2026 Artificial Intelligence Outlook flags a clear warning: some of the most hyped AI subsectors today are also the most vulnerable to capital destruction.

The common pattern across these areas isn’t a lack of demand; it’s too much sameness.

What’s overheating right now

• AI code generation

So-called “vibe coding” startups built on top of large language models have exploded in traction and funding. Cursor hit $100M ARR in just 12 months.

Cognition doubled revenue in a single quarter. That success pulled in waves of copycats, many offering marginal improvements on the same underlying models. As more capital chases the same workflow, differentiation collapses fast.

• Autonomous driving software

Self-driving AI remains capital-intensive, slow to commercialize, and heavily regulated. Despite years of progress, timelines remain uncertain and competition is brutal. For many startups, the burn curve is steep while defensibility is thin.

• Generative engine optimisation (GEO)

Startups optimising how brands appear inside ChatGPT or Claude resemble early SEO plays but with far less control.

Platforms can change ranking logic overnight, wiping out product value. Analysts see this as especially fragile infrastructure.

• Precision medicine AI

AI-driven genomics and diagnostics promise breakthroughs, but face long validation cycles, regulatory risk, and complex go-to-market paths. Capital is flowing faster than clinical proof.

Why this matters

PitchBook analysts point out that these sectors share a dangerous trait:

lots of startups, little structural differentiation, and heavy dependence on foundation models they don’t control.

When markets crowd this tightly, outcomes don’t average out they skew toward a few winners and widespread losses.

For founders & VCs

AI isn’t overheated everywhere. But in crowded, obvious categories, the window for undifferentiated entrants is closing fast. Future winners will be defined less by “AI inside” and more by distribution, proprietary data, workflow lock-in, and real switching costs.

In the next cycle, value won’t be destroyed quietly; it will be written down all at once.

SOMETHING MORE

🧩 Frameworks & insightful posts

The Billion-Dollar Founder Study: What 20 years of unicorns say about who you should build with.

There’s a very common belief in startups: the best companies are built by people who already know each other well - friends, ex-colleagues, college roommates. It feels logical. Trust is already there, communication is easier, and things move faster in the early days.

Andy Chen and Amy Lin (from Outcast Venture) decided to actually test this idea by analysing 20 years of $1B+ startup exits, manually reconstructing how founding teams were formed. And what they found challenges a lot of what founders assume.

Working together before doesn’t actually lead to bigger outcomes.

Founders who had prior working relationships ended up with lower median exit values compared to those who didn’t. The same pattern shows up with school connections as well. These relationships help you get started, but they don’t give you an edge where it really matters - during scale.

• Prior work relationship → ~$2.3B median exit

• No prior relationship → ~$2.9B median exit

• Same-school founders also show slightly lower outcomes

What this suggests is simple: familiarity reduces early friction, but billion-dollar companies are not decided in the first year. They’ve decided over a decade of building, hiring, and navigating completely new problems.

That’s where a different factor starts to dominate -complementarity.

The best teams aren’t the most comfortable ones. They’re the ones where skills, thinking styles, and strengths don’t overlap too much. As the company scales, that diversity becomes a real advantage.

You see the same pattern when you look at team structure more broadly.

Most large outcomes are not built by solo founders. While solo founding is increasing (especially post-AI), it rarely shows up in the biggest companies.

• 82% of $1B+ companies had multiple founders

• Solo founders account for ~20%, but almost disappear in top-tier outcomes

• Solo companies also take ~3 years longer to reach exit

Starting alone is clearly possible. But sustaining and scaling complexity over 10+ years is where teams outperform.

Another strong signal in the data is experience.

Founders who have been through the startup cycle before - especially those who have already built or exited a company- consistently produce larger outcomes. The gap is meaningful.

• CEOs with prior startup experience → ~41% higher median exit

• Teams with at least one prior exit → nearly 2x higher valuations

This makes sense when you zoom in. Scaling a company isn’t just about product or vision. It’s about hiring at speed, managing investors, navigating downturns, and making decisions under uncertainty. Founders who’ve done it before simply recognize patterns faster.

Time is another underestimated variable.

Most founders think in 3–5 year horizons. But the data shows something very different.

The median time to a billion-dollar outcome is around 12 years, and the largest outcomes often take even longer. Founding CEOs typically stay for a decade or more, which means the real job isn’t launching - it’s continuously rebuilding the company as it grows.

Even the founder's age reflects this dynamic. The median age is around 35—not extremely young, not too late, but more importantly, it aligns with timing.

The biggest outcomes tend to come from founders who hit their prime during major platform shifts.

• 1980s-born founders → mobile and cloud

• 1990s-born founders → now benefiting from AI

So it’s not just about who you are. It’s about when you build.

What’s interesting is what doesn’t matter as much as people think.

Elite schools show up frequently in founder lists, but they don’t correlate with larger exits. Family founding teams are extremely rare. Even long-standing relationships between founders don’t translate into better outcomes.

The pattern across all of this is surprisingly consistent.

Big companies are not built by the most obvious or convenient teams. They are built by small, complementary groups of people who bring different strengths, have some level of experience, and are able to endure and adapt over a long period of time, often aligned with a major technology wave.

The takeaway is simple, but most founders ignore it: Don’t optimize for who feels easiest to start with. Optimize for who you can build with for the next 10–15 years.

Are you overpaying your first hire with equity without realising it?

One of the easiest ways founders quietly mess up their cap table is very early. The first few hires feel equally risky, equally important, and emotionally, you want to treat them the same. But real data shows that equity should drop much faster than most founders expect.

This insight comes from Jason Lemkin, based on Carta’s State of Seed data covering ~50,000 startups. And the pattern is sharper than most pitch-deck math assumes.

Here’s the part most founders miss: only your very first hire truly belongs in the “founding team economics” bucket.

Everyone after that moves into a different risk-reward zone much faster than intuition suggests.

What the data actually shows when you zoom out.

Your first hire sits at a median of ~1.5% equity. That already feels lower than what many founders assume. But the real surprise is what happens next.

The second hire drops to ~0.85%. By hire #3, you are already around ~0.5%. By hire #5, the median is ~0.33%.

This is not a gentle slope. It is a cliff.

In practice, this means:

• The equity drop from hire #1 to hire #2 is about 43% for the very next person who joins.

• Only the very first hire reliably crosses the 1% mark at the median.

• By the fifth hire, you are already in what investors mentally bucket as normal early employee equity.

Why this matters more than founders think.

Most early cap table damage doesn’t come from one huge mistake. It comes from flattening this curve. When founders give 1-1.5% to multiple early employees, they unknowingly burn future flexibility.

Later, when you need to hire a VP of Engineering or CFO at Series A or B, those roles often need meaningful equity. And suddenly, there is nowhere clean for it to come from without painful reshuffling.

There’s also a psychological angle here. The first hire is taking an existential risk. They are joining when there is no product, no traction, and often no salary stability. Hire #4 or #5 is joining something that already exists. The data reflects that reality, even if founders emotionally don’t.

A quick note on advisors, because this is where founders often leak equity quietly.

Advisor equity is dramatically lower than what most founders assume. At the seed stage, the median advisor grant is around ~0.12%. Only the top 10% of advisors cross 0.5%, and almost nobody should touch 1% unless they are delivering something truly existential.

A useful gut check:

If this advisor disappeared tomorrow, would your company materially suffer in the next 6 months? If the honest answer is no, they should not be anywhere near 1%.

So, think of early equity as a decaying asset. It loses value fast as certainty increases. Treat your first hire as a special case, because they are. But do not let that logic spill over to hires two through five.

If you want a simple mental model:

• Hire #1 is almost a co-founder substitute.

• Hire #2 is a major early bet, but already less so.

• Hire #3 onward is execution talent, not existential risk-taking.

The data doesn’t tell you what is right for your company. But it does tell you what normal looks like. And most founders who regret equity decisions later regret being too generous too early, not the other way around.

Use this as a calibration point before you send that next offer letter.

You can also check out out Equity dilution decisions: ownership, control, and negotiation guide for founders.

Are waitlists still worth building in 2026: a16z Guide?

A few years ago, a waitlist meant hype. Ship a landing page, tweet a demo, collect thousands of emails, feel good. Today, that playbook is mostly broken. Attention is harder, inboxes are crowded, and “cool idea” signups rarely turn into revenue.

This shift was neatly unpacked by a16z speedrun in their breakdown on modern waitlists, and the core idea is simple but uncomfortable: a waitlist isn’t a vanity metric anymore. It’s a filter.

The founders who are winning in 2026 are using waitlists as a quality control system, not a popularity contest.

Here’s what actually matters now.

In the past, founders obsessed over list size. Today, the only thing that matters is what happens after someone joins.

Research shows that if you invite users within a month, waitlist-to-paid conversion can hit ~20%. Wait longer than three months, and it often drops below 10%. Big lists decay fast. Waitlists rot if you don’t activate them.

So the real question founders should be able to answer isn’t “how many people signed up?” It’s:

• Who exactly joined

• Why did they join now

• What problem do they think you solve

• And how many of them will actually take the next step

Anything else is just noise.

What does doing it right look like in practice?

The best founders now design waitlists backwards from intent.

Take Day AI. They didn’t launch early. They waited until they had a crisp story, then created a single “spike moment, a coordinated announcement across press, owned content, and social. Everything pointed to one conversion surface: the waitlist. The result wasn’t just volume, it was alignment with their sales capacity.

Another smart pattern came from Paid AI. They treated the waitlist as an intent ladder, not a dead end. Instead of stopping at an email, they progressively asked for more commitment:

• email

• role/industry context

• booking a call

• eventually, money

This is the key mental shift: your waitlist should qualify users, not just store them. Cash, even a small amount, is the strongest signal you’ll ever get.

And if you’re building hardware, this matters even more. Taya didn’t ask people to “stay tuned.” They asked for preorders. Real money. On launch day, they sold one unit every four minutes. That did two things at once: it validated demand before manufacturing, and it revealed exactly who the real customers were. That insight shaped everything that followed, from messaging to roadmap.

So a waitlist is no longer about hype signalling. It’s about learning faster and de-risking earlier. It should tell you who is serious, what they’re willing to do next, and how quickly you can move them forward.

If your waitlist can’t help you prioritise, segment, invite quickly, and measure conversion, it’s not an asset; it’s a distraction.

Build your waitlist like a machine, not a mood board.

NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Gateway Capital Partners, a Milwaukee-based venture firm founded by Dana Guthrie, has announced the first close of its $25M Fund II. (Link)

• Sonder Capital, a San Carlos, CA-based healthcare venture capital firm investing in companies enhancing the standard of care, closed its second fund, Futures II. (Link)

• Overmatch Ventures, an Austin, TX-based early-stage venture capital firm investing in deeptech, defensetech, and spacetech, closed its second fund at $250m. (Link)

New Startup Deals

• Gander Robotics, a Cambridge, MA-based autonomous rescue systems company, raised $1.1M in Pre-Seed funding. (Link)

• estaie, a Dubai, UAE-based hospitality tech startup, raised a 7-figure Pre-Seed funding round. (Link)

• Whirl AI, a San Francisco, CA-based enterprise AI company, raised $8.9M in Seed funding. (Link)

• Linx Security, a NYC-based identity security company, raised $50M in Series B funding. (Link)

• Oncomatrix, a Derio, Spain-based biopharmaceutical company, raised $67M in funding. (Link)

• Centivax, a South San Francisco, CA-based biotech company, raised $37M in funding. (Link)

• daydream, a San Francisco, CA-based AI-native search agency, raised $15M in Series A funding. (Link)

TODAY’S JOB OPPORTUNITIES

💼 Venture capital & startup jobs

All-In-One VC Interview Preparation Guide: With a leading investor group, we have created an all-in-one VC interview preparation guide for aspiring VCs. Don’t miss this. (Access Here)

• Associate - Lightbank | USA - Apply Here

• Associate - Climate Venture Fund | USA - Apply Here

• Senior Investment Analyst - Earth Foundry | USA - Apply Here

• Ventures Analyst/Associate - Plug and Play Tech Centre | USA - Apply Here

• Research Innovation Program Director - Generator | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• Finance Associate - RA Capital | USA - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Program Manager - a16z | USA - Apply Here

• Associate or Senior Associate - AI - BVP | USA - Apply Here

• Venture Fellow - age1 | USA - Apply Here

• Venture Capital Associate - Sky9 Capital | USA - Apply Here

• Investment Summer Associate - AI Tooling - M13 | USA - Apply Here

• Event Marketing Manager - AI Fund | USA - Apply Here

PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

👋 Hey, Sahil here - Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

• Elad Gil’s framework for spotting billion-dollar markets before they look big.

• The AI model shift: Vertical AI is taking over.

• Reddit marketing for B2B saas founders: a step-by-step guide.

• How long does it take for VC funds to return money to LPs?

FROM OUR PARTNER - SEMRUSH

📈 See what drives your competitors’ growth — before they do

Every winning strategy starts with the right data. With Semrush’s Traffic & Market Toolkit, you can uncover exactly where your competitors’ traffic comes from — search, social, or even AI referrals.

Track their top pages, monitor emerging trends, and find the untapped channels fueling their growth. Whether you’re a marketer, agency, or enterprise, you’ll get daily and weekly insights to outpace rivals and lead your market with confidence. Trusted by 10M+ marketers worldwide.

Try the Traffic & Market Toolkit free →

VENTURE CURATORS’ FINDING

📬 My Favourite Finds

Media Posts:

• These are the top 30 prompt techniques that actually work in 2026. (Link)

• Every Startup accelerator you can apply to right now. (2026) (Link)

• Startup legal document pack - essential legal Docs for founders. (Link)

• 30 tactics working right now to improve free-to-play conversion rates. (Link)

• What investors ask and how to answer: A practical Q&A prep kit for founders. (Link)

Reports/Articles:

• Google has been releasing a bunch of free AI tools outside of the main Gemini app. Most are buried in Google Labs. (Link)

• How to build a PMF machine by a16z. (Link)

• The billion-dollar founder study: What 20 years of exits actually say about founding teams. (Link)

• Why free trials don’t make sense anymore. (Link)

PARTNERSHIP WITH US

🤝 Get your brand in front of 116,000+ audience.

Our newsletter is read by 116,000+ tech professionals, founders, investors (VCs / Angel Investors) and managers around the world. Get in touch today.

📜 DEEP DIVE

Elad Gil’s framework for spotting billion-dollar markets before they look big.

Startup history is full of companies that looked like bad ideas at the start.

• An online marketplace for wedding invitations? Too niche.

• A payroll tool in a boring, saturated market? Who cares.

• Another file storage app? Too late.

And yet, those “nonobvious” bets became Minted, Gusto, and Dropbox.

Elad Gil, a longtime operator and investor (Stripe, Coinbase, Instacart, Airbnb), has a simple thesis:

Nonobvious markets can lead to hypergrowth, if you know how to spot and validate them.

Based on years of experience and interviews with top founders, Gil breaks down four principles and three market types that help you find overlooked opportunities with real upside.

First, start with the market. Not the idea.

Elad’s approach is clear: Market > Team > Idea.

Why? Because even great teams can’t escape bad markets. But great markets tend to give you multiple shots — even if your first idea stumbles.

“Many great teams get taken out by a terrible market. But if you’re in a great market, the idea itself doesn’t matter as much — there are always other shots on goal.”
– Elad Gil

So before building or investing, study the underlying market structure.

Ask:

• Is this a growing market with multiple unmet needs?

• Is it structured like a winner-take-all, or more like an oligopoly?

• Are existing players actually serving users well?

Most people get this wrong because they fall in love with an idea. Instead, fall in love with the market.

Four Principles to Uncover the Nonobvious

1. Use first principles, not hot takes.

Don’t just be contrarian for the sake of it. That’s lazy.

Use first principles thinking to ask:

• Is the “common wisdom” still true?

• What’s changed in cost, infrastructure, or tech?

• Has the assumption just aged out?

Example: Payments used to be avoided like the plague. “Too much fraud risk.”
But then Stripe happened. Fraud tooling, APIs, and internet trust improved and suddenly it became obvious in hindsight.

2. Scratch your own itch — or go deep on real pain.

Gil’s favorite startups often come from founders solving problems they’ve lived.

• Gusto founders had family who struggled with bad payroll tools.

• Mailgun was built after the team had to manually rebuild the same email infra over and over.

• Gil’s own startup, Color Genomics, was sparked by his cofounder’s family health history.

This kind of insight gives you edge and a compass.

“The key is that it’s a real problem. Not an idea you plucked out of thin air.”

3. Build product-first. Don’t chase vague customer feedback.

“Would you use this?” is a weak signal.

“Would you pay for this right now?” is a real one.

Break into tough markets with a product that works beautifully, even in crowded spaces.

• Dropbox beat bigger incumbents by nailing file syncing.

• Google flipped the norm by getting users off the site faster, not keeping them longer.

• Zeplin built what their designer-engineer team wished already existed.

If customers are nodding but not buying, you don’t have it yet.

4. Look on the fringe. That’s where the future often starts.

New trends start weird. Crypto, social media,and online payments all looked like toys at first.

Gil reminds us:

“Innovation often comes from small communities screwing around with random stuff.”

Pay attention to:

• What smart people are tinkering with for fun

• What communities are hacking together without outside attention

• What you see enthusiasts doing before the mainstream shows up

Three Market Types Worth Digging Into

If you’re looking for overlooked gold, Elad says these are the best hunting grounds:

1. New Tech

Tech that’s early, misunderstood, or still feels like a toy.

• The mistake? People underestimate compounding growth.

• Ask: Is it doubling year over year? Are costs falling? Is performance improving fast?

Example: In the 1980s, McKinsey told AT&T the total mobile phone market would max out at 900,000 users. That was just off by a few billion.

Tip: Survey your smart friends. What are they playing with or obsessing over?

2. Looks Crowded, But Isn’t

Just because a market is noisy doesn’t mean it’s closed.

• Ask: Are the competitors any good? Are users actually satisfied? Is the core product broken?

Gil invested in Checkr and Gusto because he saw startups struggling with those exact things. If you’re being forced to use a broken tool today, that’s a startup opportunity.

Think:

• Google, despite AltaVista and Yahoo

• Dropbox, despite iCloud and Box

• Instagram, despite Myspace and Facebook

The trick is to look where incumbents are weak or slow, then wedge in with a 10x better product.

3. Seems Niche

Gil breaks this down into 4 subtypes:

• Too small: Uber started with black cars for rich people. Now it’s infrastructure.

• Too boring: Payroll. On-call alerting. File storage. Huge wins, zero sizzle.

• Too high-end: Tesla’s $100K Roadster seemed like a toy. Now look.

• Too personally unfamiliar: Stitch Fix and Glossier were built from deep user empathy that male investors just didn’t have.

“Your entry market will almost always seem small. But that’s the point — it’s where incumbents aren’t looking.”

One More Layer: Watch for Market Timing

Some ideas are great — just not yet.

Gil warns of two patterns:

• False starts: Right idea, bad execution. (Friendster before Facebook)

• Boomerangs: Right idea, wrong timing. (Webvan before Instacart)

Don’t just ask “Is this a good idea?”
Ask “Is the infrastructure ready? Are customer habits there yet?”

If not — be patient. Timing kills more ideas than bad execution.

So Overall here’s a useful lens to spot your own nonobvious startup:

• What’s a product or tool you hate but are forced to use?

• What’s a “boring” problem you or your peers solve manually every month?

• What’s something your smart friends are excited about but others roll their eyes at?

That’s where you dig.

“Grab a shovel and dig where it looks weird. That’s usually the spot.”

You can check out a detailed write-up by First Round here.

📃 QUICK DIVES

The AI model shift: Vertical AI is taking over.

For a long time, the assumption in AI was simple: build on top of the best general-purpose models, and differentiate through product, UX, and workflows.

That assumption is starting to break.

Intercom’s latest launch makes this clear. They’ve moved away from relying on frontier models like GPT or Claude for their core system and built their own domain-specific model (Apex) for customer support and it’s outperforming general models on the metrics that actually matter.

What actually improved (and why it matters)

The gains aren’t just incremental; they hit the exact levers businesses care about:

• higher resolution rate (more issues solved without human help)

• faster responses (lower latency in conversations)

• significantly fewer hallucinations

• lower cost per interaction

In one case, resolution jumped from 68% → 75% overnight, which translates into a meaningful drop in unresolved tickets.

This is important because in categories like customer support, performance isn’t abstract; it directly ties to cost, customer experience, and headcount.

Why vertical models are suddenly winning

The key shift is not just better models, it’s a better training context.

General-purpose models are trained broadly. But companies now have:

• billions of real customer interactions

• domain-specific evaluation systems

• tightly tuned feedback loops

That creates a compounding advantage.

The model isn’t just “smarter”, it’s more aligned to a specific job. It understands tone, edge cases, workflows, and expectations in a way that general models can’t fully match.

This creates a flywheel: better data → better model → better product → more data

The bigger shift: from horizontal → vertical AI

This is where things get interesting.

General models are becoming good enough and widely available, which means they’re no longer a strong differentiator on their own.

So companies are moving toward:

• building their own specialised models

• training on proprietary data

• optimising for specific use cases (support, coding, legal, etc.)

In simple terms: The value is shifting from “who has the best model” → “who has the best data + post-training.”

What this means for startups and builders

A few clear implications:

• If you’re using the same base model as everyone else, differentiation becomes hard

• Proprietary data is becoming the biggest moat

• Full-stack AI (owning model + product) is becoming the winning approach in large categories

We’re already seeing this in coding (Cursor), customer support (Intercom), and likely soon in other verticals.

What this means for AI labs

This doesn’t kill Frontier Labs, but it changes their position.

General models are still ahead, but:

• They often over-serve specific use cases

• Open-weight models are catching up

• Specialised post-training can outperform them in narrow domains

So the pressure shifts to labs to either:

• build vertical models themselves

• partner with companies that have strong data

• or acquire them

As AI is moving toward “speciation”, instead of one model that does everything, we’ll see many models that do specific things extremely well.

And in that world, the winners won’t just be the ones with the best models.

They’ll be the ones with:

• the best data

• the tightest feedback loops

• and the clearest focus on a specific problem

That’s where the next layer of advantage is being built.

Reddit marketing for B2B saas founders: a step-by-step guide.

If you’re a B2B SaaS founder and not leveraging Reddit, you’re leaving leads on the table.

Here’s why promoting your startup on Reddit is a strategic move:

• Massive user growth: In 2024, Reddit hit 97.2M daily active users - a staggering 47% YoY growth. That’s an untapped pool of potential leads.

• SEO advantage: Reddit isn’t just a traffic source; it’s an SEO booster. Google even paid $60M for exclusive indexing rights. Every mention of your SaaS on Reddit helps you rank better.

• AI visibility: Reddit is one of the most cited sources in LLMs like ChatGPT. In fact, around 40% of AI-generated answers reference Reddit. Meaning: your SaaS can show up when people ask AI tools for recommendations.

I recently came across the Reddit Marketing Guide by Adam Shaw, so I thought to share with you some key points and my opinion with you.

And here’s the kicker: 50% of my leads come from just answering questions on Reddit for less than an hour a day.”

Here’s how you can do it

Step 1: Turn Your Profile into a Landing Page

Your Reddit profile is like a mini landing page. It’s the first place curious users will go after reading your comments or posts. Make sure it’s optimized to convert.

How to set it up:

• Choose a username that’s tied to your brand or product.

• Write an actionable profile name (e.g., “Helping SaaS Founders Scale”).

• Add a CTA to your profile banner (e.g., “Download our Free SaaS Marketing Guide”).

• Include your one-line product pitch and CTA in the bio (e.g., “We help SaaS founders grow faster. Subscribe to our newsletter below!”).

• Add links to key resources like:

  • Lead magnets

  • Newsletter sign-ups

  • Product demo

Step 2: Find Subreddits Where Your Audience Hangs Out

Reddit is vast, but your audience is likely concentrated in a handful of niche subreddits.

How to find them:

1. Use Reddit’s search bar to look for your product keywords (e.g., “SaaS,” “startup growth,” etc.).

2. Use Google to search for “your keywords + Reddit” (pro tip: Google is better at finding Reddit threads than Reddit itself).

3. Pick subreddits with active, engaged members in your niche.

Pro tip: Use tools like Gummy Search to automate finding relevant subreddits and posts.

Step 3: Prepare a Lead Magnet

Redditors hate being sold to. They love value first approaches.

That’s where a lead magnet comes in. It’s a way to engage your audience without coming across as too salesy.’

Examples of great lead magnets:

• In-depth guides tailored to specific problems.

• Free trials of your product.

• Newsletters with actionable advice (my favorite).

Make it easy for people to commit. For example, I use a newsletter landing page to capture leads. By the time they get there, I’ve already built trust through helpful comments.

Step 4: Start by Commenting, Not Posting

Engage first, post later.

Why? Comments are more personal and allow you to directly connect with people asking for help. You’ll also learn what your audience really needs.

How to comment like a pro:

• Answer recent posts (24-48 hours old) for better visibility.

• Avoid crowded posts (0-10 comments is the sweet spot).

• Add value. Most comments are generic—stand out by being actionable, specific, and helpful.

Step 5: Be the Best Commenter

The secret to success on Reddit? Write better comments than everyone else.

Here’s how:

• Be highly relevant—answer only what’s asked.

• Be specific—don’t just say “try this.” Explain how to try it.

• Go in-depth—assume the person has zero knowledge.

• Stay friendly—ignore trolls.

Make your comment stand out visually too:

• Use white space for readability.

• Add bullet points to organize your thoughts.

• Be longer than most comments (Reddit loves thoughtful responses).

Step 6: Post to Curate Conversations

Once you’ve spent a few weeks commenting, you’ll notice patterns in what your audience talks about. That’s when you start posting.

But don’t make the mistake of being self-serving. Redditors can smell that a mile away.

Here’s what works:

• Don’t post a blog or self-promo content. Instead, ask questions that spark engagement.

• Let the audience share their thoughts and experiences in the comments.

• Use their responses to build conversations and authority naturally.

Reddit can be a goldmine for B2B SaaS founders if used the right way. Focus on engagement first, provide value, and let the leads come to you.

How long does it take for VC funds to return money to LPs?

Most people assume VC funds start returning money in a few years.

But that’s not how the asset class is designed to work.

Peter Walker from Carta analysed ~2,900 VC funds, and the data shows something most people underestimate: liquidity in venture is painfully slow.

After around 5 years, things start to move, but not in the way most expect.

• ~50% of funds have returned at least $1 (some DPI)

• But only ~7–8% have returned the full capital (1x DPI)

So yes, money starts coming back… but meaningful returns are still rare.

Even if you go further out, the picture doesn’t change much.

The 2017 vintage, the most mature in the dataset, has a median DPI of just ~0.27x.
That means most funds haven’t even returned 30% of capital after ~7–8 years.

And here’s the part most people get wrong:

Early DPI isn’t always a good sign.

• If money comes back in 2-3 years, it’s often from smaller exits

• Those returns are sometimes recycled back into the fund

• The real outcomes (the ones that matter) take much longer to show up

This is also why Venture behaves the way it does:

• Returns follow a power law → a few winners drive the entire fund

• Secondary markets are booming ($60B+) to provide early liquidity

• Fund timelines are stretching → many now take 15–20 years to fully play out

So Venture isn’t a “wait 3–5 years and cash out” game. It’s a long-duration bet where time is part of the strategy, not a constraint.

TODAY’S JOB OPPORTUNITIES

💼 Venture Capital & Startup Jobs

Most aspiring VCs struggle with interviews, unclear expectations, no structured prep, and generic advice that doesn’t actually help. So we partnered with a leading investor group to build an all-in-one VC Interview Preparation Guide that gives you real clarity and frameworks. (Access Here)

• Head of Investor Community - Inovexus | Remote - Apply Here

• Communications & Content Manager - b2ventures | Germany - Apply Here

• Senior Investment Analyst - Earth Foundry | USA - Apply Here

• Senior Scouting Manager - Antler | Australia - Apply Here

• Associate - Lightbank | USA - Apply Here

• Associate - Climate Venture Fund | USA - Apply Here

• Ventures Analyst/Associate - Plug and Play Tech Centre | USA - Apply Here

• Research Innovation Program Director - Generator | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Program Manager - a16z | USA - Apply Here

• Venture Fellow - age1 | USA - Apply Here

• Venture Capital Associate - Sky9 Capital | USA - Apply Here

• Investment Summer Associate - AI Tooling - M13 | USA - Apply Here

• Event Marketing Manager - AI Fund | USA - Apply Here

PARTNERSHIP WITH US

🤝 Get your brand in front of 116,000+ audience.

Our newsletter is read by 116,000+ of tech professionals, founders, investors (VCs / Angel Investors) and managers around the world. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

👋 Hey, Sahil here - Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

• How to do user research without users: the synthetic user guide.

• The unit economics Excel sheet template every founder should use.

• Is freemium right for your SaaS product? Ask these 3 questions.

• Why do VCs care so much about who’s leading your round?

FROM OUR PARTNER - FRONTIER BIO

🧬 The end of animal testing? - Investors are backing this biotech building human tissues for testing and organ repair

Frontier Bio is developing engineered human tissues designed to replace unreliable animal testing and eventually enable lab-grown organs for transplant.

Today, ~90% of drugs that pass animal testing fail in human trials, while more than 100,000 patients in the U.S. are waiting for organ transplants. Frontier Bio is building human tissue models that help companies test therapies more accurately and advance regenerative medicine.

Why investors are paying attention:

• $5.5M+ in cumulative sales

• Partnered with Mayo Clinic, Stanford, and Intuitive Surgical

• Scientific backing from Harvard geneticist Dr. George Church

• Targeting markets including $2.1B tissue models and $15B vascular grafts

The company is already generating revenue from tissue models used in research while advancing toward implantable vascular grafts and future organ engineering.

As demand grows for more accurate therapeutics testing and new transplant solutions, Frontier Bio is positioning itself at the intersection of biotech, regenerative medicine, and drug discovery.

Invest in Frontier Bio →

VENTURE CURATORS’ FINDING

📬 My Favourite Finds

Media Posts:

• Every Startup accelerator you can apply to right now. (2026) (Link)

• Startup legal document pack – essential legal Docs for founders. (Link)

• I’ve spent over $1M on tech launches in the past year. Here’s everything I know. (Link)

• Where a former OpenAI employee is working. (Link)

• Developer productivity has surged over the past year, according to the FT. If your competitors aren’t shipping with Claude Code yet, they will be by Q2. (Link)

• Great essay on the importance of being original in business. (Link)

Reports/Articles:

• The end of fragmentation. (Link)

• What do early-stage VCs look for? (Link)

• Can you describe—exactly—the problem you’re solving? (Link)

• Column: Jensen Huang doesn’t need a new chip. He needs a new moat. (Link)

• Check out these 50 Claude Code tips and best practices for daily use. (Link)

🤝 PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

How to do user research without users: the synthetic user guide.

When you’re building an MVP or raising your first round, you usually don’t have the luxury of long research cycles or access to dozens of users. But you still need clarity: Who exactly are you building for? What do they care about? How do they behave?

This is where AI-generated personas become incredibly useful.

Instead of spending weeks interviewing users, tools like ChatGPT can create realistic, behaviour-driven personas based on your ideal customer profile. These aren’t just demographic sketches; they simulate a user’s motivations, fears, habits, workflows, and buying triggers.

Researchers even point out that generative AI can speed up persona creation without sacrificing user-centred thinking, helping teams align faster and make sharper product decisions early on.

Of course, AI personas don’t replace real users. But for founders moving fast, they’re a powerful way to:

• pressure-test product ideas

• refine messaging

• uncover blind spots

• and build team alignment around one clear customer archetype.

Crafting personas with ChatGPT (Prompt Templates)

ChatGPT can generate rich, realistic personas from just a few inputs, but the quality depends on what you ask for. The goal is not to create a demographic sketch; it’s to capture the user’s goals, motivations, behaviours, frustrations, and decision triggers.

That’s the information that actually guides product and UX decisions.

A simple rule: Demographics tell you who they are. Motivations tell you what they’ll do.

Core Prompt Template (Generate Persona)

You can start with a structure like this:

Create a detailed user persona for [ROLE or user segment] of our
[PRODUCT/SERVICE]. Include their background, goals, motivations, and main pain points or frustrations related to [problem domain]. 

Focus on how and why they would use the product. 

This forces the AI to go beyond surface-level traits and gives you a persona you can actually design for.

For example, if you ask for a “Marketing Manager who struggles with data privacy in finance apps,” ChatGPT will produce a persona with:

• job context

• daily tasks

• goals

• fears and blockers

• Why this problem matters to them

• How do they evaluate tools or switch products

These are the elements that matter, not their age or whether they drink cold brew.

Add more detail when needed

You can refine the persona by adding parameters like:

• industry

• team size

• technical comfort

• typical workflow

• Tools they already use

Or by asking follow-ups like:

• “What daily frustrations does this user face?”

• “Describe their typical workflow from morning to evening.”

• “What alternatives are they currently using and why?”

This iteration turns a basic persona into an actionable one.

Another example prompt

Create a persona for a Sales Business Development Representative using a CRM app. Frame the persona around their goals, motivations, and behaviors. Include their main pain points and how they currently use similar tools. 

Do not list demographic information unless it directly shapes their decisions.

This approach mirrors how modern UX teams work: personas built around roles, tasks, context, and decision drivers, not superficial details.

The goal: an actionable persona

A good persona should help you answer:

• What are they trying to get done?

• What stops them from doing it?

• What do they value in tools like yours?

• When and how will they use your product?

If your persona clarifies these four things, you’ve got something you can design, build, and pitch around.

Simulate a user interview

Once you’ve built a persona, the next step is to talk to them or at least simulate the conversation. ChatGPT can role-play as your target user and answer questions the way a real person in that role might. This is one of the fastest ways to stress-test assumptions before you speak to actual users.

Set the stage with a role-play prompt

Start by telling ChatGPT exactly who it should become. Give it the persona’s name, context, and behaviours, then frame the interview scenario.

Example setup:

You are [NAME], a [brief persona description] who regularly uses [PRODUCT/CATEGORY]. I am a product manager interviewing you to understand your needs, experiences, and frustrations. Stay fully in character and respond as [NAME] would.

Adding a couple of guidelines helps ChatGPT respond more realistically:

• “You should role-play as a real user with clear goals, preferences, and frustrations.”

• “You have experience with [competitor/product type] and will give honest feedback.”

This primes the model to think and speak like a real user rather than a generic chatbot.

Ask open-ended, conversational questions

From here, treat it like a real interview. Ask questions that encourage depth, not yes/no answers. For example:

• “Can you walk me through how you currently handle [task]?”

• “What’s the most frustrating part of that process?”

• “What tools have you tried before, and why did you switch?”

• “What would an ideal solution look like to you?”

Because ChatGPT stays in character, its responses feel like genuine user feedback, complete with emotions, reasoning, and tradeoffs.

Use ChatGPT to analyse the conversation

After the role-play, you can shift modes and ask:

• “Summarise the key pain points from this interview.”

• “List the top jobs-to-be-done for this persona.”

• “Highlight the strongest buying triggers or objections.”

This gives you a quick synthesis that mirrors what you would produce after a real user interview.

Why this works

Simulated interviews won’t replace real conversations, but they help founders:

• Catch flawed assumptions early

• discover angles they hadn’t considered

• refine messaging and feature priorities

• prepare better questions for real users

In a fast MVP cycle, these simulations act like a rehearsal: low cost, fast feedback, and surprisingly insightful.

Get feedback on ideas & messaging

Once your persona is defined, you can use it as a filter to evaluate your product ideas, landing page copy, feature descriptions, or even early UX flows. This gives you fast, persona-aligned feedback before you test with real users.

Ask the persona to react to your concept

You can prompt ChatGPT to “think” like the persona and tell you how they would respond to your idea or messaging.

Example prompt:

Imagine you are [Persona Name]. Here is our product concept/messaging: [insert idea, copy, or landing page]. How would you react to this? What would you like, dislike, or find confusing? Stay in character and respond as [Persona Name] would.

This usually surfaces:

• unclear value props

• jargon that doesn’t fit the persona

• missing benefits

• objections or concerns

It’s a quick way to see how your message lands with the user you’re designing for.

Ask for targeted improvements

You can also ask for focused, persona-driven suggestions:

• “As a [Persona], what would you improve about this feature description?”

• “What would make this onboarding flow easier for you?”

• “What benefits matter most to you here?”

ChatGPT will respond from within the persona’s mindset, giving you feedback grounded in their motivations, frustrations, and decision-making style.

If something stands out, go deeper:

• “Why would that bother you?”

• “Can you explain what feels unclear?”

• “What would you expect instead?”

This turns a basic reaction into rich insight, like a real user interview.

Role-play design or flow evaluations

You can even test specific moments in the experience:

• “Describe how you would feel going through this signup process on your phone.”

• “As this persona, what questions would you ask after hearing this feature pitch?”

• “What part of this dashboard would be most confusing to you and why?”

These role-plays often reveal usability issues, emotional reactions, and missing clarity that founders typically catch only after shipping.

Your only rule: keep ChatGPT in character

Always remind the model:

“Please answer as [Persona Name] would.”

This keeps the feedback consistent, grounded, and aligned with the persona’s worldview.

Stress-test features or pricing

Personas aren’t just for interviews; you can use them to pressure-test your roadmap and pricing decisions. By putting the persona in buying mode, you can quickly see how they might evaluate tradeoffs, question pricing, or prioritise features.

Test reactions to a specific feature or price

Use a simple role-play setup to understand perceived value:

You are [Persona] (a [brief description]). We’re considering releasing Feature X at price $Y. What questions or concerns would you have? Would this feel worth the cost? Please answer as [Persona] would.

This helps surface things like:

• expectations around value

• price sensitivity

• missing benefits

• objections before purchase

• reasons they might hesitate or churn

It’s not about predicting real budgets, it’s about understanding how your ideal user thinks about value.

Compare priorities between features

You can also push the persona to evaluate tradeoffs:

Act as [Persona]. Which of these two feature sets matters more to you, and why: [Feature A] vs. [Feature B]?

This forces ChatGPT to reason like the persona, revealing:

• what they care about most

• What feels “nice-to-have” vs. “must-have”

• outcomes they prioritise

• Features they would drop if pricing increases

What you learn

Since the model stays in character, you get insight into how this type of user might:

• justify paying more

• Reject pricing that feels misaligned

• Request additional value

• push back on complexity

• choose between competing features

For example, the persona might say things like:

• “I’d expect more automation before paying extra.”

• “Feature Y feels unnecessary — I’d remove it if it increases the price.”

Use these reactions to validate or challenge your assumptions before locking in scope or pricing.

When to use synthetic personas vs. real interviews

AI personas are most helpful in the early stages, when you’re ideating, aligning the team, or quickly pressure-testing assumptions. They’re great for brainstorming (“How would our target user react to this?”), and they help teams rally around a shared picture of the user. They can also sharpen your real research: a synthetic interview often reveals what questions you should ask human users next.

But they’re not a replacement for real conversations. AI can summarise motivations, goals, and frustrations, but it often misses nuance and emotional depth. ChatGPT can predict reactions, it can’t replicate lived experience. Think of its output as hypotheses, not conclusions.

Put simply: Use AI personas as a practice run. Use real users to make real decisions.

As soon as you have a product concept or MVP, switch to actual interviews to validate what matters. The strongest approach is a blend: use ChatGPT to draft personas, explore ideas, rehearse interviews, then go out and talk to users and compare the insights.

Blending AI insights with real feedback

The strongest approach is a mix of AI-generated insights and real user data. For example, you can take interview transcripts or survey quotes and feed them into ChatGPT for fast synthesis.

Builders like Shavin from Buildspace have shown that giving ChatGPT raw customer quotes can instantly generate themes, job stories, and problem statements that normally take hours.

It’s a great way to accelerate analysis, as long as you double-check everything.

ChatGPT sometimes makes confident but incorrect conclusions, so always validate AI output against the source data. If the model suggests a new pain point, check whether it truly appeared in interviews.

A simple loop works well:

1. Draft a persona or interview script in ChatGPT.

2. Talk to a few real users and see what aligns or diverges.

3. Feed the real answers back into ChatGPT to extract themes or refine personas.

4. Update your product or messaging using both sources.

This lets you move fast while staying grounded. AI helps you explore and structure ideas; real users ensure accuracy and nuance.

Red flags: bias, hallucinations, overconfidence

Be cautious about where AI can go wrong. ChatGPT can hallucinate, inventing details, competitors, or statistics that sound real but aren’t. Research has documented this pattern, and it’s one of the top concerns with AI personas. It can also amplify bias, creating personas that feel overly polished or missing uncomfortable truths.

Avoid confirmation bias by using neutral prompts.

Don’t ask “Does this idea make sense?” Ask “What might be confusing or risky about this idea?” When accuracy matters, keep the temperature low to reduce randomness.

Most importantly, never act on AI-generated insights without evidence. If ChatGPT says, “Users would love Feature X,” ask: Do I have any proof? Cross-check with mentors, data, or real interviews. AI isn’t thinking, it’s predicting patterns, so you must stay critical.

Case examples from founders and teams

Many teams use AI personas to speed up early product work:

Fast persona creation. At Traders Eco, a PM used ChatGPT to generate detailed personas from a simple target audience description, helping her team quickly align on “who our user is.”

Rapid mockups and idea testing. Hootsuite’s GM, Partho Ghosh, shared that his team uses ChatGPT plus image tools to prototype landing pages and concepts quickly, allowing them to explore ideas without engineering time.

Accelerated feedback analysis. Canny’s co-founder Sarah uses ChatGPT to scan support conversations, surface feature requests, and group similar user messages — saving hours.

Another PM built a pipeline to cluster raw reviews and then used GPT to summarise each cluster’s pain points.

In all cases, founders emphasise the same point: AI made them faster, not smarter than real users. They still validate everything with actual conversations before committing to decisions.

So - AI personas and simulated interviews are powerful for early-stage founders: they help you ideate faster, stress-test ideas, and align your team before talking to real users. With clear prompts and careful iteration, ChatGPT becomes a valuable brainstorming partner, not a replacement for user research.

Use it to explore possibilities, reveal blind spots, and draft hypotheses. Then go validate those hypotheses with real conversations. That combination is where AI actually shines: giving you speed without losing the human insight your product depends on.

📃 QUICK DIVES

The unit economics Excel sheet template every founder should use.

Most founders talk about growth. Few can clearly show how each new customer makes or loses them money. That’s why running a unit economics model early matters; it tells you if your business will compound or collapse.

Here’s how to use the sheet I’ve shared:

1. Map your revenues

• Enter your average revenue per unit (subscription, fee, or transaction).

• Decide if it’s recurring (monthly/annual) or one-time.

• Pro tip: if you don’t know exact numbers yet, start with estimates and refine over time.

2. Add cost of sales (COGS)

• Enter costs as a % of revenue (hosting, delivery, support).

• The sheet calculates your gross margin per unit automatically.

3. Plug in churn + growth assumptions

• Set billing cycle (monthly = 1, annual = 12).

• Add churn % (customers who drop each cycle).

• Add expected annual margin growth (e.g., 5%).

• The model uses these to estimate the average customer lifetime.

4. Add acquisition + retention costs

• Enter your CAC (cost to acquire one customer).

• Add optional retention/expansion costs if relevant.

5. Review key outputs

• LTV (Lifetime Value): how much a customer is worth over their lifecycle.

• LTV/CAC ratio: >3 is usually healthy.

• CAC payback period: months it takes to earn back the acquisition spend.

• Cumulative cash flows: show when you turn profitable per customer.

6. Stress-test scenarios

• Increase churn by 30%.

• Drop your AOV (average order value) by 20%.

• Raise CAC by 50%.
  See how fast LTV/CAC breaks. This is where most founders get surprised.

Why this matters

• Investors use this as a quick sanity check.

• It forces you to confront pricing, churn, and acquisition head-on.

• A business with shaky unit economics will break no matter how good the growth story sounds.

Download the sheet, plug in your numbers, and run 3 scenarios: best case, expected case, worst case. You’ll instantly see whether your idea scales or needs fixing.

Is freemium right for your SaaS product? Ask these 3 questions.

Deciding whether to offer a freemium model is a common dilemma for founders. Many try out freemium strategies, but not all SaaS companies can replicate the success of Dropbox or Typeform.

Done wrong, freemium can end up cannibalising your paid user base while also draining your company’s precious engineering and customer support resources.

So how can you determine if it’s the right move for your company?

The most reliable way to find out is through A/B testing. However, getting solid results can take a long time, especially if you’re looking at the impact on virality and your viral cycle is six months or longer.

If you can’t wait that long or aren’t set up for a full A/B test, consider these “Three Factors for Freemium Strategy”:

1. Does your paid plan have a gross margin of 80–90%?
   If you have a lower gross margin — for example, because your product is not fully self-service, requires extensive customer support or is extremely costly in terms of tech infrastructure — freemium will probably not work for you.

2. Does your free plan attract the right audience?
   If your free users are too different from your paying users, your free-to-paying conversion will be low, and you’ll risk developing your product for the wrong audience.

3. Is your product inherently viral?
   If your answer is no, that doesn’t make it a complete no-go, but it does mean that it’s much less likely that freemium is right for you.

In the end, freemium only makes sense if a certain percentage of your free users do one of three things:

• Eventually, convert to paid,

• Refer paying customers, or

• Provide the kind of valuable feedback that will improve your product.

A freemium product that fails to achieve any of these effects will merely saddle you with extra costs and distract you from servicing your most important users.

Also, check out this interesting article on why companies fail with freemium. I highly recommend it, definitely worth a read.

Why do VCs care so much about who’s leading your round?

This is one of the most annoying questions founders hear on a pitch: “Who else is investing?”

It often feels like code for “I’ll decide after someone smarter goes first.” But the data suggests it’s not just lazy signalling, it’s structural.

This insight comes from Peter Walker (Head of Insights at Carta), based on Carta data covering 13,000+ Seed and Series A rounds (2019–2025).

Here’s what’s actually happening beneath the surface.

Over the last few years, lead investors have been taking a bigger share of rounds, while the number of funds willing to lead has gone down.

In many recent Seed and Series A rounds, the lead is now taking 60–75%+ of the entire round, compared to ~50–55% a few years ago. At the same time, more funds are participating just not leading.

That changes incentives.

For a non-lead investor writing a smaller check, the identity of the lead suddenly matters a lot more. They’re implicitly underwriting the lead’s conviction, judgment, and future behaviour.

A few practical realities behind that question:

• If one fund owns most of the round, they effectively control the outcome

• Their ability (or willingness) to lead the next round becomes critical

• If they don’t follow on, it sends a strong negative signal to future investors

• Their domain expertise (or lack of it) affects how helpful they’ll be post-check

From the founder side, this explains why strong leads still unlock momentum, even in markets where VCs claim to be “independent thinkers.” The first check is still celebrated, but the right first check matters more than ever.

The real takeaway for founders isn’t “optimise for name-brand VCs.” It’s this:

• Be deliberate about who you let lead

• Understand how much of the round they’re taking ownership shapes power

• Anticipate follow-on questions and signaling effects early

• Realize that today’s market structurally favors fewer, more dominant leads

So yes, some investors ask this question for the wrong reasons. But in a world where leads increasingly own the round, “who’s leading?” is no longer a soft question it’s a risk question.

That’s the shift founders need to internalise.

TODAY’S JOB OPPORTUNITIES

💼 Venture Capital & Startup Jobs

Most aspiring VCs struggle with interviews, unclear expectations, no structured prep, and generic advice that doesn’t actually help. So we partnered with a leading investor group to build an all-in-one VC Interview Preparation Guide that gives you real clarity and frameworks. (Access Here)

• Partner 20, Applied ML - a16z | USA - Apply Here

• Junior Operations & Community Manager - CV VC - Apply Here

• Associate - Novo Venture | Singapore - Apply Here

• Senior Scouting Manager - Antler | Australia - Apply Here

• Head of Investor Community - Inovexus | Remote - Apply Here

• Associate - Lightbank | USA - Apply Here

• Ventures Analyst/Associate - Plug and Play Tech Center | USA - Apply Here

• Research Innovation Program Director - Generator | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Program Manager - a16z | USA - Apply Here

• Venture Fellow - age1 | USA - Apply Here

• Venture Capital Associate - Sky9 Capital | USA - Apply Here

• Investment Summer Associate - AI Tooling - M13 | USA - Apply Here

• Event Marketing Manager - AI Fund | USA - Apply Here

🤝 Partnership With Us

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

👋 Hey, Sahil here — Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

Big idea + report of the week :

• Where did all the LPs go, and why is fundraising suddenly so hard for new VCs?

• Why unicorns are staying private for much longer.

Frameworks & insightful posts :

• What people really want from AI? - Anthropic Survey.

• This is what raising $1.5M really looked like: 250 meetings, 171 rejections in 98 days.

• How to find a product idea that people actually pay for?

FROM OUR PARTNER - FRAGMENT MEDIA

📰 Investors are backing a media network reaching 80M+ monthly readers

Fragment Media is building a portfolio of premium publications designed to be funded primarily by readers. Their model flips the traditional ad-driven newsroom by focusing on paid subscriptions, high-trust journalism, and aligned brand sponsorships.

Why investors are paying attention:

• $47M cumulative revenue over the last 4 years across Fragment-managed publications

• 80M+ monthly reach across publications

• 1M+ free subscribers, 20K+ paying members

• Inc 5000 Fastest-Growing Media Company (2022 & 2023)

• Targeting a $17B U.S. subscription news market (145M adults willing to pay)

• Leadership from Gawker, VICE, Vox, The Atlantic, Hearst

While most digital media depend on volatile programmatic ads, Fragment is shifting toward recurring reader revenue.

With media M&A deals reaching hundreds of millions and subscription platforms trading at strong revenue multiples, investors see a chance to own equity in the next generation of reader-owned media.

Invest in Fragment Media →

🤝 PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

START WITH

🧠 Big idea + report of the week

Where did all the LPs go, and why is fundraising suddenly so hard for new VCs?

For a long time, the venture narrative was simple: more funds were getting created, and more LPs were entering the asset class.

That balance has quietly broken.

New data shared by Carta’s Head of Insights, Peter Walker, shows something deeper happening beneath the surface - the number of funds is still going up, but the number of LPs backing them is going down.

And that mismatch is starting to define the entire early-stage VC landscape.

If you look at the data across fund sizes from 2021 to 2025, the pattern is consistent. Smaller funds that once had 30-40+ LPs are now raising with closer to 20-25. Even mid-sized and larger emerging funds are seeing a noticeable drop in LP participation.

• More funds are being created, but fewer LPs are writing checks.

In 2025, more emerging managers entered the market than in 2024, but the median number of LPs per fund declined across almost every fund size category. That means each fund is now competing for a smaller pool of capital.

• The same LP base is being stretched thinner.

Instead of new LPs entering the venture, existing LPs are being asked to back more funds. Naturally, this leads to smaller check sizes, slower closes, and more funds struggling to reach meaningful scale.

• Institutional LPs are consolidating, not expanding.

After the 2020-2022 boom, many institutional LPs are now doubling down on existing GP relationships, especially those with proven DPI and distributions. New or first-time managers are getting pushed out of allocation decisions.

• Liquidity is the hidden driver behind all of this.

LPs haven’t seen enough cash come back from recent vintages. When distributions slow, new commitments slow. It’s not just about conviction—it’s about available capital.

What this creates is a very different venture market than what most people are used to.

On one side, established managers with strong track records are still raising capital relatively smoothly.

On the other hand, emerging managers are facing one of the toughest fundraising environments in recent memory.

And the gap between those two groups is widening.

The interesting part is that this isn’t just about fundraising difficulty - it changes behaviour.

When LPs become scarce:

• GPs spend more time fundraising than investing

• Fund sizes get compressed or delayed

• Ownership strategies shift because capital is tighter

• New managers need stronger differentiation, not just access

It also quietly changes how venture evolves as an asset class.

Fewer new managers getting funded means less experimentation in fund strategy, geography, and thesis. Over time, that can reduce the diversity of bets being made across the ecosystem.

The takeaway here isn’t just that “fundraising is harder.”

It’s that venture is moving from an expansion phase (more LPs, more funds, more risk-taking) to a selection phase (fewer LPs, higher bar, capital concentration).

And in that environment, the question for any emerging manager becomes very simple:

Not “Can you raise a fund?”
But “Why should you exist in a market that already has too many funds and too little capital?”

Why unicorns are staying private for much longer.

A recent analysis by Stanford GSB’s Ilya Strebulaev shows a clear shift in how long unicorns remain private. The takeaway is simple but important: private capital has reduced the urgency to exit, and public markets are no longer the default endgame.

What the data shows:

• 2011 cohort: About 51% of unicorns founded in 2011 are still private. These are companies more than a decade old that have chosen to delay IPOs or acquisitions.

• 2021 cohort: Roughly 77% of unicorns founded in 2021 remain private, highlighting how slow the liquidity path has become for recent high-growth startups.

• Even older companies linger: Among unicorns founded in 2004, around 27% have still not exited nearly 20 years later.

What’s driving this trend:

• Deep private markets mean late-stage capital can replace public listings.

• Founders and investors can optimise for control, timing, and valuation rather than speed to IPO.

• Market volatility has made staying private a safer option than testing public markets early.

Why this matters:

• Liquidity timelines are stretching across every vintage, not just recent ones.

• LPs wait longer for distributions.

• IPOs are becoming a strategic choice, not a milestone.

Unicorn outcomes are no longer tied to quick exits. Private capital has reshaped the startup lifecycle, turning “staying private” into a long-term strategy rather than a temporary phase.

SOMETHING MORE

🧩 Frameworks & insightful posts

What people really want from AI? - Anthropic Survey.

Most conversations about AI get stuck in abstractions. We hear big claims about job loss, AGI, productivity, safety, and existential risk, but much less about a simpler question: what does “AI going well” actually look like for real people already using it?

Anthropic recently ran one of the most interesting studies I’ve seen on this. It invited Claude users to describe how they use AI today, what they hope it could unlock in their lives, and what worries them most. More than 80,000 people across 159 countries and 70 languages participated, which makes this less like a normal product survey and more like a global snapshot of how people are beginning to fit AI into everyday life.

What makes the findings valuable is that they move beyond generic optimism or fear. They show where AI is already delivering value, where it still feels shaky, and what kinds of products or companies are likely to matter next.

The biggest thing people want from AI is not magic. It’s a relief.

When Anthropic asked people what they most wanted from AI, the answers were surprisingly grounded. The top categories were:

That tells you something important. Most people are not asking AI to become some futuristic super-being. They want it to help them do better work, reduce mental overload, manage life logistics, learn faster, and reclaim time.

That’s a useful correction for founders. The most immediate demand is not for “AI for everything.” It’s for products that remove friction from real life.

A lot of the strongest responses had the same emotional structure underneath them:

AI helped me do what I already needed to do, but with less stress and more breathing room. Someone used it to reduce documentation burden at work. Someone used it to learn coding despite a learning disorder. Someone used it to ask questions they felt embarrassed asking other people.

So, the best AI products may not be the ones that feel the most futuristic. They may be the ones that quietly remove cognitive load.

Work is still the main wedge, but quality of life is the real promise

The largest category people mentioned was “professional excellence.” That makes sense. Work remains the easiest entry point for AI because the ROI is easier to spot. If AI saves time, improves output, or helps someone get through repetitive tasks faster, the benefit is obvious.

But what’s more interesting is what sat underneath that answer. Many people started by talking about productivity, then revealed that what they really cared about was what productivity enabled outside of work: more time with family, more emotional energy, less admin, less mental clutter.

That distinction matters.

A founder might think they’re building a productivity product, when in reality the real value proposition is:

• more time freedom

• less anxiety

• less friction in daily life

• more space for better work and better living

That is a much richer product insight than “help users move faster.”

AI is already delivering in a few very specific ways

When Anthropic asked whether AI had already taken a step toward users’ goals, 81% said yes. That’s a meaningful number. But the shape of delivery matters more than the headline.

The main buckets where people felt AI had already helped were:

This mix is fascinating because it shows that AI is not just being used as a faster search engine. It is already functioning as a kind of thought partner, teacher, translator, research assistant, and in some cases even an emotional support layer.

Some of the strongest responses came from people who felt AI gave them access to things they previously couldn’t reach. Not because the knowledge didn’t exist, but because the format was inaccessible, judgment-heavy, too expensive, or too difficult to navigate. That’s a powerful clue about where AI creates the most real value: not just speed, but access.

For builders, that opens up a big product opportunity. AI seems especially strong when it does one of three things:

• reduces intimidation

• removes judgment from learning

• translates complexity into something people can act on

That’s a better lens than simply asking whether a task can be automated.

The most common fear is not “AI takes over the world.” It’s that AI becomes unreliable at exactly the wrong moment.

Public AI debates often drift to dramatic long-term fears. But the top concern in this study was much more immediate: unreliability.

Users worry that AI will sound convincing while being subtly wrong. In some categories, that’s a mild annoyance. In others, it’s dangerous. Law, finance, healthcare, and government workers especially raised this concern because the cost of a confident error is high.

This is a critical insight for founders. Reliability is not a “nice to have” layer you add later. In many AI products, it is the product.

Other major concerns included:

What’s striking is how practical most of these fears are. People are not mainly debating whether AI is philosophically good or bad. They are asking whether it will make them less employable, less independent, less thoughtful, or too dependent on a system that is always available.

The real story is not optimism vs pessimism. It’s tension.

One of the best concepts in the report is what Anthropic calls the “light and shade” of AI.

The same capabilities people love are often the ones they fear. For example:

• AI helps people learn faster, but they worry it may weaken their own thinking.

• AI saves time, but they worry it increases expectations and speeds up the treadmill.

• AI offers emotional support, but they worry it could become a substitute for human connection.

• AI creates economic opportunity, but it also raises fear about displacement.

That is a much more honest frame than dividing people into “AI believers” and “AI sceptics.” Most users are both at once. They see upside and risk together.

That’s an important product lesson. Great AI products will not win simply by maximising usefulness. They’ll win by managing the tension around usefulness. The winners won’t just provide capability. They’ll create trust, boundaries, and clarity around how the product should fit into a person’s life.

The regional split is worth paying attention to

The report also found a noticeable pattern across geographies.

Users in lower- and middle-income countries were generally more optimistic about AI than users in wealthier regions. In many emerging markets, AI is seen less as a threat and more as a ladder, a way to start businesses, access education, or overcome infrastructure gaps.

That matters if you’re building global products.

In richer markets, AI is often framed around life management, overload reduction, and economic anxiety. In many developing regions, it is framed more around entrepreneurship, learning, and access. Same technology, very different emotional job-to-be-done.

Founders who understand that distinction will position themselves much better across geographies.

For founders -

The strongest message from this report is that people do not just want faster outputs. They want better lives.

That sounds obvious, but it is surprisingly easy to forget when building in AI. Many teams optimise around what the model can do instead of what the user is trying to become. Anthropic’s findings suggest that the most enduring AI products will be the ones that sit at the intersection of usefulness and human aspiration.

The near-term winning categories likely look less like vague general-purpose intelligence and more like products that help people:

• do meaningful work with less administrative burden

• learn without fear or embarrassment

• manage overloaded lives

• turn curiosity into action

• gain economic leverage

• access systems that previously felt closed off

At the same time, founders need to respect the downside. If your product saves time but increases dependency, gives emotional support but weakens real relationships, or accelerates output while making judgment worse, users will eventually feel that tension.

That’s why this study is so useful. It reminds us that AI is not entering a vacuum. It is entering messy human lives. And the companies that win won’t just be the ones with better models. They’ll be the ones who understand what people are actually trying to protect, improve, and reclaim.

This is what raising $1.5M really looked like: 250 meetings, 171 rejections in 98 days.

Most fundraising stories you see are clean.

• “Raised $2M in 2 weeks.”

• “Oversubscribed round.”

• “Strong investor demand.”

What you don’t see is the messy middle.

Matija Sonic shared what actually happened behind the scenes: 250+ meetings, 171 rejections, 24 ghosted, and just 17 investors closing the round over 98 days. Even after YC Demo Day generated 100+ investor leads, not a single one invested.

That’s the real starting point most founders don’t talk about.

Fundraising is not one big moment. It’s a system.

The biggest mistake early founders make is treating fundraising like a few high-stakes conversations.

In reality, it behaves much more like sales.

• 212 investors contacted

• 250+ meetings run

• constant follow-ups, tracking, refining

If you don’t treat it like a pipeline, you lose control of it. The founders who struggle the most are usually the ones approaching it casually, reacting to meetings instead of building a system around them.

Your first 50 meetings are basically practice

They walked into early meetings expecting quick closes.

Instead, most investors didn’t understand the product at all. It was technical, pre-revenue, and they had no strong network advantage.

But something important happened around meeting #50.

The pitch started improving, not because they rewrote it overnight, but because repetition forced clarity. By meeting #100, both founders knew the pitch deeply, could anticipate objections, and communicated with confidence.

That shift is underrated.

Confidence in fundraising doesn’t come from preparation alone. It comes from exposure. You earn it by sitting through uncomfortable conversations until your thinking sharpens.

The mindset shift that changes everything: chase No’s

Around 50 meetings in, rejections started piling up, and morale dropped.

Then came a simple but powerful reframe: stop chasing yeses. Start chasing no’s.

Set a target, like 100 rejections.

Why this works:

• No’s are predictable and within your control

• They remove emotional pressure from each meeting

• They give you a clear sense of progress

If you haven’t hit your rejection target, it likely means you haven’t spoken to enough investors.

This turns fundraising from an emotional rollercoaster into a process you can actually manage.

The “valley of death” is real

For nearly 2 months, they were stuck around ~$300K. No momentum. No visible progress. Others were closing rounds.

This is where most founders quit, not because of rejection, but because of silence.

What they didn’t see: their lead investor was doing deep diligence behind the scenes. Fundraising often feels dead… right before it breaks open.

Then everything compounds at once

After weeks of slow progress:

• Investors started closing faster

• urgency kicked in

• The round filled quickly

• It became oversubscribed

This is how most rounds actually work: slow → stuck → sudden acceleration

What founders should actually take from this

• Don’t try to convince non-believers - find investors who already understand your space

• Warm intros > random inbound (Demo Day interest is often a low signal)

• Treat fundraising like a pipeline, not conversations

• Track pitch quality, not just money raised

Those 250 meetings weren’t just about raising money. They made them better founders.

Because in the end, fundraising isn’t just about getting a yes. It’s about becoming someone who can consistently earn it.

How to find a product idea that people actually pay for?

Most people think building a startup starts with an idea. In reality, it starts with a mistake.

They sit down, open a blank page, and try to “come up with something.” It feels like progress, but most of the time it leads to products no one really needs. And in today’s world, where AI can build almost anything, the real bottleneck isn’t execution anymore; it’s finding something worth building.

Recently, Ronin (on X) shared a simple process that helped him secure 5 paying buyers before writing a single line of code. The interesting part isn’t the outcome, it’s how the process flips the usual way founders think.

Instead of starting with an idea, he starts with problems that already exist in the real world.

It begins with observation, not brainstorming

Good ideas rarely come from sitting and thinking hard. They come from noticing things that shouldn’t be broken but are.

If you look closely, there are signals everywhere:

• small tasks you keep doing manually

• tools that feel unnecessarily complicated

• recurring frustrations in your own workflow

• complaints you hear from people around you

Over time, this builds a list of problems grounded in reality, not imagination. That shift alone eliminates a huge percentage of bad ideas before you even start.

Then you go where demand already exists

One of the biggest misconceptions is that you need a completely original idea. In practice, most successful products are iterations on what already works.

So instead of trying to invent something new, the smarter move is to study where people are already asking for solutions.

Places like Reddit, Product Hunt, X, YouTube, and even App Store reviews are full of unfiltered demand. People openly share what they’re struggling with, what tools they use, and more importantly, what they hate about them.

The goal at this stage is not to pick one idea quickly. It’s to collect multiple ideas that already have some signal of demand, and then narrow them down thoughtfully.

Validation is where most people go wrong

This is the step almost everyone skips.

Once they feel excited about an idea, they jump straight into building. But excitement is not validation.

Before writing any code, a few simple questions can save months of wasted effort:

• Are there existing products solving this?

• Are people actually paying for them?

• What do users complain about in those products?

If competitors exist and people are paying, that’s not a negative signal, it’s one of the strongest green lights you can get. It means the market is real, and the problem is worth solving.

Not every good idea is a good business

Even if something works, it doesn’t always mean it’s worth building.

At this stage, the focus shifts from “does this exist?” to “can this grow?”

Things to pay attention to:

• Is demand increasing or stagnant?

• Can this be monetised as a subscription or only one-time?

• Is the buyer an individual or a business?

• How big can this realistically become?

Many ideas fail not because they don’t work, but because they don’t scale. A small tweak in positioning here can completely change the outcome.

Your edge comes from focus, not superiority

You don’t need to build something dramatically better than everything else. You just need a clear, focused entry point. In most cases, products win by being:

• simpler for a specific type of user

• faster at solving one use case

• cheaper for a particular segment

• or tailored to a niche that’s being ignored

Trying to build a “better version for everyone” rarely works. Building a 10× better experience for a narrow group often does.

The smartest move: test before you build

This is where everything comes together.

Before writing any code, you can test demand in surprisingly simple ways. Talk about the idea publicly, reach out to potential users, or even offer to solve the problem manually for a few people.

If people show interest, or better, are willing to pay, you’ve already crossed the hardest part of building a product.

If no one cares, it’s usually not a marketing problem. It’s a signal that the idea itself needs rethinking.

The real shift most founders miss

What’s changed over the last few years is simple but important.

Building has become easy. Validation hasn’t.

AI has removed a lot of the friction from creating products, but it hasn’t changed the core rule: people only pay for things that solve real problems.

That’s why the smartest founders today spend less time coding in the beginning and more time understanding the problem deeply. Even one or two weeks of focused research and validation can save months of building something no one wants.

In the end, the goal isn’t to build fast. It’s to build something that people are already ready to pay for.

EXPLORE MORE

💡 Fundraising tools, template and Excel sheets

Fundraising Tools

• SaaS Financial Model Template

• Early-Stage Financial Model for Fundraising (Excel)

• Early-Stage Financial Projections Template

• Investor CRM & Fundraising Tracker

• Startup Valuation Guide

• Investor Data Room Guide + Templates

• All-In-One Term Sheet Guide

Strategic Templates

• Competitor Analysis Framework (Excel)

• Cap Table Template for Founders

• The Founder’s Guide to Building in Public (With 20+ Real Examples)

Communication Tools

• Monthly Investor Update – Email Template

• Pitch Deck Storytelling Guide + Free Template

• VC Job Interview Question & Answer Guide

• What Investors Ask: A Q&A Prep Kit

• Investor Outreach Email Sequence Pack: 20+ Proven Templates Used & Approved by VCs

Legal & Operational

• Founders’ Agreement Template

• Startup Legal Document Pack – Essential Docs for Founders

NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Quantonation Ventures, a NYC and Paris, France-based venture capital firm focused on quantum and physics-based technologies, closed its second flagship fund at €220m. (Link)

• Gradient, a San Francisco, CA-based early-stage venture capital firm designed for founders in artificial intelligence, closed a new $220m flagship seed fund. (Link)

• Sands Capital, an Arlington, Va.-based investor in innovative growth businesses, closed Global Innovation Fund III at $1.1 billion. (Link)

• Audeo Ventures, a NYC, San Francisco, CA, and Dubai, UAE-based venture capital firm specialising in early-stage investments, achieved $65m for Fund II. (Link)

New Startup Deals

• xmemory, a London, UK-based developer of a memory layer for AI workflows, raised $4M in Pre-Seed funding. (Link)

• Homaio, a Paris, France-based provider of an investment platform for carbon and energy transition markets, raised €3.6M in Seed funding. (Link)

• Certiv, a Seattle, WA-based cybersecurity startup, raised $4.2M in Pre-Seed funding. (Link)

• Standard Template Labs, a NYC-based provider of an AI-first service management platform, raised $49M in Seed funding. (Link)

• Quorus, a Westport, CT-based technology-driven asset manager service company, raised $5M in Seed funding. (Link)

• Understood Care, a NYC-based provider of an AI-native patient advocacy platform, raised $5M in Seed funding. (Link)

• Great Sky, a Boulder, CO-based provider of computing hardware solutions, raised $14M in Seed funding. (Link)

TODAY’S JOB OPPORTUNITIES

💼 Venture capital & startup jobs

All-In-One VC Interview Preparation Guide: With a leading investor group, we have created an all-in-one VC interview preparation guide for aspiring VCs. Don’t miss this. (Access Here)

• Investment Scout - CVX Venture | USA - Apply Here

• Director of Finance & Operations - Quona Capital | USA - Apply Here

• Investor - Expresso NB | Thailand - Apply Here

• Associate - Novo Venture | Singapore - Apply Here

• Senior Scouting Manager - Antler | Australia - Apply Here

• Head of Investor Community - Inovexus | Remote - Apply Here

• Associate - Lightbank | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Program Manager - a16z | USA - Apply Here

• Venture Fellow - age1 | USA - Apply Here

• Venture Capital Associate - Sky9 Capital | USA - Apply Here

• Investment Summer Associate - AI Tooling - M13 | USA - Apply Here

• Event Marketing Manager - AI Fund | USA - Apply Here

PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

• How to get Investors interested months before you raise - with the exact emails to send.

• The quick test: Is your homepage written for VCs or users?

• How to study a new market and build expertise - tactics from a Twitter PM turned healthcare founder.

• How investors value pre-revenue startups with an Excel sheet.

FROM OUR PARTNER - SEMRUSH

📈 See what drives your competitors’ growth — before they do

Every winning strategy starts with the right data. With Semrush’s Traffic & Market Toolkit, you can uncover exactly where your competitors’ traffic comes from — search, social, or even AI referrals.

Track their top pages, monitor emerging trends, and find the untapped channels fueling their growth. Whether you’re a marketer, agency, or enterprise, you’ll get daily and weekly insights to outpace rivals and lead your market with confidence. Trusted by 10M+ marketers worldwide.

Try the Traffic & Market Toolkit free →

VENTURE CURATORS’ FINDING

📬 My Favourite Finds

Media Posts:

• The real 7-moat you need for your company. (Link)

• Startup legal document pack – essential legal Docs for founders. (Link)

• Agent harness is the real product. (Link)

• How to win in an era where the tech is cheap, and distribution is everything. (Link)

• Alex Bouaziz (Deel founder) just shared his principles for work. This guy knows his stuff. (Link)

• Great essay on the importance of being original in business. (Link)

Reports/Articles:

• How a16z founder uses an AI tool for GTM. (Link)

• SaaS is dead. (Link)

• Freebies to LPs are hitting fund managers in the pocket. (Link)

• A framework for your first SaaS sales comp plan. (Link)

• Perplexity prompt guide to get more work done. (Link)

• How to keep winning. (Link)

🤝 PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

How to get investors interested months before you raise (with exact emails to send).

Imagine you plan to raise a funding round in six months. The rookie move is to wait until then to approach investors. The savvy move is to start now – building relationships and keeping potential investors in the loop well before you ask for a check.

But how? It’s not just about showing up at events or spraying cold emails. There’s a much smarter, proven approach.

It’s called the Pre-Raise Update Strategy.

The Pre-Raise Update Strategy is simple: send regular, concise updates about your startup’s progress to potential investors in the months leading up to a fundraise.

Instead of only contacting investors when you’re raising (or worse, when you’re low on cash), you court them in advance by sharing your journey over time.

Why bother? Because investors often “invest in lines, not dots”, – they want to see a startup’s progress as a trajectory rather than a one-time snapshot.

By receiving periodic updates (“the line”), an investor can build conviction that you execute well and hit milestones, making them far more likely to say yes when you finally pitch.

In fact, startups that keep investors updated regularly are 3× more likely to raise follow-on funding - According to NFX survey

Consistent updates build familiarity and trust: you stay on an investor’s radar (in a good way) and demonstrate momentum and discipline. Remember, investors fund momentum, not just potential. If you show steady progress and the rare discipline of actually sending updates, you’ve immediately set yourself apart (the majority of founders promise updates and never deliver, so those who do are in the top 5% for investor communication

Courting investors early with updates de-risks your raise.

They’re not scrambling to understand your business under pressure; they’ve watched it unfold. Or as one seasoned investor described backing a founder he followed for two years: “I wasn’t taking a risk – I was placing a bet I’d watched them earn over time.”

So, how to start and send the updates?

1. Build a focused target list

Don’t just create a giant spreadsheet of every VC email you can find. A shotgun approach wastes everyone’s time. Instead, curate a list of investors who are actually a fit:

• Look at who has backed startups similar to yours, especially at your stage (pre-seed, seed, etc.). Tools like Crunchbase, venture databases, and newsletters are useful here.

• Focus on alignment: sector, stage, check size, track record. If you’re a fintech seed-stage startup, a biotech Series B fund is irrelevant — cut them out.

• Check activity: Is the fund actually writing checks? If they haven’t invested in 18+ months, they may be dormant.

• The goal is not “the biggest list.” It’s a qualified list — the people most likely to say yes.

2. Segment and qualify your leads

Once you have your list, break it down into useful tiers:

• Tier 1 (“reach” investors): The dream firms or angels you’d love to bring on board, usually well-known names in your sector.

• Tier 2 (“priority” investors): Still good fits, maybe smaller checks or slightly less perfect alignment, but worth engaging.

Also, segment by connection type: who can you reach via a warm intro versus who will be cold outreach? Warm leads always go first.

Pro tip: Use your network to expand the list. Ask advisors, mentors, or founder friends to suggest relevant VCs.

“I know a healthtech founder did this and landed 50 warm introductions in a single weekend.”

And treat this like sales. Use a lightweight “investor CRM” — even a spreadsheet — to track:

• Who’s in your funnel

• Where the relationship stands

• Notes on conversations

That way, no warm lead slips through the cracks.

3. Find the right people at each firm

Don’t just write down firm names; identify individuals. At the seed stage, associates or junior VCs are often the best entry points.

• Associates are literally incentivised to scout companies early and build relationships. They may not sign the check, but they can champion you internally to partners.

• Don’t overlook angels and micro-VCs. At pre-seed and seed, a syndicate of well-connected angels can be just as valuable as a big-name fund.

• Segment your list further by type: angels, seed funds, strategic investors, corporates, and tailor your approach to each.

By the end of this research and filtering, you might have, say, 50–100 solid prospects who could invest in your round. Now it’s time to warm them up – long before you officially pitch.

Warm-up campaign: reach out and get on their radar

Once you’ve built your target list, the next step is simple: start the conversation.

The goal here isn’t to ask for money. It’s to get on their radar and earn permission to keep them updated.

Start with warm intros

Warm introductions beat cold emails every time. Investors get buried in inbound, but when a trusted contact forwards your note with “Hey, you should meet this founder,” they’ll pay attention.

• Warm referrals typically see a 20–30% response rate, compared to <5% for pure cold outreach.

• Look for mutual connections through fellow founders (especially those in a VC’s portfolio), accelerator peers, or advisors.

• Tools like LinkedIn are useful for spotting 2nd-degree connections. When asking for an intro, make it easy by writing a short forwardable blurb that your contact can simply copy-paste.

Don’t fear cold outreach

That said, cold emails aren’t worthless — especially with smaller funds, solo angels, or “super-connectors” in your industry. Many investors do respond if you keep it short, relevant, and personal.

The key is not to open with “we’re raising.” Instead, try a softer approach: ask for permission to send updates as you build. It’s a low-commitment question that’s easy to say yes to, and it starts the relationship without pressure.

One SVB advisor calls this the “permission email.” In just a few lines, you:

• Introduce yourself and your company

• Show why you might be relevant to their thesis

• Ask if you can include them in your progress updates

Most investors will say yes, especially if you’ve done the homework to ensure they’re a good fit.

Example – The Investor Update Invite Email: Below is a template you can adapt when reaching out to a VC (perhaps an associate) you haven’t met yet.

The goal is to introduce yourself, highlight why they might be interested, and get their OK to send updates:

Subject: [Your Company Name] – Updates for [Investor Name/Fund]

Hi [Investor Name],

I’m [Your Name], the founder of [Company], a [brief one-liner describing your startup]. I’m reaching out with a quick ask: we send a short monthly update on our progress and milestones, and I’d love to include you (BCC’d) on that email.

We haven’t officially kicked off fundraising yet, but based on [why they’re a fit – e.g. your focus on healthtech], I thought our journey might be relevant to you. In the past few months we’ve [mention one recent achievement, e.g. launched our beta with 100 users], and over the next quarter we’re aiming to [upcoming goal, e.g. expand to 3 pilot customers].

If you’d prefer not to receive these updates, no worries at all – just let me know. Otherwise, I’ll be excited to keep you in the loop. Thank you!

Best,
[Your Name]
[Your Title], [Company]
[LinkedIn or Website URL]

A few important notes about this approach: keep it short and factual. You’re not attaching a deck, you’re not asking for a meeting. You’re simply asking, “Can I send you updates?” That’s an easy yes for most investors; it costs them nothing until they decide they’re interested.

You’re also dropping a subtle signal that fundraising is on the horizon (“haven’t kicked off fundraising yet”), so they understand the context. Mentioning a recent win plus a near-term goal gives them just enough to see momentum without overselling. And when you start sending updates, always BCC your list, don’t put investor emails on display, and keep the note feeling like a direct message from you.

By doing this, you accomplish two things:

1. You get on an investor’s radar in a positive, non-intrusive way.

2. You set yourself up to send consistent, more substantive updates over time.

Most founders are surprised at how high the hit rate is. Early-stage VCs and angels almost always reply with a quick “Sure, go ahead.” Why? Because it’s literally their job to track emerging companies. Or as one Techstars mentor likes to remind founders: “Build your network before you need it.” That’s exactly what you’re doing here.

Once someone agrees, they’ve moved from a cold lead to a warm one. Now the job shifts: send updates that are worth reading and make them want to keep following along.

Crafting your pre-raise investor updates email

So you’ve got a list of investors who’ve agreed to receive updates. Now comes the big question: what do you actually send them?

The guiding principles are simple: keep it concise, keep it high-signal, and keep it consistent. You’re not writing a press release or a novel. This is a short, scannable email that lets investors see your momentum and your thinking — in under two minutes.

Frequency

Monthly is the sweet spot.

• Frequent enough to show momentum, not so frequent that it’s annoying.

• If your business moves slowly (deep R&D, hardware, etc.), bi-monthly or quarterly is fine. But don’t stretch beyond that.

• The real key is consistency. If you commit, stick to it. Sporadic updates erode trust, while regular updates build it.

Think of it like brushing your teeth: set a recurring reminder and get it done on schedule.

Length and format

Keep it short, simple, and skimmable. A few short paragraphs or bullet points.

• Use headers like Highlights, Challenges, and Asks.

• Bullet lists > big blocks of text.

• Investors don’t want a board report; they want a quick sense of how things are moving.

Pro tip: Some founders even mirror the structure of post-raise investor updates, just leaving out sensitive details.

What to include

Most strong pre-raise updates have some version of these sections:

• Quick overview A one-liner upfront: “Strong month: grew 20% and launched [X].” Think of it as your subject line in sentence form. Investors skim — don’t bury the lede.

• Key metrics Share 1–3 metrics that matter: revenue, GMV, users, engagement, churn, burn/runway. Add the change vs. last month/quarter to show momentum. Example:

  • MRR $15K (+10% MoM)

  • Users: 8,000 → 9,500

• Highlights/wins: A few bullet points of recent wins:

  • +$50K in new bookings from 3 pilots

  • Hired a senior engineer from Spotify

  • App featured in Product Hunt’s newsletter

• Challenges/lows Be real. Acknowledge what’s hard — churn up, launch delayed, hiring bottleneck. This builds credibility and often invites help.

• Next steps/priorities: What’s coming next? Keep it short, e.g.:

  • Launch beta with Partner X

  • Hire 2 sales reps

  • Target $20K MRR by July

• Ask them how they can help. Be specific. Examples:

  • Referrals for frontend engineers

  • Intros to HR leaders for beta testing

  • Advice on B2B SaaS pricing

  Specific asks turn passive readers into active allies.

• Closing note: End with a quick thanks and sign-off. Something simple like:
  “Thanks for following along — appreciate the support. Until next time, [Your Name].”

Subject lines & delivery

• Stick to a consistent format: [Company] Update – [Month Year]. Easy to search later.

• Optional: add a teaser (“March Update – 3× Revenue Growth”) if you want to drive opens.

• Always BCC your list, don’t expose emails, and keep the tone personal.

Many top founders swear by this formula of concise, regular updates. It keeps your company at the top of investors’ minds and shows that you operate methodically.

As one VC survey found, the majority of great founders communicate at least monthly. And if you’re wondering whether investors actually read these emails – they do. In fact, some investors will look forward to your updates if you consistently share interesting insights and progress; they become emotionally invested in your journey. You’re effectively turning prospective investors into an audience rooting for you.

Subject: [ABCD] – September 2025 Update

Hi everyone,

It’s been a busy month at ABCD! Our focus this past month was on scaling our new mobile app and boy, did things move.

Key Results: We grew to 15,000 users (25% MoM increase) and landed 2 pilot customers for our B2B platform. Revenue hit $8,200 MRR (+18% MoM).

Highlights:

• Product: Launched the Android version of our app, which now accounts for 30% of new signups.

• Sales: Signed a 6-month pilot with MegaCorp Inc. (our biggest client to date!).

• Team: Brought on a UX designer (ex-Google) to polish our onboarding flow.

Challenges:

• App churn ticked up slightly this month – we’re analyzing user feedback to address retention.

• Hiring for Head of Sales is moving slower than expected; looking for more candidate leads.

• Our AWS costs spiked unexpectedly (perhaps a good problem – usage is growing fast!).

Next Up: In October, our goal is to launch the Team collaboration feature (waitlisted by 500 users) and reduce churn back below 5%. We’re also gearing up for our first industry conference demo in late October.

How You Can Help: 1) We’re hiring a Head of Sales – please send any great SaaS sales leader referrals our way. 2) Seeking intros at enterprise HR departments; we want 2 more pilot customers by year-end in that space. If you know any HR execs open to innovative solutions, we’d love an intro. 3) Any recommendations for optimizing AWS costs at our stage? Tips welcome!

Thank you for reading and for all the support. Several of you responded to last month’s update with helpful advice – hugely appreciated! As always, feel free to reach out any time.

Until next time,
[Your Name]
CEO, AcmeCorp
[LinkedIn] | [Email] | [Company Website]

The example structure above is just a guide. Your updates can be shorter or vary in format — the important thing is to include the core ingredients: a friendly hello, quick recap of focus and wins, a couple of key metrics, highlights, challenges, what’s next, and clear asks.

Keep the tone transparent and grounded, not hypey. If something’s off-track, say so and show how you’re fixing it. Investors value honesty more than spin. And remember: some of your audience isn’t yet an investor, so don’t overshare sensitive details like exact cash in bank — top-line numbers and qualitative progress are enough.

There’s also a hidden benefit for you: writing these updates forces reflection. You celebrate wins, confront problems, and sharpen your story month by month. Many founders later realise their updates became the backbone of their pitch deck — proof they said they’d do X, then did it, then built momentum toward Y and Z.

In short, updates aren’t just about keeping investors warm. They make you a sharper, more disciplined founder, too.

From updates to fundraising: smoothly transitioning to “ask mode”

If you’ve been sending pre-raise updates for a few months, the next step is inevitable: raising for real.

The question is, how do you flip those warm relationships into actual funding conversations? The answer is: tactfully and directly.

It shouldn’t be a surprise to your update recipients that you’re planning to raise. In fact, you may have hinted at it already (“before we start looking to raise, I want you to know us,” or “we’re planning to raise in Q1 to fuel growth”). When the moment comes, don’t send a mass blast. Instead, prioritise the investors who’ve been the most engaged with your updates — the ones who reply, click links, or have already taken calls with you. Send them a short, personal note along the lines of:

“As you’ve seen from my updates, we’ve hit A, B, and C over the last six months. Now we’re raising a $X round to scale [business]. Since you’ve been following along, I wanted to reach out early to see if this could be a fit for [Fund].”

Attach your deck if appropriate, suggest a meeting, and make it clear you’re officially in fundraising mode. This way, the “ask” feels natural — it’s just the next step in a conversation you’ve already been having.

You can also mention your raise more broadly in an update (without a direct ask), e.g.:

“P.S. – We’ve begun preparing to raise our seed round in January. If you’d like details or know someone who might be a fit, let me know.”

This creates soft urgency. You’ll often find that even investors who never replied before will resurface now — nobody likes to miss out once momentum is visible.

The beauty of this strategy is that when you enter fundraising mode, you’re not starting from scratch. These investors already know your team, your cadence, and your execution.

When you finally sit down for pitches, you’ll notice the difference: instead of “so, what do you do again?” the conversation jumps to deeper topics.

Some investors may even feel emotionally invested (“I remember when you had 100 users — now you’re at 10k!”). That sense of shared journey often accelerates a yes.

But don’t get complacent. Warm relationships don’t mean automatic checks. Run a proper process: prep your deck and data room, set a timeline, and keep new investors flowing into the funnel. The difference now is you’re pushing on open doors.

Common mistakes to avoid

Even the best strategy can backfire if you trip on common pitfalls. Here are the ones that derail founders most often:

• Waiting too long to start: The #1 mistake is going quiet until you’re desperate for capital. If you show up cold with two months of runway left, expect tough terms (if any). Dig the well before you’re thirsty. One founder raised $1.2M, went silent for 14 months, and when he came back low on cash, almost every investor ignored him. Don’t wait for the “perfect milestone.” Start 3–6 months pre-raise, even if it’s just short updates.

• Inconsistent or sporadic updates: One update followed by silence kills momentum. Investors assume something’s wrong or forget you exist. Pick a cadence (monthly or quarterly) and stick to it. Even a two-paragraph note beats disappearing. Use a template and block an hour a month — this isn’t a 3-day writing project.

• Sending fluff instead of signal: Avoid vague cheerleading (“we’re excited and working hard!”). Investors want numbers, milestones, and context. Share meaningful metrics (“MRR up 20% MoM thanks to a new referral program”) and the story behind them. Honesty about challenges builds credibility; PR spin erodes it.

• Over-optimising and overthinking: Don’t hold updates hostage until you hit some magical milestone. Progress is rarely perfect — share it anyway. Better a simple, timely update than a polished masterpiece that never ships. And don’t over-worry about individual recipients. If someone thinks you’re too early, that’s fine. You’re writing for momentum, not perfection.

Wrapping up: your playbook for “warm” fundraising

Fundraising is hard. But it’s much easier when you’ve been laying bricks for months. The Pre-Raise Update Strategy is about turning cold outreach into warm conversations, and warm conversations into real checks.

To recap:

• Start early: Build your target investor list 3–6 months before raising.

• Ask for permission: Use a simple outreach to get on their radar with updates.

• Send consistent updates: Monthly is ideal. Include metrics, wins, challenges, next steps, and asks. Keep it short, honest, and useful.

• Engage replies: Respond quickly to feedback. That’s where real relationships form.

• Transition smoothly: When it’s time to raise, reach out personally to your most engaged contacts.

• Avoid pitfalls: Don’t ghost, don’t send fluff, don’t over-polish, and don’t break trust.

Done well, you’ll notice the shift: instead of convincing strangers, you’re inviting familiar allies to join your next chapter. For many founders, that difference is everything.

This strategy works — it’s the same rhythm many great founders use with their existing investors, extended to future ones. It costs almost nothing beyond an hour each month, but the payoff in fundraising efficiency is massive. Think of it as planting seeds: you invest in relationships today so that capital grows tomorrow.

Also, recently, we published an Investor Outreach Email Sequence Pack that includes 20+ proven templates used and approved by VCs

• Cold outreach templates that actually get replies

• Warm intro scripts that make people want to introduce you

• Follow-up emails that boost response rates by ~28%

• Post-meeting templates to keep momentum alive

• Real examples of emails that led to actual investments

• BONUS:

  • Fundraising CRM Guide for Founders

  • Fundraising CRM Excel Template

You can access it here.

📃 QUICK DIVES

The quick test: Is your homepage written for VCs or users?

One of the most common mistakes founders make after raising their first round is copy-pasting their pitch deck into their website.

But here’s the truth: investors and customers care about very different things.

• Investors want the big picture - vision, TAM, defensibility, team strength.

• Customers want to know one thing: “Can this product solve my problem today?”

If your homepage still sounds like your seed pitch, you’ll lose customers who just want clarity and confidence, not a roadmap of your 5-year vision.

Here’s a simple framework (from Emily Kramer) to translate your pitch into customer-facing messaging:

1. Problem slide → Homepage hero
   Don’t say: “Radiologist-read images are frequently misinterpreted.”
   Do say: “Reduce diagnostic errors.”
   Customers want today’s pain described in their language.

2. Solution slide → Plain description of what your product does now
   Don’t say: “Compliance and security management platform.”
   Do say: “SOC 2 audit platform.”
   Clarity beats cleverness every time.

3. Market size slide → Targeted ICP callout
   Don’t say: “We’re targeting remote-first and hybrid companies globally.”
   Do say: “Built for early-stage remote startup teams.”
   The more specific, the faster customers say, “This is for me.”

4. Vision/Why now slide → About page, not homepage
   Your website’s job isn’t to inspire belief in your billion-dollar market - it’s to show why you’re useful today. Save the big vision for your story page or founder’s note.

Audit your homepage. For every line of copy, ask: Is this written for a VC or a customer? If it’s VC-speak, rewrite it in your customer’s words.

Your website doesn’t need to sell the future of your category. It needs to sell the next signup.

How to study a new market and build expertise - tactics from a Twitter PM turned healthcare founder.

Most founders try to “study” a new industry with research reports, Statista charts, or high-level TAM decks. Othman Laraki (ex-Google/Twitter PM, now co-founder/CEO of Colour, a billion-dollar healthcare company) took a different path: he learned healthcare by building into it.

Here’s a framework he used to study a market he didn’t know:

1. Map how money really flows (don’t stop at the buyer).

In healthcare, the patient isn’t the buyer. The insurer or employer often pays. Clinicians influence decisions, but administrators handle approvals. Laraki sat down with claims adjusters, procurement managers, and even lab operators to understand:

• Who influences the decision?

• Who signs the contract?

• Who sets the price?

• Who actually pays (and when)?

Do this for your own market. Draw a flowchart from first touch → payment collected. Until you see each actor and their incentive, you don’t understand the market.

2. Run a unit economics teardown.

Instead of guessing, Laraki decomposed the cost of delivering a genetic test, line by line. He set a target price ($250 vs. the $4,000 incumbents charged) and worked backwards:

• Negotiate with suppliers (“ask for a discount” was a rule, not a hope).

• Unbundle inputs (suppliers often add hidden buffers).

• Spot timing incentives (end-of-quarter discounts, prepayment benefits).

Try the same: build a spreadsheet of every cost driver. Then ask, for each line item: “How can I bend this?”

3. Test whether your wedge is a feature or a solution.

Colour’s first wedge was low-cost testing. But insurers didn’t care; they priced by median cost across labs, not the cheapest option. A $250 test was just a feature. The solution buyers actually wanted? A vertically integrated cancer care clinic that reduced overall spend.

Apply this lens: if your product is a “better part,” ask who needs a “whole car.” Sometimes you need to bundle your tech into a broader solution to unlock budgets.

4. Talk to operators, not celebrities.

Instead of chasing famous doctors or regulators, Laraki reached out to genetic counsellors, lab managers, and non-famous scientists. These were the people actually running the processes Colour needed to integrate into.

Advisors were compensated simply (hourly rate, research sponsorship), not with vague equity promises.

Your move: make a list of the doers in your industry. Cold email them with a mission-driven story. They’ll tell you how transactions really happen.

5. Treat pivots as progress, not failure.

Laraki cycled through:

• DTC tests (failed: CAC too high, LTV too low).

• Selling via physicians (failed: billing friction too high).

• Employer health plans (worked: aligned with budgets).

• Expanded into a full clinic (big unlock).
  Don’t cling to your first model. Every failed buyer segment is data that sharpens where the incentives align.

6. Don’t mistake false positives for traction.

Colour scored early deals because of influence (warm networks, Stripe founder referral). But those didn’t generalise. Laraki’s test: could a stranger buy this product on standard terms? If not, you don’t yet have PMF.

Run the same filter: strip away influence. Would the product still close deals?

Mini checklist for founders to run this week

• Map one real transaction in your target market from first touch to cash collected. Name each actor and their incentive.

• Identify your product’s current status on the buyer’s ladder: feature, component, or solution. If it’s a feature, who can you bundle with to become a solution?

• List the top three cost drivers and one concrete action to bend each (renegotiate, alternate supplier, process change).

• Book five calls with non-celebrity operators who move money or approvals. Ask them to redline your transaction map.

• Write your “no influence” test: how would a cold buyer discover, evaluate, purchase, and activate without your network?

You can read a detailed write-up here by Firstround.

How investors value pre-revenue startups with an Excel sheet.

Even with $0 in sales, your startup can have a strong valuation if you understand the levers investors pull. Valuation isn’t just about money coming in; it’s about perceived potential, team credibility, and the size of the opportunity.

Pre-revenue reality:

• You don’t have revenue yet, but investors still see value in the story you’re building.

• Early credibility builds trust with partners and makes it easier to attract top talent.

• Market perception plays a big role; how you’re seen will influence deal terms and equity split.

The common methods investors use:

• Berkus Method: Values five areas (idea, team, product, market, launch) at up to $500K each, capping most valuations at ~$2.5M.

• Scorecard Method: Benchmarks your startup against similar ones, adjusting for strengths, weaknesses, and competitive landscape.

• VC Method: Starts from a projected exit revenue, applies profit multiples, and works backwards to match investor ROI targets.

• Risk Factor Method: Rates 12 risk categories from team quality to market size, adding or subtracting value based on each.

Boosting your valuation before revenue:

• Build an MVP to prove you can deliver.

• Showcase a strong, credible founding team with relevant track records.

• Choose market comps that work in your favour.

• Land early sales or pilots before fundraising to show momentum.

We’ve included a detailed breakdown of each method, plus an Excel valuation calculator for each method, in our full guide: Valuation Guide

TODAY’S JOB OPPORTUNITIES

💼 Venture Capital & Startup Jobs

Most aspiring VCs struggle with interviews, unclear expectations, no structured prep, and generic advice that doesn’t actually help. So we partnered with a leading investor group to build an all-in-one VC Interview Preparation Guide that gives you real clarity and frameworks. (Access Here)

• General Partner - mHub | USA - Apply Here

• Director of Finance & Operations - Quona Capital | USA - Apply Here

• Investor - Expresso NB | Thailand - Apply Here

• Senior Scouting Manager - Antler | Australia - Apply Here

• Head of Investor Community - Inovexus | Remote - Apply Here

• Associate - Lightbank | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Program Manager - a16z | USA - Apply Here

• Venture Fellow - age1 | USA - Apply Here

• Venture Capital Associate - Sky9 Capital | USA - Apply Here

• Investment Summer Associate - AI Tooling - M13 | USA - Apply Here

• Event Marketing Manager - AI Fund | USA - Apply Here

• VC Platform & Events Manager - 1001 VC | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

🤝 Partnership With Us

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

👋 Hey, Sahil here — Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

• How to raise money with cold outreach: tips from a founder who raised $1 billion.

• How founders can build AI agents with OpenClaw: a step-by-step guide.

• What founders actually pay themselves in 2026?

FROM OUR PARTNER - STARTUP SCIENCE

🤝 Investors are backing a founder platform solving a $27B startup problem

Startup Science is unifying the startup ecosystem, which brings founders, accelerators, mentors, investors, universities, and service providers into one structured platform.

Instead of founders jumping between disconnected tools and advice, Startup Science guides them step-by-step through each stage of their startup journey. They get the resources they need, exactly when they need them.

Why investors are paying attention:

• Built by 12x Exited Founder Gregory Shepard, including a recent $925M transaction

• Fixing a $27B annual problem of preventable startup failure in the U.S.

• 89,000 users across four continents

• 200+ accelerators, universities, and government partners onboarded

• 3 acquisitions completed to consolidate the ecosystem

As AI makes it easier than ever to start companies, failure rates are still near 90%. Startup Science believes the issue isn’t talent, it’s a fragmented ecosystem that doesn’t set up founders for success. Their platform brings structure, timing, and alignment to the entire founder journey.

This is infrastructure for the next wave of entrepreneurship.

Invest in Startup Science →

VENTURE CURATORS’ FINDING

📬 My Favourite Finds

Media Posts:

• The only pitch preparation you will need. (Link)

• $20K to $700K ARR in 6 Weeks Inside YC. (Link)

• Be distinctive & don’t die. (Link)

• The two positions that create a durable moat. (Link)

• Investor data room superpack: everything you need to build a VC-ready data room (Guide/Templates) (Link)

Reports/Articles:

• AI industry spedning $1 to make 0.99. (Link)

• 10 charts that shows world is changing. (Link)

• Anthropic’s AI Jobs Report is a 17-page deep-dive on how AI is reshaping jobs across industries. (Link)

• What’s next for AI agent ROI? (Link)

• How to find one metric to rule them all. (Link)

🤝 PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

How to raise money with cold outreach: tips from a founder who raised $1 billion.

The probability of success for a high-growth company is predicated on your ability to raise capital. As a leader, you will never achieve your maximum entrepreneurial potential without being able to raise capital quickly and successfully.

Capital is the lifeblood of any high-growth company. It has to be treated as a key priority. It can never be outsourced or downgraded to a second priority.

Brett Adcock, the founder and CEO of Figure AI, secured $1 billion in 2025. Also, his previous ventures, Adcock has successfully raised significant capital: Vettery was acquired for $110 million, Archer Aviation went public with a valuation of $2.7 billion and raised over $1 billion. He shared the framework for founders to raise capital. So read along -

“Remember - Investors are looking for certain traits. Without these, you have little chance of raising a single dollar. Let’s talk about how you can use the formula to build a repeatable process for raising capital.

Fundraising Formula:

You need to maximise the number of shots on goal in fundraising. Aim to get as many meeting attempts with investors as possible. The above formula for capital raising is a combination of -

1. the number of qualified investors you are targeting, combined with

2. the company idea & overall branding.

The first step in the fundraising process is defining success. The ultimate goal is to raise capital successfully. If you work backwards from there, getting to a lead investor “term sheet” is the next clear milestone. And if you work backwards once more, the goal is to maximise the number of unique investor meetings.

The Fundraising Formula is a way to compute how to maximise the number of qualified investor meetings. Fundraising is about talking to 200 investors and finding the 1 person who will take a bet on you. I’ve always experienced really low conversion rates in these efforts - it’s never been easy for me to raise capital. And that’s quite normal.

Here is the sensitivity to each part of the formula:

1. Qualified investors: You want to drive this number as high as possible. I speak to so many founders who are “waiting for referrals”. This inevitably means this number is too low. You want to identify every possible investor on the planet or else your equation will point to low odds of success.

2. Outreach: This is your reach-out process to investors. I gauge this as a percentage conversion rate. This will be really sensitive to the way you are reaching out (referral vs. cold email) and the messaging associated with the reach-out process (e.g. subject and body of a cold email). There is an art to this. The conversion rate can greatly increase if you treat it as a recursive project to get better.

3. Idea: This is the company idea, and it’s fixed. There is very little you can do besides pivot your company to a new idea. The idea will instantly either A) resonate or B) turn off investors based on their industry focus or their internal thesis of the market/industry.

4. Branding: This is how well-branded or attractive your company is at first glance. You have limited time to impress an investor enough for them to commit to a meeting. There are many ways to drive this up, including a nice investor deck. But ultimately, that’s a small lever, so don’t stress too much over this.

5. Investor Meetings: This is the output of all your hard work. The goal is to drive up the number of qualified investor meetings as high as possible. You want to do 50-200 of these meetings. The more qualified investor meetings, the higher your odds of raising capital.

Building the Fundraising Machine:

I believe in building and executing on a highly structured “machine” for fundraising processes. This machine is something you can wash, rinse, and repeat. This should be similar to how you would run a sales or marketing team.

I track this process in a Google sheet, similar to how you track items in a CRM. Let’s break down the key elements of the formula so you can go out and execute it yourself.

Key Elements of a Fundraising Machine:

1. Qualified List of Investors: This is a list of investors who are tailored specifically to your company. Start with investment groups that match well with your company sector. Research to find an investment partner who covers your specific industry and maturity. Ideally, you have found 100% of every investor on the planet that you think could give you a term sheet.

2. Outreach Process: Reach out to investors with the goal of setting up a meeting. Try to get as many referrals as possible, then move to outbound for the rest.

3. Pitching: Your first interaction with the investor that includes them previewing the deck. Optimise the conversion rate from the first meeting to the term sheet.

4. Term Sheet: Receiving a term sheet is the ultimate goal for the first leg of capital raising. Negotiate quickly and fairly to get the lead investor signed up.

The entire process from start to finish will take at least 3 months. Spend 30 days preparing the investor deck and data room, mapping out investors, and your cold email template. The next 30-60 days are for investor outreach and meetings. Once you have a term sheet, it generally takes <10 days to negotiate and sign, and you usually close in 30 days.

Additional Capital Raising Insights:

1. Show metrics and customers that can potentially correlate to long-term success for early investors to understand your product’s potential.

2. Understand VC mandates and match them to your industry to avoid spinning your wheels.

3. Showcase your “Championship Team” - there’s no way you’re building a great product without a #1 team.

4. Aim for 80% of investor pitches to come from outbound processes (cold calls or emails) and 20% from inbound processes (referrals).

5. Build a well-branded investor deck, as it’s often the first impression investors have.

6. Recognise that interested investors move quickly. If they’re not hurrying, it’s likely a sign you don’t have a deal.

7. Focus on finding a “lead” investor who can give you a term sheet and lead your round.

8. Balance fundraising time with product development, as fundraising can be distracting for founders.

Remember, fundraising is a competitive process. Once the round is complete, put your attention back to the product and get building. I suggest setting up a quarterly “nurture” sequence to keep investors updated.”

Many founders still struggle to write cold emails to investors. They’re often confused about what to include, how to reply, what to send, and how to follow up.

With this in mind and drawing insights from leading founders and investors, we’ve created an all-in-one guide: Investor Outreach Email Sequence Pack: 20+ Proven Templates Used & Approved by VCs.

More than 100+ founders are already using it and have found it extremely useful.

TODAY’S RESOURCE PICK

📬 Startup legal document pack

Messy or missing legal documents can slow down fundraising and create unnecessary risk.

This Startup Legal Document Pack gives you everything you need to incorporate, protect equity, and raise confidently — all in one place.

Inside:

• Formation & incorporation documents (C-Corp, LLC)

• Founder agreements & equity splits

• SAFE notes, term sheets, and cap tables

• Employment, contractor & advisor agreements

• IP protection (NDAs, invention assignments)

• Customer contracts, TOS & privacy policies

Built with startup attorneys and VC teams. Investor-ready. Fully editable.

If you want your legal foundation set up properly before you scale or raise, this pack does the job.

Get access here →

📃 QUICK DIVES

How founders can build AI agents with OpenClaw: a step-by-step guide.

AI agents are quickly moving from demos to practical tools people actually use every day. Instead of manually prompting models again and again, an agent can run tasks for you - checking emails, researching topics, scheduling work, or sending updates through messaging apps.

Recently, Alex Prompter shared a way to architect an AI agent using OpenClaw. What’s useful about his approach is that instead of experimenting randomly, you start with a structured blueprint that defines the entire system: the workflow, the tools, the guardrails, and how the AI should behave.

Below is a simplified and practical breakdown of how this method works.

First: understand what OpenClaw actually does

OpenClaw is a local-first AI agent framework. Instead of running in the cloud like most AI tools, the agent runs directly on your own machine and connects to messaging platforms such as:

• WhatsApp

• Telegram

• Discord

• Slack

• Signal

You can interact with your AI agent through these apps the same way you would message a colleague.

The system works with three main components:

1. The LLM (the brain)

This can be Claude, GPT-4o, or DeepSeek. The model handles reasoning, understanding instructions, and generating decisions.

2. OpenClaw (the body)

This is the execution layer. It performs actions like:

• browsing the web

• accessing email

• scheduling calendar events

• running automations

• triggering scripts

3. Skills (the capabilities)

Skills extend the agent’s abilities. For example:

• search the internet

• read emails

• send notifications

• Run scheduled jobs

• interact with APIs

Together, these components allow the AI to think, decide, and execute tasks.

The key idea: design the entire agent before building it

Instead of manually experimenting with configurations, Alex recommends prompting Claude to generate a full agent architecture first.

You describe the goal of your agent, and the AI generates a detailed blueprint including:

• the agent’s purpose

• which messaging platform to use

• Which LLM works best

• What skills are required

• The system prompt that defines the agent’s behaviour

• the workflow logic and decision paths

• safety guardrails

This makes the build process much more structured.

The prompt used to design the agent

"You are a world-class OpenClaw agent architect.

OpenClaw is a local-first autonomous AI agent that runs on your own
device and operates through messaging platforms like WhatsApp,
Telegram, Discord, Signal, and Slack. It connects to Claude,
GPT, or DeepSeek as its brain, and uses Skills to extend
its capabilities.

My goal: [DESCRIBE WHAT YOU WANT YOUR OPENCLAW AGENT TO DO]

Build me a complete OpenClaw agent blueprint with:

AGENT NAME & PURPOSE: [Name + one-line objective]

RECOMMENDED CHANNEL: Which messaging platform to use and why (WhatsApp / Telegram / Discord / Slack / Signal)

LLM SELECTION: Which model to connect (Claude / GPT-4o / DeepSeek) and why for this specific use case

SKILLS NEEDED: List each skill the agent requires, what it does, and how to configure it (browser search, cron scheduling, email reader, calendar manager, note generation,  web scraping)

WORKSPACE SETUP: How to structure the workspace and agent sessions for this task

SYSTEM PROMPT FOR THE AGENT: Write the full system prompt that goes into OpenClaw for this agent's behavior, tone, and decision rules

TOOL CONNECTIONS: Which tools to enable (browser, cron, canvas, email, calendar, etc.) with exact purpose for each. For example - 
- browser → research tasks
- email → inbox automation
- calendar → scheduling meetings
- cron → recurring tasks
- canvas → document creation

WORKFLOW STEPS:
1. Trigger → Action → Output
2. Decision point → If X then Y, if Z then W (Map every step completely)
For example - User sends message → agent searches sources → summarizes insights → sends response

SAFETY GUARDRAILS:
- What permissions to restrict
- Confirm-before-acting rules for destructive actions
  (deleting emails, sending messages, etc.)
- How to run: openclaw doctor to check for risks

EXAMPLE COMMANDS TO TEST IT: Give me 5 real commands I can send via WhatsApp/Telegram
to test this agent immediately

Be specific. Production-ready. No placeholders."

How to actually build the agent

Once Claude generates the blueprint, the build process is straightforward.

1. Step 1: Install OpenClaw using the terminal: openclaw onboard

2. Step 2: Run the setup wizard and configure:

• gateway

• messaging channel

• workspace

3. Step 3: Copy the architecture prompt and insert your goal.

4. Step 4: Paste it into Claude to generate the full agent blueprint.

5. Step 5: Implement the system prompt, tools, and skills inside OpenClaw.

6. Step 6: Test the agent using the example commands generated by Claude.

If you’re building products or running a startup, agents like this can automate surprisingly useful workflows.

Examples:

• Customer feedback summarizer - reads incoming support emails and generates daily insights.

• Investor research agent - Tracks funding announcements and summarises interesting companies.

These kinds of tools are increasingly becoming personal operating systems for knowledge work.

And frameworks like OpenClaw make it possible to build them locally with full control over how they operate.

What founders actually pay themselves in 2026?

Founder compensation is one of the most misunderstood topics in startups.

From the outside, people assume founders of venture-backed companies are paying themselves massive salaries. In reality, most founders keep salaries relatively modest because the real upside comes from equity, not salary.

A new report analysing (Creandum) compensation across hundreds of startups (from bootstrapped to Series D) shows a clear pattern:

Founder salaries increase steadily with stage, but the biggest payoff still comes from ownership.

Founder salaries grow with the company stage

Founder compensation tends to scale alongside funding and company maturity.

Median salaries increase consistently as companies raise larger rounds:

• Seed founders: ~€89K median salary

• Series A founders: ~€154K

• Series B founders: ~€199K

• Series C founders: ~€225K

Bonuses also become more common as companies scale. By Series C, around 70% of founders receive bonuses, reflecting more structured compensation systems.

Interestingly, Pre-Seed salaries actually declined about 12% year-over-year, suggesting early founders are conserving cash and extending runway rather than paying themselves more.

Another surprising data point: bootstrapped founders sometimes earn more than Pre-Seed founders. Many profitable bootstrapped companies generate enough revenue to support higher salaries even without outside capital.

The biggest salary gap is in geography

Where a founder builds matters a lot.

US founders earn significantly more than their European peers.

On average:

• US founder salaries are about 1.5× higher than European founders

• Companies that raised €100M+ pay ~€225K in Europe vs ~$350K in the US

The difference reflects several factors:

• larger funding rounds

• the higher cost of living

• stronger venture competition for talent

Even within Europe, there are interesting regional differences. Baltic and Nordic founders reported surprisingly strong compensation levels, sometimes surpassing those in more traditional tech hubs.

AI founders are not the highest paid

Given the massive funding flowing into AI startups, many people assume AI founders are earning the most.

The data says otherwise.

Median founder salaries by sector:

• FinTech: ~€142K

• CleanTech: ~€142K

• AI-native startups: ~€122K

Two structural reasons explain this.

FinTech companies often monetise quickly, allowing founders to justify higher compensation earlier.

CleanTech companies tend to have long development cycles, so higher salaries help compensate founders during the long journey to profitability.

AI startups, despite the headlines, still vary widely in funding size and maturity, meaning founder compensation hasn’t yet caught up with the hype.

CEOs actually earn less than revenue-focused founders

Another interesting trend: founders responsible for revenue tend to earn more than CEOs.

Median compensation by founder role shows:

• CRO / CCO founders: ~€133K

• CEO founders: ~€118K

• Product-focused founders: ~€85K

This reflects the current market environment.

Investors and boards are prioritising revenue generation and capital efficiency, rewarding founders who directly influence growth and sales.

The gender gap is shrinking, but representation remains low

Encouragingly, the pay gap between male and female founders is narrowing.

At Seed and Series A:

• Female founders earn within 3–5% of male founders

At Series C, female founders even slightly out-earned their male peers in median compensation.

However, there’s still a major representation problem.

Only 13% of founders surveyed were women, reflecting the broader startup funding gap where:

• only 2.3% of global VC funding goes to female-only teams

Equity still matters far more than salary

The biggest financial outcome for founders is still equity ownership.

And one factor dominates equity outcomes: how early you joined the company.

For example:

• Founder joining at incorporation: ~48–50% ownership

• Founder joining six months later: ~20–24%

That difference can translate into millions in exit value.

In other words, while salary matters for day-to-day life, the real financial impact comes from ownership and dilution over time.

How founders should think about compensation

The report suggests a practical framework for setting founder pay. Good founder compensation balances five principles:

• Alignment: Most founder wealth should come from equity, not salary. Salary should support focus, not create lifestyle inflation.

• Sufficiency: Pay enough to remove financial stress, but avoid draining company cash.

• Transparency: Compensation decisions should be documented, benchmarked, and board-approved.

• Accountability: Cash and bonuses should align with performance and operational impact.

• Consistency: Founder pay should evolve predictably with company maturity, not spike suddenly after funding rounds.

Founder salaries are rising again after the startup slowdown in 2023. But the data reinforces a core truth of startups: Founders don’t get rich from a salary. They get rich from equity.

Salary keeps the lights on. Ownership is what changes your life.

TODAY’S JOB OPPORTUNITIES

💼 Venture Capital & Startup Jobs

All-In-One VC Interview Preparation Guide: With a leading investor group, we have created an all-in-one VC interview preparation guide for aspiring VCs. Don’t miss this. (Access Here)

• Venture Capital Associate - Sky9 Capital | USA - Apply Here

• Analyst - Infrastructure & Real Assets - Stepstone Group | Australia - Apply Here

• Analyst / Associate - Saints Capital | USA - Apply Here

• Investor - Expresso NB | Thailand - Apply Here

• Head of Investor Community - Inovexus | Remote - Apply Here

• Associate - Lightbank | USA - Apply Here

• General Partner - mHub | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Program Manager - a16z | USA - Apply Here

• Investment Summer Associate - AI Tooling - M13 | USA - Apply Here

• VC Platform & Events Manager - 1001 VC | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

🤝 Partnership With Us

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

👋 Hey, Sahil here — Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

Big idea + report of the week :

• 5 predictions that reveal where AI agents will actually make money.

• Does bigger ARR equal better IPO outcomes?

• Are AI companies secretly training their models on your chats?

Frameworks & insightful posts :

• 31 ways to grow your startup fast in 2026.

• Sequoia Capital: The biggest AI opportunity for founders.

• Framework: How to get warm intros to VCs without a network?

• How this founder used Claude to validate the idea (Now at $2.3k MRR)

FROM OUR PARTNER - UPDF

🤝 UPDF - An AI PDF Editor Built for People Who Don’t Have Time to Waste

For professionals who handle large volumes of documents, UPDF’s advanced search and organisation features are invaluable. Quickly locate key information with keyword search, categorise documents with folders and tags, and navigate efficiently with smart indexing.

You can also batch process and export files, making it easier to prepare reports, presentations, or academic materials without repetitive work.

Whether you’re managing contracts, research papers, or business reports, UPDF gives you the flexibility to work seamlessly across Mac, Windows, iOS, and Android.

Try UPDF and simplify how you manage PDFs →

🤝 PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

START WITH

🧠 Big idea + report of the week

5 predictions that reveal where AI agents will actually make money.

Everyone is talking about AI agents. But the real question isn’t whether agents will exist - it’s where the money will flow once companies start deploying them at scale.

A recent report from CB Insights breaks this down using hiring signals, funding activity, and enterprise surveys.

Building agents is no longer the hard part. Deploying, securing, and scaling them inside enterprises is.

That shift is creating several new markets around the “agent stack.” Here are the five areas where the biggest opportunities are emerging.

1. Multimodal AI agents will dominate customer service

Customer support is quickly becoming the largest deployment ground for AI agents. According to the enterprise survey, it’s already the #1 adoption area for enterprise AI agents, with 115+ companies competing in the market and at least 6 private companies generating $100M+ in revenue.

The next wave of competition will revolve around multimodal capabilities - agents that seamlessly operate across: voice, text, documents, images and video inputs.

Voice is becoming the real proving ground. Handling interruptions, latency, and conversational turn-taking requires much deeper architectural design than text agents simply adding voice later.

This is why AI-native entrants like Sierra (2023) and Crescendo (2024) are quickly climbing into the top revenue leaders alongside earlier companies like PolyAI.

So the next generation of customer service tools won’t just automate tickets, they’ll manage conversations across every channel.

2. Voice AI will shift to “high-touch” deployments

Most AI tools today follow a self-serve model. Voice AI is going the opposite direction.

Startups such as ElevenLabs, Deepgram, Bland AI, Vapi, and Synthflow are increasingly hiring forward-deployed engineers and solutions architects to work directly with enterprise clients.

Why? Most enterprises still struggle with real-world AI implementation.

CB Insights’ survey found:

• 65% of companies lack internal expertise

• 59% cite integration complexity as the main barrier

So instead of selling software alone, vendors are embedding engineers directly inside customer environments to integrate voice agents with legacy systems.

This approach sacrifices some margin but dramatically increases enterprise adoption - especially in industries like healthcare, finance, quick-service restaurants, and government.

3. AI agent security will become mandatory infrastructure

Agents introduce a new type of security risk. Unlike copilots, agents can:

• execute code

• call APIs

• move data between systems

• make decisions autonomously

That means every tool an agent accesses becomes a potential attack surface.

Security vendors are responding with continuous red teaming systems, tools that simulate attacks against AI agents to uncover vulnerabilities before real attackers do.

These systems test issues like: prompt injection, agent hijacking, tool misuse and multimodal attacks

Startups like Virtue AI are building platforms that continuously stress-test agents in production environments.

Large cybersecurity players are already moving quickly:

• Palo Alto Networks acquired Protect AI

• Check Point acquired Lakera

• F5 acquired Calypso AI

The message is clear: AI security is becoming an extension of traditional cybersecurity infrastructure.

4. AI agent observability will trigger a wave of acquisitions

Once agents are deployed, enterprises need tools to answer a simple question:

What are these agents actually doing? That’s why the market for agent observability and evaluation tools is heating up.

These tools help companies:

• monitor agent decisions

• audit behavior

• Evaluate model performance

• manage permissions and governance

M&A activity across the AI agent ecosystem jumped 10x in 2025, approaching 100 deals, with observability and evaluation tools becoming prime acquisition targets.

Examples include:

• Coralogix acquiring Aporia

• Snyk is acquiring Invariant Labs

• ClickHouse acquiring Langfuse

• Anthropic acqui-hiring HumanLoop

Even infrastructure giants are positioning themselves.

Datadog has already invested in multiple observability startups, including LangChain, Arize, Braintrust, and Patronus AI. Enterprise platforms like Salesforce and Workday are also expected to acquire reliability tooling to support their own agent ecosystems.

As Bessemer Venture Partners recently noted, AI evaluation remains one of the biggest bottlenecks in enterprise deployment.

Solving that bottleneck could unlock the next wave of adoption.

5. World models will power the next generation of physical AI

While software agents are transforming enterprise workflows, another frontier is emerging: physical AI agents.

These systems rely on world models, AI systems that simulate real-world physics such as gravity, friction, and object interactions.

World models allow robots, autonomous vehicles, and factory systems to train in simulated environments before operating in the real world.

Signals suggest this market is accelerating quickly:

• Funding activity ranks in the top 3% of CB Insights markets

• Talent from leading AI researchers is entering the space

• Major companies are hiring aggressively to build simulation environments

Examples already appearing across industries include:

• Waymo is building 4D world models for autonomous driving

• Agility Robotics and Figure AI using Nvidia simulation models

• Manufacturing systems using AI agents to autonomously manage factory operations

The result is a new foundational layer for robotics and physical automation.

Half the companies in this category are still early-stage or research-focused, but the hiring, investment, and deal activity suggest world models will soon become core infrastructure for physical AI systems.

Put together, these signals reveal an important shift.

• The first wave of AI focused on models.

• The second wave focused on applications.

• The next wave will focus on deployment infrastructure.

And the companies that capture value will likely sit in the layers that make AI agents reliable, secure, and usable inside real businesses.

Does bigger ARR equal better IPO outcomes?

For years, venture capital has pushed a simple narrative: bigger ARR equals better IPO outcomes.

But when you actually look at public market data, the relationship is much weaker than most people assume.

Dan Grey recently highlighted an analysis from Meritech Capital that examines how ARR scale correlates with valuation multiples in public SaaS markets. The conclusion challenges one of venture capital’s most persistent assumptions.

There is very little correlation between ARR size and valuation multiples.

In a regression comparing revenue multiples vs ARR scale, the R² is just 0.11 - meaning ARR explains only a small portion of valuation differences. When outliers like Adobe and Salesforce are included, the correlation drops even further to around 0.02.

In simple terms: bigger companies do not automatically get higher valuation multiples.

The reason many investors believe they do comes down to something simpler -selection bias.

Companies with billions in ARR typically already have strong fundamentals:

• durable growth

• strong margins

• leadership positions in large markets

• defensible competitive advantages

Those qualities allow them to scale into the largest revenue buckets. But it’s those underlying qualities, not the scale itself, that public markets reward.

In other words, scale often reflects quality, but it doesn’t cause it.

The problem, according to Grey, is that venture incentives have gradually distorted this distinction.

Private market investors frequently optimise for rapid ARR growth at almost any cost, because higher revenue numbers support larger funding rounds and valuation markups. That dynamic encourages companies to prioritise aggressive scaling even when the underlying business quality isn’t strong.

Over time, this creates several structural problems.

• Companies stay private far longer than necessary.

Public investors miss the early growth phase while private capital captures most of the upside.

• Post-IPO performance deteriorates.

Businesses optimised for growth at all costs often struggle once public markets demand profitability and capital discipline.

• Capital flows toward scalable models rather than innovative ones.

Ideas that can scale ARR quickly attract funding, even if they aren’t the most transformative technologies.

The result is an ecosystem where ARR becomes the headline metric, even though public markets evaluate companies using a much broader set of criteria.

Public investors typically look at a combination of factors:

• sustainable growth rates

• margins and operating leverage

• total addressable market

• product differentiation and competitive positioning

Revenue scale plays a role, but it’s only one piece of the puzzle.

This distinction also explains why some smaller SaaS companies can still command strong multiples if they demonstrate exceptional fundamentals, while larger companies with weaker economics may trade at lower valuations.

The deeper insight for founders is simple. Chasing ARR milestones purely to hit arbitrary “IPO thresholds” can be misleading. Public markets don’t reward size alone, they reward durable business quality that can scale over time.

In many cases, scale is simply the outcome of those fundamentals working. Not the reason investors value the company in the first place.

Are AI companies secretly training their models on your chats?

Most people assume their conversations with AI are private. But according to new research, that assumption may not match reality.

A group of Stanford researchers, King, Klyman, Capstick, Saade, and Hsieh, recently analysed the privacy policies of six major AI chatbot providers: Amazon, Anthropic, Google, Meta, Microsoft, and OpenAI. Together, these companies control an estimated 90% of the U.S. chatbot market.

Their key finding: every one of these companies trains AI models on user chats by default.

The shift became universal in September 2025, when Anthropic moved from an opt-in system to an opt-out model, aligning with the rest of the industry.

That means, unless a user explicitly changes settings, their conversations may be used to improve AI systems. The research surfaced several important dynamics shaping how AI companies handle user data.

• Training on chat data is now the default industry practice.

All six major developers allow model training using user inputs and outputs. While enterprise customers are typically excluded from this data usage, individual consumers are included by default.

• Enterprise and consumer users receive very different protections.

Companies generally opt enterprise customers out of model training automatically, creating a two-tiered privacy system. Businesses get stronger data protections, while hundreds of millions of consumer users remain part of the training pipeline unless they manually opt out.

• The scale of data involved is enormous.

OpenAI alone reported around 700 million ChatGPT users as of August 2025. Given that most people never adjust default settings, a massive amount of conversational data is likely feeding model training systems.

Researchers also highlighted how difficult it can be for users to even understand these practices.

Across the six companies, the team analysed 28 separate policy documents, including privacy policies, FAQs, and additional sub-policies. Information about data usage was often scattered across multiple pages rather than clearly explained in a single place.

In one example, OpenAI’s data practices required six different documents to fully understand how chat data may be used.

That fragmentation makes it extremely difficult for ordinary users to grasp what happens to their conversations.

Another subtle dynamic involves how opt-out choices are framed.

Some platforms present the option to disable training with messaging such as “Help improve the model for everyone.” Researchers argue that framing like this can encourage users to leave defaults unchanged, aligning user behaviour with company incentives.

For AI developers, this approach has a clear advantage: conversational data is one of the most valuable resources for improving models, especially as companies compete to build more capable systems.

But it also raises broader questions.

As AI becomes embedded in everyday workflows, from writing and coding to therapy-like conversations, the boundary between private communication and training data becomes increasingly blurred.

If privacy matters, it’s worth checking platform settings and understanding whether conversations are being used for training.

Because in most cases today, the default answer is yes.

SOMETHING MORE

🧩 Frameworks & insightful posts

31 ways to grow your startup fast in 2026.

Most founders try every growth tactic they see on the internet. The fastest-growing SaaS companies don’t. They sequence their growth — using the right 3–5 levers at the right revenue stage. ICONIQ, Founderpath, and hundreds of SaaS founders keep repeating the same thing: growth is not about working harder, it’s about picking leverage that compounds.

Below is a cleaner, practical breakdown you can follow no matter where you are shared by Nathan Latka :

Stage 1: Bootstrap to $1M Revenue — Speed and Cash First

At this stage, anything slow is a distraction. You want distribution loops, cheap awareness, and anything that directly converts attention into revenue.

What works best

• Virality loops: Add sharing triggers like “Powered by…”, referral rewards, and shared dashboards. This is how tools like Typeform and Dropbox grew without ads.

• Webinars that sell Run tight, niche sessions with a strong CTA. Founders who are good storytellers convert faster because webinars collapse trust cycles.

• Product Hunt launches. One strong PH push = thousands of new users. Relaunch often, test different angles, and collect emails.

• Cold outreach with personalisation, Loom videos + targeted lists outperform templates. Even 100 great emails can get your first $10K–$20K MRR.

• Influencer marketing (micro-creators only) Bottom-up trust works better than polished brand ads. Partner with niche creators who feel “native” to your audience.

What founders should remember At <$1M, your job is to find one repeatable way to acquire users cheaply. Most founders stall because they chase too many channels instead of doubling down on one.

Stage 2: $1M–$3M ARR — Build Compounding Inbound + Partnerships

This is where the engine needs more predictability. Founders who grow 300%+ yearly build content systems, partner loops, and scalable inbound.

Plays that work consistently

• Free tools Calculators, templates, grading tools — these index extremely well and convert better than blog posts. Shopify scaled free tools into billions.

• Newsletter Publishing weekly builds authority, lowers CAC, and compounds trust. At this stage, the founder voice is still the strongest asset.

• Co-marketing with partners Joint webinars, shared ebooks, mutual case studies. This drives warm leads at almost zero cost.

• Affiliate programs Think of it as a distributed sales team that only gets paid on performance. Stripe and Zapier used this extremely early.

• Compare pages and programmatic SEO “X vs Y” pages rank fast for high-intent queries and consistently pull in users without spending.

Founder reminder Your time shifts from raw selling → building repeatable acquisition flywheels.

Stage 3: $3M–$5M ARR — Paid Growth + Acceleration

Now you have a working engine. The question becomes: how do we scale what already works while keeping CAC under control?

High-impact tactics

• Retargeting Target people who already touched your product — demo viewers, free trials, website visitors.

• Lookalike ads Feed Meta/Google a list of your top 500 users. This performs better than any cold targeting.

• LinkedIn + short-form video ads Works well for B2B if messaging focuses on revenue triggers, not features.

• Acquisitions (small ones) Buy small tools, Chrome extensions, or utilities that give you more distribution or features instantly.

Founder reminder Paid acquisition works when your retention is strong. If churn is weak, nothing in this stage will scale.

Stage 4: $5M+ ARR — Build Moats That Defend Your Category

At this point, growth comes from trust, brand presence, and ecosystem dominance. These plays are slow but incredibly durable.

Moats that actually work

• Review site dominance G2, Capterra, and TrustRadius. Enterprise buyers care more about peer validation than ads.

• App marketplace distribution: Salesforce, HubSpot, Zapier — marketplaces can become passive growth channels.

• Books + podcasts + events These aren’t vanity — they’re how you cement a category narrative and attract higher-ticket buyers.

• Live events + communities You build a tribe around your brand. Companies like Notion and Figma built defensibility with community long before they had revenue.

Founder reminder At scale, your advantage is no longer speed. It’s authority, relationships, and the ecosystems you control.

You don’t need all 31 tactics. You need the right ones for your stage. The best founders don’t hustle harder, they sequence smarter — and they resist the temptation to mix Stage 1 tactics with Stage 4 tactics. That’s how companies go from $0 to $10M without burning themselves out.

Sequoia Capital: The biggest AI opportunity for founders.

Most founders building AI products worry about the same thing: What happens when the next model update turns my product into a feature?

It’s a valid concern. If you’re selling a tool, you’re competing directly with the model itself. But there’s another path emerging, one that may create much larger companies.

Instead of selling the tool, sell the work. For example, a company might pay:

• $10K/year for accounting software

• But $120K/year for accountants to close the books.

The next generation of AI companies won’t just sell accounting software; they’ll close the books themselves.

That shift from copilot (tool) to autopilot (outcome) could unlock a massive opportunity.

Where AI agents are actually being used today

Recent data shows where AI agents are already doing real work.

Nearly half of all agent activity today is in software engineering, while every other category is still relatively small.

Top domains where AI agents are deployed today:

• Software engineering: ~49.7%

• Back-office automation: 9.1%

• Marketing & copywriting: 4.4%

• Sales & CRM: 4.3%

• Finance & accounting: 4.0%

• Data analysis: 3.5%

This pattern reveals something important.

Software engineering is largely intelligence work, translating specifications into code, debugging, and testing. AI excels at this type of structured problem-solving.

Other professions still require more judgement, which slows automation.

But that gap is shrinking quickly.

Copilots vs Autopilots

Most AI startups today are building copilots. A copilot helps professionals do their job faster. The human remains responsible for the output.

Examples:

• Harvey → helps lawyers

• Rogo → helps investment bankers

• coding copilots → assist developers

But a new category is emerging: autopilots. Autopilots don’t help professionals - they replace the workflow entirely.

Examples already appearing:

• Companies drafting NDAs directly instead of hiring lawyers

• AI systems are closing accounting books

• Insurance purchased through AI brokers instead of human brokers

The difference is massive.

• A copilot captures the software budget.

• An autopilot captures the labour budget.

And labour budgets are far larger.

The smartest place to start: outsourced work

The easiest entry point for autopilots isn’t replacing employees, it’s replacing outsourced services.

When a task is already outsourced, three things are true:

• The company already pays an external vendor

• There’s a clear budget line

• The buyer is purchasing an outcome, not a tool

That makes switching to an AI service much easier.

For example:

• Replacing a legal service with an AI legal service → simple vendor swap

• Replacing internal employees → requires a full organisational change

So the playbook is simple: Start with outsourced tasks that are intelligence-heavy.

Some of the biggest opportunities

Sequoia Capital shared this - When you map services markets by intelligence vs judgment and outsourced vs internal, several large AI opportunities appear.

Examples include:

• Insurance brokerage ($140–200B) Brokers mostly compare policies and fill forms, highly structured work that AI can automate.

• Accounting & audit ($50–80B). The profession faces a major labour shortage, making firms more open to automation.

• Healthcare revenue cycle ($50–80B). Medical coding translates clinical notes into standardised codes, complex but rule-based work.

• Claims adjusting ($50–80B). Interpreting insurance policies and calculating settlements follow structured processes.

• Tax advisory ($30–35B). Multi-jurisdiction tax rules are exactly the type of complexity AI handles well.

• IT managed services ($100B+). Monitoring systems, patching, and troubleshooting are repetitive intelligence tasks.

• Supply chain & procurement ($200B+) Large enterprises leave huge portions of supplier negotiations untouched because humans can’t manage the scale.

Why this shift matters

For every $1 spent on software, roughly $6 is spent on services. That means the real AI opportunity may not be software tools at all, it may be AI-native services companies.

Companies that:

• sell outcomes instead of tools

• automate entire workflows

• Capture labour budgets rather than software budgets

The next generation of AI giants may look less like traditional SaaS companies and more like services firms powered by AI systems.

In other words, the next trillion-dollar company might be a software company that looks like a services company.

Framework: How to get warm intros to VCs without a network?

Toby Egbuna shared one of the best no-network fundraising tactics and ways to get intros

Instead of cold emailing 300 VCs, he recommends this step-by-step process:

• Shortlist 30 top funds you actually want

• Use Crunchbase to find their 3 most recent portfolio founders

• Connect with those founders on LinkedIn

• Email and ask for a quick chat about their experience with the fund

• If the call goes well, ask for a warm intro

Why it works:

• Founders usually say yes

• VCs value intros from their own portfolio more than cold emails

• You often get bonus intros to other funds too

“You don’t need a huge network. You just need a system.”

Here’s the 14-step process Toby recommends:

1. Build a list of 300+ VCs

2. Prioritise the top 10% (30 funds) based on thesis, connections, brand, and fit

3. Filter for just those 30

4. Go to Crunchbase

5. Search each fund

6. Find the 3 most recent investments

7. Note founder names, sites, and companies

8. Repeat for all 30 funds

9. Go to LinkedIn

10. Send a connection request to every founder

11. Open your email

12. Email each founder asking about their experience (include a short, warm note)

13. Do the calls, be genuinely curious

14. If it goes well, ask for the intro

How this founder used Claude to validate the idea (Now at $2.3k MRR)

From a Reddit founder who turned 12 scattered SaaS ideas into one that actually made money, all by using Claude for validation instead of guesswork.

They had tried everything before: built two projects, got almost no users, lost motivation. The turning point came when they stopped building from instinct and started validating using real user complaints.

Here’s the process that worked:

1. Use AI to find real pain, not fake demand.

They prompted Claude to scrape Reddit, Quora, G2, and other public forums for real complaints about “cold email personalisation.”

It came back with 3 pages of data, real quotes from salespeople complaining about manual personalisation, bad templates, and low reply rates.

2. Ask AI to rate the opportunity.

They told Claude to score the idea 1–10 on “demand vs. competition.” It gave an 8.5, with reasoning about why the gap existed. That was enough to commit.

3. Build the simplest version fast.

They built Introwarm, upload a prospect list, and it generates personalised openers by analysing what each person posts or reacts to online.

Soft-launched it quietly → got the first paid user ($29) in week 2 → now at $2.3K MRR.

What founders can steal from this:

• Users are already validating your ideas for free, in the form of online complaints.

• AI can connect patterns way faster than manual research.

• You don’t need “perfect” validation, just enough data to prove people care.

Prompt

You are my personal market research assistant. I’m a solo developer, fully bootstrapped, building B2B or prosumer SaaS tools with a strict infrastructure budget of $200/month or less. No big team, no venture capital, just me coding and deploying.

Your job is to scan the web for current, real pain points that users, developers, or small businesses are struggling with. You can look in forums (Reddit, Hacker News, Indie Hackers, Twitter/X, GitHub issues, niche Discords, Quora), reviews, blog comments, etc.

My main goal is to scale a product from $0 to $10k month and see how it goes from there.

For each opportunity you surface, break it down like this:

1. Pain Point: Describe the real-world problem or complaint users are having.

2. Target Audience: Who is having this problem? Be specific.

3. Why It Hurts: Explain why this problem matters or costs them time, money, or peace of mind.

4. Tool Idea: Suggest a simple SaaS or tool I could build to solve it, considering my constraints:
    - Solo dev
    - <$200/month infra
    - MVP in ~2 weeks

5. Monetization Potential: Explain how it could realistically make money (subscription, pay-per-use, etc.)

6. Bonus: If applicable, mention existing solutions and what sucks about them (pricing, UX, complexity, etc.)

Keep the tone direct, no fluff, and prioritize practicality over theory. Focus on problems people are actively complaining about, not abstract trends or “maybe someday” ideas.

NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Axiom Partners, a San Francisco, CA-based early-stage venture capital firm, closed its $52M inaugural fund. (Link)

• General Catalyst, a Silicon Valley–based venture firm with $43B+ AUM, has committed $5 billion to India over the next five years to back startups. (Link)

• Quantonation Ventures, a NYC and Paris, France-based venture capital firm focused on quantum and physics-based technologies, closed its second flagship fund at €220m. (Link)

New Startup Deals

• Vivox AI, a London, UK-based technology company building regulator-ready, atomic AI agents for AML, KYB/KYC and financial crime, raised £1.3M in its first funding. (Link)

• Vor Systems, a San Francisco, CA-based provider of an AI-enabled transaction platform, raised $3M in Pre-Seed funding. (Link)

• Navigara, a San Francisco, CA-based developer of an engineering performance measurement layer, raised $2.5M in Seed funding. (Link)

• PartsPulse, a Las Vegas, NV-based provider of an AI-powered platform for managing parts businesses, raised $3M in funding. (Link)

• Cyclops, a Miami, FL-based stablecoin and crypto infrastructure platform provider, raised $8M in funding. (Link)

• Shellworks, a London, UK-based bio-materials startup, raised $15M in Series A funding. (Link)

TODAY’S JOB OPPORTUNITIES

💼 Venture capital & startup jobs

All-In-One VC Interview Preparation Guide: With a leading investor group, we have created an all-in-one VC interview preparation guide for aspiring VCs. Don’t miss this. (Access Here)

• Partnerships & Community Manager - Pear VC | USA - Apply Here

• Portfolio Operations Associate - BVP | USA - Apply Here

• Analyst - Iconique Capital | USA - Apply Here

• Network Lead - Catalyst Lap | USA - Apply Here

• Analyst - Infrastructure & Real Assets - Stepstone Group | Australia - Apply Here

• Analyst / Associate - Saints Capital | USA - Apply Here

• Head of Investor Community - Inovexus | Remote - Apply Here

• Associate - Lightbank | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Program Manager - a16z | USA - Apply Here

• VC Platform & Events Manager - 1001 VC | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

👋 Hey, Sahil here — Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

• How to design a seed pitch deck investors actually read (analysis of 50+ Y-Combinator decks that raised $450M+).

• The uncomfortable truth about OpenAI’s moat.

• The marketing framework ClickUp used to scale to a $4B company.

FROM OUR PARTNER - LONG ANGLE

🤝 After financial success, the decisions get harder

Long Angle is a private community for founders and executives who’ve reached real financial scale and want better inputs for what comes next.

Members discuss private market allocations, portfolio construction, tax strategy, and lifestyle tradeoffs with peers who’ve navigated similar transitions.

Private. Peer-led. No solicitation.

Apply for membership →

VENTURE CURATORS’ FINDING

📬 My Favourite Finds

Media Posts:

• Startup legal document pack – essential legal Docs for founders. (Link)

• Churn and retention in the era of AI. (Link)

• Lessons from building financial agents for financial services (Link)

• When to raise vc. (Link)

• The $495B AI opportunity most founders are completely missing (Mark Cuban’s thesis). (Link)

Reports/Articles:

• AI is destroying open source. (Link)

• Nobody knows what programming will look like in two years. (Link)

• The Human-in-the-loop is tired. (Link)

• How Americans view Elon Musk and Mark Zuckerberg. (Link)

• Applied intuition’s path to product-market fit, The $15B founder’s formula for a winning company. (Link)

• How today’s founders make friends and influence people. (Link)

🤝 PARTNERSHIP WITH US

Get your product in front of over 115,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

How to design a seed pitch deck investors actually read (analysis of 50+ Y-Combinator decks that raised $450M+)

Building a pitch deck for your seed round is a crucial step in your startup journey. It’s not just about putting slides together; it’s about crafting a story that resonates with investors and gets them genuinely excited about your vision.

While there are plenty of guides online about building pitch decks, most of them fall short of offering clear, actionable advice. That’s why I’ve taken the time to dive deep into Y Combinator’s resources, watch hours of their videos, and analyse detailed articles to create this comprehensive and practical guide for you.

Why a Great Pitch Deck Matters

Your pitch deck should achieve two things:

• clearly convey the most critical points about your startup and

• make it easy for investors to understand and remember your story.

For seed-stage startups, this often means focusing on simplicity and narrative clarity. Remember, at this stage, most startups don’t have extensive data or years of history to share—and that’s perfectly fine. Your goal is to make your story as compelling and concise as possible.

Structuring Your Seed Round Pitch Deck

Creating a pitch deck doesn’t have to be overly complicated. It’s all about telling a clear, concise, and compelling story that investors can follow easily. Here’s how you can structure your deck:

Title Slide

Start strong with a title slide that sets the tone for your pitch. It should include:

• The name of your company.

• A one-line description of what you do.

Keep it simple and professional. This is your first impression, so make it count.

The Problem

This is where you lay out the real-world issue your startup is solving. A well-defined problem statement is crucial for connecting with your audience.

• Focus on the impact of the problem on real people or businesses.

• Use a statistic, example, or story to make it relatable.

Avoid overloading this slide with too much text or technical jargon. The problem should be clear and easy to understand.

The Solution

Next, explain how your product or service solves the problem you’ve just described. This is your chance to show why your approach is unique and valuable.

• Keep your explanation brief and to the point.

• Highlight the concrete benefits of your solution.

• If needed, use visuals or diagrams, but avoid cluttering the slide.

Traction

Investors love to see traction—it’s proof that your startup is making progress. Even if your numbers are early, show them off in a way that’s easy to understand.

• Use a chart or graph to showcase key metrics like revenue or user growth.

• Add context to explain why these metrics are important.

• Keep it honest and straightforward. Smooth growth curves are rare, and that’s okay.

Your Unique Advantage

What makes your startup special? Use this slide to highlight what sets you apart from competitors.

• Focus on your unique insights, technology, or approach.

• Keep it concise and avoid unnecessary technical details.

Business Model

Your business model is all about showing how you make money. While it doesn’t have to be perfect, it should be clear enough for investors to understand.

• Explain your revenue streams and pricing strategy.

• If you have any early results, include them here.

• Break complex models into simpler components if needed.

Market Opportunity

Investors need to see the potential for scale. Use this slide to showcase the size of your market and the opportunity you’re addressing.

• Provide a high-level overview of your Total Addressable Market (TAM).

• Use a clean, visual representation to convey scale.

A compelling market opportunity slide gives investors confidence in your startup’s long-term potential.

Team

Your team is one of the most critical factors for early-stage investors. Use this slide to show why your team is uniquely qualified to solve the problem.

• Highlight the key strengths and relevant experiences of the founders.

• Keep the focus on the people leading the company rather than advisors or extended team members.

The Ask

Wrap up your pitch with a clear “ask.” Be specific about what you need and what investors can expect in return.

• State the amount you’re raising.

• Explain how the funds will be used (e.g., product development, hiring, marketing).

• Outline where you expect to be within a year and how this funding helps you get there.

Remember - even the best content can fall flat if your slides aren’t designed effectively. According to YC, a great pitch deck follows three fundamental principles: Legibility, Simplicity, and Obviousness.

1. Legibility

• Use large, bold fonts with high contrast.

• Avoid clutter; each slide should be easy to read, even from the back of a room.

• Place key text at the top for better visibility.

2. Simplicity

• Stick to one idea per slide.

• Avoid excessive text, diagrams, or branding.

• Use visuals sparingly and only when they add value.

Example slide:

3. Obviousness

• Test your slides on someone unfamiliar with your startup. If they can’t understand a slide in seconds, simplify it.

• Use captions and labels to make data and visuals explicit.

• Avoid distractions like animations or memes.

Avoiding Common Mistakes

To make your deck truly stand out, steer clear of these common pitfalls:

• Overloading slides with information: Keep your slides clean and focused.

• Using illegible screenshots: Replace them with simplified visuals or bullet points.

• Skipping key slides: Ensure you address the problem, solution, and team clearly.

• Forgetting the big picture: Focus on the opportunity and how your startup fits into it.

By following these guidelines, you’ll create a pitch deck that’s not only visually appealing but also tells a compelling story. Remember: clarity, simplicity, and obviousness are your best friends. Focus on making your key points memorable, and you’ll stand out to investors.

You can also find Y-Combinator startups’ pitch decks here that have collectively raised over $450M.

You can find pitch decks here.

📃 QUICK DIVES

The uncomfortable truth about OpenAI’s moat.

There’s a quiet but fundamental question hanging over OpenAI in 2026: After starting the AI wave, how does it actually win it?

This analysis originally explored OpenAI’s competitive position in depth, and the core argument is simple but uncomfortable: OpenAI has extraordinary technology and scale — but not yet a structural advantage.

• The models are strong.

• The user base is massive.

• The capital ambition is historic.

But none of those, by themselves, create power.

Let’s unpack why.

1. Frontier models are converging — not diverging

At the model layer, competition is tighter than most people admit.

Today, roughly half a dozen organisations are shipping competitive frontier models. They leapfrog each other every few weeks. Benchmarks vary, but the pattern remains consistent: capabilities are clustered closely.

What’s missing is a known mechanism for one player to build an unmatchable lead.

Historically, tech dominance has come from structural advantages:

• Windows had developer lock-in.

• Google Search had data network effects.

• iOS had ecosystem reinforcement.

• Instagram had social graph gravity.

Foundation models, at least today, do not display those properties. They are capital-intensive and technically complex, but not self-reinforcing in the same way.

If the core layer becomes commodity infrastructure sold at marginal cost, differentiation must come from somewhere else.

2. The 900 million user paradox

OpenAI’s clearest advantage is scale: 800-900 million users.

On the surface, that sounds like inevitability. But engagement tells a more nuanced story.

• Only about 5% of users pay.

• Most are weekly active, not daily.

• 80% sent fewer than 1,000 messages in an entire year.

• Even teens use it occasionally, not habitually.

This is broad awareness without deep integration.

That distinction matters. Transformational platforms typically embed into daily workflows. If most users can’t think of something to do with the product on a given day, then either the use-cases aren’t clear, or the experience hasn’t fully landed.

OpenAI calls this a “capability gap”, the gap between what the models can do and what people actually do with them.

From a product standpoint, that’s a sign that product-market fit is still evolving.

And in markets where products are hard to differentiate, early adoption rarely guarantees durable dominance. Competition shifts toward brand and distribution.

We’re already seeing that play out:

• Gemini leverages Google’s distribution.

• Meta AI rides on Meta’s installed base.

• Anthropic competes on model quality but lacks consumer reach.

When products look similar to most users, distribution becomes the weapon.

3. The chatbot problem: thin interface, limited differentiation

There’s a strong analogy here to web browsers in the late 1990s.

A browser is fundamentally an input and output surface. You can improve performance and rendering, but the surface remains thin. Innovation becomes incremental.

Chatbots resemble that structure:

• An input box.

• An output stream.

• A handful of added features.

You can improve the model beneath the surface, but how do you create durable product differentiation at the interface level?

History suggests that when the interface layer is thin, value capture shifts upward. Microsoft won early browser battles, but the enduring value of the web accrued to search engines, marketplaces, and social networks built on top.

If generative AI follows a similar arc, then the next wave of value may not sit in the model itself, but in entirely new experiences built above it.

And here lies the strategic dilemma:

Thousands of startups and every major tech incumbent are racing to invent that second layer. Why should OpenAI be the one to win it?

4. Capex: advantage or table stakes?

Part of OpenAI’s answer has been scale at the infrastructure layer.

Massive capital raises. Gigawatt-scale compute ambitions—aspirations measured in hundreds of billions.

There is logic here. If AI infrastructure behaves like semiconductor fabrication — where fixed costs rise, and only a handful of players can afford to stay at the frontier — then securing a seat at that table could be decisive.

But infrastructure dominance does not automatically translate into ecosystem leverage.

Consider:

• Developers don’t build “TSMC apps.”

• End users don’t know which cloud hosts their SaaS tools.

• API providers are often invisible to the final customer.

Owning the bottom of the stack does not guarantee control over the top.

So the question becomes: Does capex buy more than table stakes?

5. The platform's ambition and the real test

OpenAI has articulated a platform vision: build the full stack, from chips and compute to models, APIs, developer tooling, identity, and integrations. Enable others to create value on top. Become the glue.

That is a compelling story. But real platforms create structural dependency.

A true platform achieves:

• Developers must build on it.

• Users must participate in its ecosystem.

• Switching carries a real cost.

• Alternatives are structurally weaker.

Today, foundation models do not yet exhibit those characteristics. Developers can integrate multiple providers. Enterprises can switch APIs. Users rarely know or care which model powers a feature.

Without friction in switching and without strong network effects, the platform's claim remains aspirational.

Execution matters enormously. Speed matters. Talent matters. But execution is not, in itself, a moat.

What this means - practically

For founders and operators watching this unfold, there are several strategic lessons worth extracting:

• Assume model parity spreads quickly. If you’re building on frontier models, your differentiation likely won’t come from the raw capability layer.

• Distribution still wins in commoditised layers. When products look similar, incumbents with embedded channels have a structural edge.

• The durable layer is usually above the infrastructure. Workflows, proprietary data loops, ecosystem integration, and switching costs create leverage.

The real question facing OpenAI is not whether it can build better models.

It is whether it can build structural reasons for developers, enterprises, and users to depend on its system, even when alternatives exist.

Leading a technological moment is one thing. Owning the ecosystem that follows is another. The difference between the two is power.

The marketing framework ClickUp used to scale to a $4B company.

Should you go all in on SEO? Launch a TikTok? Buy a billboard? Run a cold email campaign? The options are endless, but what works for one startup might not work for you. So how do you choose?

Hustle fund shared ClickUp’s (valued at $4B) marketing framework that they used to scale to $4B company. Here’s how he thinks about marketing.

The ClickUp Marketing Framework

On the X-axis, we have audience size. On the Y-axis, we have lifetime value (LTV). Where you fall on this model determines your marketing approach.

High LTV, Tiny Audience (e.g., Defense Tech, Enterprise SaaS)

• Selling a $500M contract? Google Ads won’t cut it.

• You need business development, a strong network, and a killer pitch deck.

• Sales is 1:1, relationship-driven, and highly personalized.

Low LTV, Huge Audience (e.g., Keychains, Stickers, Fidget Spinners)

• Paid ads don’t make sense here—ad costs eat up profits.

• Growth comes from community, virality, and organic reach.

• Strategies: Product Hunt, Reddit, TikTok, referral loops (think Dropbox, Boomerang).

Higher LTV, Large Audience (e.g., SaaS, Consumer Products, B2B Software)

• With money in the bank, paid media (Facebook, Google Ads) can drive growth.

• Early-stage startups often lack budget for this, so experiment wisely.

• Sponsoring newsletters (like this one 😉) can work if done right.

Mid-Size Audience, Mid-High LTV (e.g., Niche SaaS, B2B Tools, DTC Brands)

• Trade shows, outbound emails, investor intros, and live events become key.

• If you’re selling to 100,000 buyers, scaling outreach matters.

• The goal: personalized + scalable marketing efforts.

Low LTV, Small Audience? Bad business. Stay away.

How Would ClickUp Go-to-Market Today?

ClickUp is a collaboration + work planning tool valued at $4B. In 2022, they served 100,000+ customers, made $150M in revenue, and raised $400M.

From Clicksup growth manager - If I were starting ClickUp today:

Validate Product-Market Fit First

• Talk to users. Why do they love the product?

• Use that data to craft killer messaging.

Find Where Users Hang Out

• Ask: “How did you hear about us?”

• Identify their communities (Reddit, Facebook groups, LinkedIn, etc.)

• Build your own engaged community.

Leverage Organic Growth

• Launch on Product Hunt, Hacker News, and Reddit.

• Invest in SEO—it takes time but pays off.

Scale Once the Foundation Is Strong

• Trade shows, outbound emails, targeted ads come next.

• Nail product/message fit first, then hit the gas.

It all boils down to:

• What’s your LTV?

• How big is your audience?

• How much $ can you spend on marketing today?

Before throwing money at ads, focus on product-market fit, organic traction, and the right messaging. Once that’s in place—light the fire.

TODAY’S JOB OPPORTUNITIES

💼 Venture Capital & Startup Jobs

Most aspiring VCs struggle with interviews, unclear expectations, no structured prep, and generic advice that doesn’t actually help. So we partnered with a leading investor group to build an all-in-one VC Interview Preparation Guide that gives you real clarity and frameworks. (Access Here)

• VC Analyst - Alstin Capital | Germany - Apply Here

• Analyst - Infrastructure & Real Assets - Stepstone Group | Australia - Apply Here

• Internship/Stage - VC Analyst - Sopra Steria | USA - Apply Here

• Social Media Freelancer - Initialised Capital | USA - Apply Here

• Analyst / Associate - Saints Capital | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• VC Platform & Events Manager - 1001 VC | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

• Senior Analyst - Iconique Capital | USA - Apply Here

• Partner - a16z | USA - Apply Here

• Program Manager - gener8tor | USA - Apply Here

• Visiting Associate - First Momentum Venture | Germany - Apply Here

🤝 Partnership With Us

Get your product in front of over 95,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

👋 Hey, Sahil here — Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

• Y-Combinator’s framework for building a successful MVP.

• Raising a seed round 101: Templates, Tips and Strategy to Raise.

• Use this 30-minute GPT prompt stack to validate your startup idea.

FROM OUR PARTNER - ARCHIVE

If you’re running influencer campaigns, Archive is worth a look

If you’re still screenshotting Instagram Stories and tracking creators in spreadsheets, you’re wasting time.

Archive is an AI-first influencer marketing platform that automatically captures what your community posts about you, helps you discover and vet creators at scale, and surfaces conversations your brand should join.

Instead of chasing links and manually building reports, teams use Archive to:

• Track creator content automatically

• Prove ROI to leadership with clean reporting

• Run larger creator programs without hiring a bigger team

1,000+ brands already use it to simplify the messy backend of influencer marketing.

If you’re serious about scaling creator programs properly, this is worth a look.

See how it works - Book a demo →

VENTURE CURATORS’ FINDING

📬 My Favourite Finds

Media Posts:

• Fundraising mistake every founder should avoid. (Link)

• Startup funding funnel you should know. (Link)

• How to write a fundraising email: First email, follow-ups approved by leading VCs? (Link)

• 3 myths about going public that should die (Link)

• Prompt for market research with AI. (Link)

• Steal this mega prompt to create static ads for your brand with Nano Banana. (Link)

Reports/Articles:

• VC Valuation Method Excel Sheet. (Link)

• AI agents are starting to eat SaaS. (Link)

• An AI chatbot that actually knows how to fix things. (Link)

• Investor data room superpack: everything you need to build a VC-ready data room (guide/templates) (Link)

• Transparent leadership beats servant leadership. (Link)

• How smart companies can thrive amid uncertainty. (Link)

• Why big ideas are built in temperate climates. (Link)

🤝 PARTNERSHIP WITH US

Get your product in front of over 103,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

Y-Combinator’s framework for building a successful MVP.

When it comes to building a startup, one of the things that comes to a founder’s mind is creating a Minimum Viable Product (MVP) to test whether their solution to a particular problem is valid or not. However, one of the common mistakes I observe founders making is forgetting about the “Viable” part and just focusing on the product.

This common mistake is observed in 90% of startup founders and the rest 10% of founders who know how to build MVP become startups like Airbnb, twich & stripe etc.

Also, most of the founders mistaken the meaning of MVP that it’s about considering one feature and building a product. Actually, It’s more than that!

So - In this, we will deep dive into -

• Real Meaning of Building a Minimal Viable Product (MVP)

• How did Airbnb, Twitch, and Stripe build their MVPs?

• How did these startups use the Y-Combinator MVP building strategy?

• How can you build the MVP for your startup?

So grab your coffee and let’s deep dive into it!

What is an MVP (Minimum Viable Product)?

A minimum viable product (MVP) is a version of a product with just enough features to be usable by early customers …— Wikipedia

What should be your MVP mindset In the Pre-Launch Stage?

Let’s start with the meme above. It’s called the midwit meme.

Many first-time founders (the guy to the left of the bell curve) launch quickly and iterate (continue launching quickly). This is the same for the expert founder (the guy to the right of the bell curve).

But the guy in the middle is the “smart founder”. Let’s call him Benny. Benny wants to do surveys, raise money, build a team, etc. He wants to do it all. Benny learns after a while he should have been like the “dumb guys”.

As a founder, you want to build an MVP, get it to customers, and learn if it helps them or not. Then iterate.

You only really start learning about your users when you put a product in front of them. Not by doing 1000 surveys, gathering a mailing list, or fundraising for half a year.

The amazing thing, your MVP might not even work how you want it to.

Your goal as an early-stage founder:

• Launch quickly. (MVP)

• Get your first customers.

• Talk To Customers & Get Feedback

• Iterate (Improve the product)

These goals need to be your guiding principle.

Expect that after 3–5 iterations, your MVP would have changed so much. But that’s a good thing. It means you’ve been listening to your customers.

Try and remember that you are not the hero of your customer’s story. You’re not Harry Potter, you’re Dumbledore who gives him the tools to be the hero of his own story. You serve at the pleasure of the customer.

Is an MVP really the way to go?

Many founders want to build a product (like the iPhone) that blows people out of the water at once. That’s a very steep hill to climb though.

The MVP approach is best. You might have a question that -

But What If My MVP Is Rubbish and they Never Want To Talk To Me Again?

Even the biggest startups had this same fear. YC deals with it every time. It’s okay that you feel this fear. But never act on it.

FEAR- False Expectations Appearing Real

Think about it. If one customer doesn’t like your product, does that kill your startup? No. You can reach out to another customer. You can reach out to the same customer after you’ve made the product better.

The more amazing thing is that most people who speak with startups at an early stage are early adopters. They try out products that are in their rubbish phase. They are used to products not working quite as well as they should.

Early users usually have a problem. They are the only ones brave enough to try untested products that could solve the problem. They are usually there for the long haul to give you feedback on how to make the product better.

Fake Steve Jobs -

These types of founders think they know exactly what the customer needs. Let’s call them fake Steves.

In their minds, Steve Jobs built the iPhone and iPod, such amazing products in one go. The mistake they’re making is that’s not true. Many of them remember the 3rd or 4th iteration of these products. The first iPhone had 2G internet, no app store, etc.

You don’t know what the customer needs. The customer alone knows what they need. If you ask them, they will tell you. How you ask them is by showing them your MVP.

Examples of Successful MVPs

1. Airbnb:

This is the first version of AirBnB. They didn’t have a lot of features. If you were an early adopter, here was what you enjoyed.

• No payment system.

• No map view. You couldn’t see where the house was in the city.

• You had to sleep on an airbed. The whole house could not be rented to you.

• It was only for conferences.

2. Twitch:

Twitch was the world’s leading video platform and community for gamers.

• They began as Justin TV with one page.

• Only 1 streamer, Justin.

• Their video was low res.

But they’re now one of the most popular streaming websites in the world.

Do you know what thing they focused on at the pre-launch stage?

Software MVP

• Very fast to build (weeks not months)

• Very limited functionality

• Appeal to a small set of users

They built a product that people loved, not one that everyone loved. It didn’t solve all the problems. Solve problems step by step.

Who Are These Early Adopters? Why Do They Want To Go Through So Much Pain?

“You want to build your product for customers who have their hair on fire.” What product would you want to buy from me?

Maybe a hose, bucket of water, or something that can bring water. Well, all I have is a brick (my MVP). Walking away won’t even cross your mind. You’re desperate, you’ll buy the brick and hit yourself on the head to kill the fire.

The early adopters are the desperate ones. Forget the ones that are not desperate.

Why surveys don’t work.

You might have had a formal business school education. Now you’re thinking let’s do a survey and learn everything the user needs. Then we build it.

That won’t work either. The customer doesn’t know everything about the answer to their problem. They don’t even know everything about the problem itself. If they did, don’t you think they would build the solution on their own or hire people to do it for them?

It’s your job to know. Surveys help you find out your users’ pain but not how to solve it. Only a product can do that.

You give them a product and ask, does this solve your problem? Yes or no and
you go down the scientific method flow chart from there.

How To Build An MVP Quickly!

• Set a specific deadline for completion.

Give yourself a timeframe. It’s easier to move on if you do. If you allow endless time to build, you’ll never stop building the MVP.

• Write down the MVP features.

Don’t keep doing a double take while building. Write everything you want to build down now. Then you won’t have to rethink everything later.

• Cut unnecessary features.

At this point, you will cut many features. Think how important is this to solve the user’s problem.

A startup to solve overseas payments doesn’t need a chat feature in the MVP. Neither does it need APIs connected to every bank in the world. Both are important but can be skipped for the MVP.

Don’t fall in love with your MVP.

Have you heard married couples say something like: this is not the person I married or fell in love with?

Please please please don’t fall in love with your MVP.

It will change so much as you’re iterating.

If you’re in love with it, you won’t want it to change. You’re building an emotional barrier to your growth.

Final Thoughts

“It’s better to have 100 customers that really love your product than 100k that are just ok with it.” — Michael Seibel, Y-Combinator

Those 100 will give you rich feedback that you need to grow. Build for them, and the others will come when you have a shiny product.

All these concepts are covered in the Video by Michael Seibel!

TODAY’S RESOURCE PICK

📬 Startup legal document pack

Messy or missing legal documents can slow down fundraising and create unnecessary risk.

This Startup Legal Document Pack gives you everything you need to incorporate, protect equity, and raise confidently — all in one place.

Inside:

• Formation & incorporation documents (C-Corp, LLC)

• Founder agreements & equity splits

• SAFE notes, term sheets, and cap tables

• Employment, contractor & advisor agreements

• IP protection (NDAs, invention assignments)

• Customer contracts, TOS & privacy policies

Built with startup attorneys and VC teams. Investor-ready. Fully editable.

If you want your legal foundation set up properly before you scale or raise, this pack does the job.

Get access here →

📃 QUICK DIVES

Raising a seed round 101: Templates, Tips and Strategy to Raise.

Founders often ask: how much should I raise, what do I need to prove, and how do I actually close a great seed round? Here’s a practical playbook you can use:

1. Figure out how much to raise

   1. Model for a 24–36 month runway. Median time from seed to Series A is ~23 months (Carta data).

   2. Add a 25% buffer. Unexpected things always happen.

   3. Example: If your burn is $60k/month → $1.8M for 30 months → round up to ~$2.25M with buffer.

   4. Simple spreadsheet - how does this model work out?

2. Prove the basics before you pitch

   1. Proof of commitment: quit your job, show you’re all in.

   2. Proof of work: talk to 30+ SMB buyers or 100+ consumers. Listen for 40%+ saying “wow, when can I get this?”

   3. Proof of insight: either a working prototype with early users or a clear memo/deck that explains your unique thesis.

3. Run a compressed fundraising process

   1. Block 2–3 weeks on your calendar. Speak to as many investors as possible in that window.

1. 1. The compression creates FOMO and scarcity, which pushes decisions.

2. Do your homework on investors

   1. Build a target list with check size, sectors, whether they lead, and reputation with other founders.

1. 1. Use Crunchbase, LinkedIn, and backchannel references to prep. Or you can download the investors’ contact database here.

2. Polish your materials

   1. Keep it minimal: a clear deck or memo, a budget, and a simple model.

   2. Make it visually sharp — good design = signal of quality.

   3. Recommended templates: Sequoia, YC, 40 deck

3. Line up strong intros

   1. Best: portfolio founders of the VC, respected operators, or other active investors.

   2. Avoid: intros from lawyers/service providers or investors not investing in your round.

   3. If you don’t have an intro, send a crisp cold email — low hit rate, but worth trying.

4. Practice your pitch

   1. Write a memo, even if you don’t share it. It sharpens your story.

   2. Rehearse with “B-list” investors first. Iterate before you hit your top targets.

   3. Expect investor questions to carry more weight than your slides. Prepare FAQs.

5. Use angels to build momentum

   1. Open a SAFE and let smaller checks come in early.

   2. This creates social proof that makes bigger VCs move faster.

6. Manage the close carefully

   1. Don’t reveal names of other investors — just describe in general terms.

   2. A round isn’t closed until funds are wired. Push for quick close dates.

   3. Treat verbal commitments as meaningless until you see a signed SAFE/term sheet.

If you take away one thing: seed rounds are less about a perfect business and more about running a tight, well-planned process that creates momentum and scarcity.

Use this 30-minute GPT prompt stack to validate your startup idea.

Too many founders spend weeks polishing ideas the market doesn’t want. You don’t need perfect data to spot a weak bet; you need a fast, honest pass that forces clarity on market reality, customer truth, positioning, and go-to-market.

Below is a tight, copy-paste stack you can run in ChatGPT to pressure-test any idea in under 30 minutes.

How to run the 30-minute sprint:

1. Set the mode (3 minutes)

2. Market reality check (8 minutes)

3. Define the customer (6 minutes)

4. Write value props (4 minutes)

5. Find the wedge vs competitors (6 minutes)

6. Pick GTM mode (3 minutes)

Outcome: a cold recommendation (Go / No-Go) with enough evidence to kill weak ideas quickly or move strong ones to deeper work.

Prompt 1 — set the operating mode, Copy-paste:

You are my marketing strategist, growth specialist, creative director, and positioning expert. For every response:

• Think critically.
• Challenge assumptions.
• Speak like a seasoned operator. When you use an acronym, include the full form in brackets (e.g., CPL (cost per lead)).
• Offer structured feedback, not just answers.
• After each output, add a short “Teach me” paragraph explaining the why.

Commit this business to long-term memory:
“My business is called [INSERT BRAND NAME]. I help [AUDIENCE] solve [CORE PROBLEM] by offering [PRODUCT/SERVICE]. You will build on each new detail as we go.”

My marketing skill level is [BEGINNER/INTERMEDIATE/ADVANCED]. Adapt technical depth to my level but keep the strategist’s viewpoint. If I’m beginner or intermediate, define acronyms briefly in-line.

Do not suggest next prompts unless asked. Always revert to this role for all future replies in this chat.

Prompt 2 — market reality evaluator

Purpose: remove optimism bias, ground the idea in public knowledge and business logic. Copy-paste:

Enter Market Reality Evaluator mode. Do not soften bad news or over-validate weak markets. Use credible public knowledge (2023+), trained inference, and structured business logic.

Evaluate my market and tell me if it’s worth entering.

What I sell:
[One-line product summary — e.g., “A digital course that helps freelancers write faster with GPT.”]

Who I sell to:
[Target audience in plain terms]

What I know (optional edge data):
[Competitor prices, COGS (cost of goods sold), ad costs, performance signals, user data, internal benchmarks]

My estimated pricing:
[If you have it]

Return the following, with concise evidence under each:
• Estimated Total Addressable Market (TAM)
• Category maturity (Emerging / Growth / Plateau / Decline)
• Market saturation level (Low / Medium / High)
• Dominant players (Top 5) with market share/gross revenue/costs/margins (best available estimates)
• Market growth rate (% or trendline)
• Buyer sophistication (Impulse / Solution-aware / Skeptical)
• Purchase frequency (One-off / Repeat / Recurring)
• Pricing ceiling (based on value and competition)
• Viable acquisition channels (SEO, Paid, Organic, Influencer, Partnerships, etc.)
• Estimated CAC ranges per viable channel
• Suggested CLV target for sustainable CAC
• Strategic opportunity mode (Steal / Expand / Defend / Stimulate)
• Overall difficulty score (1–10)
• Clear recommendation: Go or No-Go, with brief, cold reasoning

Bonus (if I provide margin data like “COGS = $22”):
• Profit per sale
• Breakeven CAC
• Minimum conversion rate needed from ads

End with a brief “Teach me” note explaining how you arrived at each call.

Prompt 3 — define the customer (use only the frameworks that fit)

Purpose: capture reality, not fantasy; choose frameworks by model. Copy-paste:

Based on the product above, define the ideal customer using only the most relevant frameworks. Choose from:
• SaaS/service: Jobs to Be Done (JTBD), Awareness Levels (Schwartz), Hormozi first principles
• DTC/brand-led: Brand Archetypes, Psychographics, Empathy Map
• High-ticket B2B: First Principles, Awareness Levels, Moat Thinking
• Content/influencer: Psychographics, Brand Archetypes, Traffic Temperature

Return the sections below. Label a section with the framework used if helpful (e.g., “(JTBD)” or “(Awareness: Problem-aware)”). Be clear, concise, and non-customer-facing.

• Demographics (only if decision-relevant): age/role/income/industry/location
• Psychographics: beliefs, values, aspirations, fears, identity drivers (who they want to become)
• Core frustrations: what they want to stop feeling/doing/struggling with
• Primary goals: outcomes, progress, or emotional relief they actively seek
• Current alternatives: what they use today (including workarounds or “do nothing”)
• Resonant messaging: tone/promises/insights that land; the beliefs or objections we must shift

Avoid repeating product features. Focus entirely on the customer. Close with a short “Teach me” note on why these choices matter.

Prompt 4 — three value propositions under 20 words

Purpose: make the promise crisp and believable. Copy-paste:

Write 3 value propositions under 20 words each:

We help [AUDIENCE] go from [BEFORE STATE] to [AFTER STATE] using [PRODUCT].

Use the modular logic that fits:
• SaaS or B2B services: Hormozi’s value equation (dream outcome vs. friction), April Dunford positioning (Alternative → Unique → Value), Awareness Levels (Problem/Solution-aware)
• DTC/brand-led: Brand Archetypes, Empathy Map + Emotional Ladder, Blair Warren persuasion triggers
• High-ticket B2B/consulting: First Principles (pain → path → belief shift), Andy Raskin narrative (enemy → promised land), Hormozi objections
• Creator/influencer: Tribal logic (“people like us…”), identity shift, StoryBrand clarity (hero meets guide)

Ensure emotional clarity, outcome specificity, and believability. End with a brief “Teach me” note.

Prompt 5 — pick a defensible wedge vs competitors

Purpose: find a clear, ownable position with proof. Copy-paste:

Operate as a Competitive Strategy Analyst.

Competitors (add if you have them):
[Competitor 1], [Competitor 2], [Competitor 3]
Websites:
[URL 1], [URL 2], [URL 3]

Analyse each competitor’s homepage and core product messaging. Summarise:
• Primary value prop (headline + implied promise)
• Likely axis of competition (e.g., speed, price, power, simplicity, brand)
• Who they’re really speaking to (persona insight)

Then propose 3 positioning axes that are unclaimed or under-leveraged. Return a table:

| Axis | Emotional benefit | Who it’s for | How to prove |
| --- | --- | --- | --- |
| [e.g., Simplicity at scale] | [Control, calm, clarity] | [Teams with tool fatigue] | [One dashboard; one prompt; full funnel] |
| [ ] | [ ] | [ ] | [ ] |
| [ ] | [ ] | [ ] | [ ] |

Close with: “Of these 3, lead with [X] because [strategic rationale].”
Bonus: include a sharp one-liner to communicate this wedge. End with a “Teach me” note.

Prompt 6 — choose the GTM mode

Purpose: align go-to-market with structure, not vibes. Copy-paste:

Enter GTM Mode Selector. Use prior outputs (market, pricing, positioning, TAM, growth, CAC, wedge).

My product:
[Insert if single product]

Answer:
1) Most viable GTM mode: Steal, Expand, Defend, or Stimulate
2) Strategic rationale (structural, not tactical): why this mode fits margins, market shape, and model
3) Optimise for:
   • Speed vs margin?
   • Awareness vs conversion?
   • Breadth vs depth of messaging?
4) Modes I should not pursue—and why
5) GTM difficulty (1–10) with blind spots

Do not recommend specific tactics yet. End with a “Teach me” note.

Decision rules (kill or keep)

• Kill if growth is flat/declining, saturation is high, or the wedge is trivial.

• Kill if CLV (customer lifetime value) can’t support CAC (customer acquisition cost) at 3:1 within 6–12 months.

• Kill if pricing ceiling leaves unsustainably low margins after COGS (cost of goods sold) + discounts.

• Proceed if there’s a provable wedge, at least one efficient channel with viable CAC, and a path to repeatable revenue.

Good output looks like

• Crisp numbers with sources or reasoned estimates

• Competitor reads that reveal the true audience

• Value props sceptics would believe

• A wedge provable in product/experience

• A defendable Go / No-Go call

Pro tips

• Bring one edge metric (COGS, CPC, or baseline conversion) to anchor the math

• Ask for ranges/sensitivity if early (e.g., “show outcomes if CPC is +30%”)

• Save the chat—compounds as you add more data

TODAY’S JOB OPPORTUNITIES

💼 Venture Capital & Startup Jobs

All-In-One VC Interview Preparation Guide: With a leading investor group, we have created an all-in-one VC interview preparation guide for aspiring VCs. Don’t miss this. (Access Here)

• Ventures Associate - Plug and Play Tech Centre | USA - Apply Here

• Visiting Analyst - Redalpine | UK - Apply Here

• Operations Associate - Initialised Capital | USA - Apply Here

• Associate - Primary | USA - Apply Here

• Investor Relations - SC Founders Fund | India - Apply Here

• VC Analyst - Alstin Capital | Germany - Apply Here

• Analyst - Infrastructure & Real Assets - Stepstone Group | Australia - Apply Here

• Social Media Freelancer - Initialised Capital | USA - Apply Here

• Analyst / Associate - Saints Capital | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• VC Platform & Events Manager - 1001 VC | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

🤝 Partnership With Us

Get your product in front of over 95,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

👋 Hey, Sahil here — Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

Big idea + report of the week :

• Is AI quietly eliminating customer support jobs?

• Should LPs back small VC funds for upside or large funds for stability?

Frameworks & insightful posts :

• Anthropic Research: Where AI agents are actually being deployed and where the real opportunity lies.

• Mark Cuban’s AI Thesis: The $495B AI opportunity that most founder missing.

• The FBI negotiation tactic founders should use in every pricing call.

FROM OUR PARTNER - FRAMER

🤝 Launch fast. Design beautifully. Build your startup on Framer — free for your first year.

First impressions matter. With Framer, early-stage founders can launch a beautiful, production-ready site in hours. No dev team, no hassle. Join hundreds of YC-backed startups that launched here and never looked back.

Pre-seed and seed-stage startups new to Framer can enjoy:

• One year free: Save $360 with a full year of Framer Pro, free for early-stage startups.

• No code, no delays: Launch a polished site in hours, not weeks, without hiring developers.

• Built to grow: Scale your site from MVP to full product with CMS, analytics, and AI localisation.

Join YC-backed founders: Hundreds of top startups are already building on Framer.

Apply to claim your free year →

🤝 PARTNERSHIP WITH US

Get your product in front of over 103,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

START WITH

🧠 Big idea + report of the week

Is AI quietly eliminating customer support jobs?

For years, AI replacing jobs felt theoretical. Now we’re starting to see clean data.

Jason Lemkin recently shared hiring numbers from Pave covering 386,500 new hires, and the shift in Customer Support is dramatic. In Q4 2023, support roles accounted for 8.30% of new hires. By Q3 2025, that number fell to 2.88%.

That’s a roughly 65% decline in two years. And almost half of that drop happened in just the last three quarters.

This isn’t gradual automation. It’s an accelerating adoption curve.

Lemkin also shared a concrete operating example:

one company went from 20+ support employees to just 3 humans supported by AI agents, while revenue swung from -19% to +47% YoY during the transition. AI now handles the majority of inbound volume.

But the real story isn’t that support is disappearing. It’s being redesigned.

What’s actually happening inside companies:

• AI is now Tier 1 support.

Basic ticket routing, FAQs, account updates, policy explanations, these are increasingly handled by AI at 40–60%+ deflection rates without degrading quality.

• Entry-level ticket roles are shrinking.

The classic $50K generalist support rep high-volume, repetitive tasks, is fading.

• Higher-skilled human roles are expanding.

What remains requires judgment: technical escalations, implementation, complex integrations, revenue-sensitive accounts. These roles often pay $100K+ and look more like hybrid Customer Success positions.

Support is merging upward into CS.

When AI handles half the tickets, the remaining human work becomes proactive:

• onboarding

• expansion conversations

• churn prevention

• relationship building

That’s not elimination. That’s elevation. The more interesting implication is strategic.

Support is simply the easiest category to automate first:

• high volume

• structured queries

• well-documented answers

• low tolerance for long response times

It’s the cleanest training ground for AI agents. And that makes it a leading indicator.

The same pattern is likely to come for:

• SDR roles handling repetitive outbound

• Parts of marketing ops and content workflows

• QA and testing functions

• Mid-level product ops work

Any function where AI can reliably handle 40-60% of structured volume without degrading output quality becomes vulnerable to redesign.

For founders, the takeaway isn’t “cut headcount.” It’s this:

If AI can handle the repetitive layer of your function, redesign the human layer above it.

Companies that simply reduce costs miss the upside. The real leverage shows up when:

• Response times collapse

• Coverage becomes 24/7

• Humans shift to revenue-impacting work

• Cost per ticket drops while CSAT improves

The 65% drop in support hiring doesn’t mean companies care less about customers. It means the best ones have figured out a new operating model.

The window to adapt is shrinking. Support was first because it was easiest. It won’t be the last.

Should LPs back small VC funds for upside or large funds for stability?

For years, the default narrative has been simple: small funds outperform.

But when you look at the full distribution of outcomes, the story is more nuanced and more structural.

John Rikhtegar recently analysed 760 North American VC funds (vintages 2000–2020) using PitchBook Net TVPI data and split them into five fund-size quintiles. Instead of focusing only on averages, he plotted the entire distribution of returns.

The pattern was clear.

• Small funds stretch the return curve.

• Large funds compress it.

• The middle is where things get uncomfortable.

Here’s what the dispersion shows.

Quintile 1 ($0–$40M funds):

• 90th percentile ≈ 5.6x

• Median ≈ 1.82x

• 25th percentile ≈ 0.99x

Wide spread. Real asymmetry. Real volatility.

Quintile 5 ($450M+ funds):

• 90th percentile ≈ 2.83x

• Median ≈ 1.60x

• 25th percentile ≈ 1.18x

Tighter clustering. Fewer blowouts, in both directions.

Interestingly, both the smallest and the largest funds outperformed the three middle quintiles across top, median, and bottom quartile marks. The middle 60% of funds showed less asymmetry and less consistency, the least attractive combination.

So what’s structurally happening?

Small funds benefit from ownership math.

When a $25M fund owns 10–15% of a breakout company, even a “modest” $500M–$1B exit can move the entire vehicle meaningfully. The fund doesn’t need a $20B outcome to generate a 3–5x net return. Ownership concentration turns realistic wins into fund-level impact.

That’s the asymmetry LPs are buying.

But that asymmetry comes with volatility. The dispersion is wide. Bottom quartile outcomes are materially weaker.

Large funds operate under a different design constraint.

At $1B+, a single $1B exit barely moves the needle. These funds need multi-billion-dollar outcomes or repeated solid wins to drive returns. As a result, their distribution tightens. They deliver steadier profiles and often meaningful absolute dollars returned to LPs, even if multiples are lower.

For many institutions, that matters.

One large GP relationship can return substantial capital without managing dozens of small fund exposures. Operational simplicity and capital recycling predictability are part of the appeal.

The middle segment is where it gets tricky.

Across the three middle quintiles ($40M–$450M), 3x net returns appear mostly in the top decile. That makes top-decile selection increasingly critical. If you’re not picking the best managers in that band, the return math struggles — especially when LPs can allocate to shorter-duration private credit or liquid public markets.

A few practical implications emerge.

• If an LP wants asymmetry and can tolerate dispersion, small funds structurally offer it.

• If an LP prioritises steadier capital return and relationship scale, large funds fit better.

• If allocating to mid-sized funds, top-decile selection becomes non-negotiable.

Fund size isn’t just a branding choice. It shapes the return curve itself. The dispersion isn’t random. It’s structural, tied to ownership, check size, and how much a single win can move the vehicle.

The real question isn’t “small or large?” It’s whether the fund size aligns with the kind of return profile you’re underwriting.

FROM OUR PARTNER - SEMRUSH

📈 See what drives your competitors’ growth — before they do

Every winning strategy starts with the right data. With Semrush’s Traffic & Market Toolkit, you can uncover exactly where your competitors’ traffic comes from — search, social, or even AI referrals.

Track their top pages, monitor emerging trends, and find the untapped channels fueling their growth. Whether you’re a marketer, agency, or enterprise, you’ll get daily and weekly insights to outpace rivals and lead your market with confidence. Trusted by 10M+ marketers worldwide.

Try the Traffic & Market Toolkit free →

SOMETHING MORE

🧩 Frameworks & insightful posts

Anthropic Research: Where AI agents are actually being deployed and where the real opportunity lies.

There’s a lot of noise around AI agents. But if you strip away the hype and look at real-world deployment data, a clearer picture emerges: agents are here, they’re gaining autonomy, and they’re concentrated in very specific domains, for now.

Anthropic analysed millions of real human-agent interactions across Claude Code and its public API. Instead of speculating about the future, they studied how agents are being used in practice.

And the results are revealing.

First: Autonomy Is Increasing, But Carefully

Most Claude Code sessions are still short. The median turn lasts around 45 seconds.

But the extreme tail tells a different story. In just three months, the 99.9th percentile turn duration nearly doubled, from under 25 minutes to over 45 minutes. Importantly, this growth was smooth across model releases. That suggests the shift isn’t purely about new model capability. It’s about usage patterns.

Users are granted more autonomy. As people gain experience:

• New users manually approve each action.

• By 750 sessions, over 40% of sessions are fully auto-approved.

• Interrupt rates rise from 5% (new users) to 9% (experienced users).

This indicates something important: oversight strategy evolves. Users move from micro-approval to delegation + intervention. They let agents run, but step in when needed.

That behavioural shift is foundational for domain expansion.

Because before agents move into higher-stakes environments, humans need to learn how to supervise them efficiently.

Where AI agents are actually being deployed today

Right now, deployment is highly concentrated.

1. Software Engineering (~50% of agentic activity)

Software engineering dominates agent usage. Nearly half of all tool calls on Anthropic’s public API fall into this domain.

This includes:

• Writing and modifying code

• Deploying bug fixes

• Running tests

• Monitoring system health

• Managing development workflows

Why engineering first?

Because:

• Outputs are testable.

• Mistakes are usually reversible.

• Developers understand the systems.

• Feedback loops are tight.

Engineering is the ideal sandbox for autonomy.

This is where agents are becoming operational infrastructure, not just assistants.

2. Emerging Domains (Still Small, But Growing)

Outside of engineering, we see early experimentation in:

• Healthcare (retrieving medical records, workflow support)

• Finance (automated transactions, reporting)

• Cybersecurity (red team simulations, vulnerability scanning)

• Customer support (triaging tickets, drafting responses)

• Sales and business intelligence workflows

These are not yet dominant. But they are strategically important.

They represent the transition from “tool for developers” to “operational layer for businesses.”

And here’s the key: most of these deployments are still low-risk.

Anthropic found:

• 73% of tool calls appear to have a human in the loop.

• 80% involve some safeguard (permission systems, approval gates).

• Only 0.8% appear irreversible (like sending a live customer email).

That means we’re early. Very early.

Agents are being tested in higher-stakes domains, such as security systems, financial transactions, and production deployments, but not yet at a massive scale.

The frontier exists. It just hasn’t fully expanded.

The Real Opportunity Isn’t in Coding, It’s in Domain Expansion

The engineering domain is saturated with early adopters. The next major wave will come from vertical expansion.

Think about what happens when agents move beyond code:

• In healthcare → patient workflow automation, claims processing, and triage support.

• In finance → compliance checks, reconciliation, and autonomous trading logic.

• In cybersecurity → real-time threat response.

• In operations → procurement, vendor management, reporting pipelines.

• In customer service → full resolution systems, not just drafting replies.

Each of these domains increases both risk and autonomy. And that’s where the opportunity lies.

Right now, most businesses are experimenting at the “low-risk, reversible” layer.

The gap sits between: “I used a chatbot to draft something” and “AI autonomously runs this workflow with monitoring.”

That gap is enormous and largely unsolved.

Why Monitoring Infrastructure Becomes the Next Layer

Anthropic emphasises something subtle but critical: post-deployment monitoring will become essential as autonomy expands.

This is where most founders are underestimating the opportunity.

As agents move into higher-stakes environments, businesses will need:

• Autonomy dashboards

• Risk scoring systems

• Intervention triggers

• Audit logs

• Escalation frameworks

• Clarification training (teaching models when to stop)

Interestingly, Claude Code already pauses for clarification more often than humans interrupt it on complex tasks. That suggests that self-awareness and uncertainty recognition are becoming safety levers.

But scaling this across industries requires tooling, not just better models.

The next wave of AI startups may not be:

• Bigger models

• More tokens

• Faster inference

It may be:

• Agent orchestration platforms

• Risk-layer infrastructure

• Monitoring + intervention systems

• Verticalized deployment frameworks

So right now, AI agents are heavily concentrated in software engineering because that’s where:

• Errors are reversible

• Oversight is natural

• Technical users exist

But as trust and autonomy increase, the frontier expands.

And that expansion will likely follow this path:

1. Low-risk engineering automation

2. Internal operational workflows

3. Customer-facing systems with guardrails

4. Financial and compliance-heavy domains

5. High-risk autonomous decision systems

Each step increases economic value.

The largest opportunity does not sit in the current 50% of tool calls. It sits in the 50% that hasn’t yet scaled.

If you’re building in AI agents, the strategic question isn’t “How autonomous can we make this?”

It’s:

• Which domain can tolerate autonomy next?

• How do we build oversight infrastructure for it?

• How do we make risk visible and manageable?

• How do we train both humans and models to share control?

Because the data suggests we are not at peak autonomy. We are at an early controlled experimentation.

And the companies that solve domain-specific autonomy safely, visibly, and measurably, will capture the next phase of agent expansion.

Mark Cuban’s AI Thesis: The $495B AI opportunity that most founder missing.

Most founders are chasing the wrong layer of the AI stack.

They think the opportunity is in building the next foundation model, the next ChatGPT, or the next AI-native SaaS unicorn. But when Mark Cuban calls AI the biggest job creation wave since the internet, he’s not talking about model labs.

He’s talking about services. And when you look at the numbers, the thesis becomes hard to ignore.

There are 33 million businesses in the United States that need AI integration. Yet only 8.8% actually have AI in production. The current AI services market sits around $11B, but the projected opportunity ranges between $165B and $495B. Even a modest 10% buy-in implies nearly half a trillion dollars in addressable spend.

That’s not incremental growth. That’s a structural shift.

The real gap isn’t between “no AI” and “ChatGPT access.” It’s between experimentation and operational transformation.

Most businesses today fall into the illusion of adoption. You’ll hear that 68% of companies use AI. But in practice, that often means:

• Someone in marketing prompts ChatGPT for email drafts

• A manager uses AI to summarise documents

• Teams experiment with generic copilots

That’s surface-level usage. It doesn’t touch the core of how the business runs.

The distance between “I used a chatbot” and “AI runs parts of our operations” is massive. And that distance is where the real opportunity sits.

What businesses actually need isn’t another SaaS subscription. They need someone who can walk into their company, understand how work flows, and design AI systems that execute real tasks.

That requires a different skillset than building models from scratch. The opportunity lies in agent orchestration.

Most companies don’t struggle with access to AI. They struggle with integration. No tool, on its own, can:

• Map messy workflows

• Identify repetitive decision loops

• Connect CRM, support, finance, and internal tools

• Design escalation paths

• Monitor performance and reliability

• Continuously improve outputs

This is not a feature problem. It’s a systems problem.

Agent orchestration is about turning AI from a chatbot into an operational layer. Instead of “AI helping,” it becomes “AI doing.”

That might mean:

• Support tickets auto-resolved with guardrails

• Sales leads are pre-qualified before a human touches them

• Internal reporting is generated autonomously

• Contract review is automated end-to-end

• Repetitive back-office tasks delegated to agents

When you frame it that way, the job creation thesis becomes clearer. Every one of those 33 million businesses needs someone who can:

• Understand business incentives

• Translate processes into logic

• Deploy agentic workflows

• Build safeguards

• Iterate based on results

And most of those people will not be pure engineers.

The next high-demand profile in tech isn’t necessarily the person training foundation models. It’s the translator, someone who understands both business operations and agent systems. Former operators, product leaders, consultants, technical generalists, people who can bridge workflows and orchestration.

That’s why betting on agent orchestration makes sense. The foundational models are largely built. The APIs exist. The tools are improving weekly. What’s missing is deployment at scale.

If you’re building in AI right now, the real strategic question is not whether your product uses the best model. It’s whether you are closing the gap between curiosity and execution.

Because the $11B market today isn’t the ceiling. It’s the starting point. The expansion to $165B-$495B won’t come from better demos. It will come from businesses that move from “AI experiment” to “AI runs part of our company.”

That transition requires people who can design, implement, and operate agent systems inside real-world constraints.

And those people are about to become some of the most in-demand operators in tech. Not because they built AI. But because they made it work.

The FBI negotiation tactic founders should use in every pricing call.

Most founders negotiate the wrong way.

When a vendor raises prices, an enterprise customer pushes back, or a partner squeezes terms, we default to logic. We explain budgets. We justify usage. We ask for a “win-win.” We try to meet halfway.

And that’s usually a losing move.

A Reddit post recently broke down a tactic inspired by Chris Voss, former FBI international kidnapping negotiator and author of Never Split the Difference.

His core idea flips traditional business logic on its head: “Let’s meet halfway” and “win-win” often weaken your leverage. The real move is tactical empathy combined with no-oriented, calibrated questions.

The surprising part? AI can execute this framework better than most humans.

Here’s how founders can use it -

The core shift: stop trying to get to “yes”

In most negotiations, we push for agreement. We try to get the other side to say yes.

But Voss argues that “yes” is often meaningless. It can be a polite brush-off. A way to end the conversation. What you actually want to hear is: “That’s right.” That signals you’ve understood their position so clearly that they feel seen.

Let’s take a SaaS renewal scenario: A vendor attempted to raise pricing by 20%. Instead of writing a long, polite email asking for a discount with budget logic and apologies, the person used ChatGPT, but with strict constraints.

The key was telling AI what NOT to do:

• No logic.

• No budget arguments.

• No apologising.

• No asking directly for a discount.

Instead, the AI was instructed to use labelling, phrases like “It seems like…” or “It sounds like…” followed by what Voss calls a calibrated question. A how-or-what question that forces the other side to solve your problem.

The output was almost uncomfortable in its simplicity:

“It seems like you’re under a lot of pressure to increase revenue this quarter. It sounds like you feel that our current pricing is unfair to the value you provide. How am I supposed to agree to a 20% increase when our usage has remained flat?”

That was the entire email.

No pleasantries. No “hope this finds you well.” No emotional padding. Just psychological judo.

The result? The vendor dropped the increase entirely and locked in the old rate for two years.

Why this works (especially on sales reps)

Sales teams are trained to handle objections. They have scripts ready for:

• “It’s too expensive.”

• “I need to check with my boss.”

• “Can you do better on price?”

But they don’t have scripts for: “How am I supposed to do that?”

That kind of calibrated question bypasses their defensive scripts. It shifts the conversation from defending price to solving a problem.

Instead of negotiating against you, they begin negotiating with themselves.

That’s the power move.

How founders can apply this in real scenarios

This isn’t just for vendor renewals. It applies across the founder's life:

When raising capital, Instead of pushing valuation logic, you might say:

“It seems like you’re concerned about the durability of our growth. What would need to be true for this to feel like a must-invest?”

When negotiating enterprise contracts:

“It sounds like you’re trying to minimize risk on your side. How are we supposed to commit engineering resources without clearer deployment timelines?”

When a partner pushes unfavourable terms:

“It seems like you’re trying to optimize for flexibility. How does that align with building a long-term strategic relationship?”

Notice the pattern:

• Start with empathy (“It seems like…”)

• Reflect their likely pressure or concern

• End with a how/what question that forces them to engage

The AI layer founders miss

ChatGPT defaults to being helpful and professional. Which means it tends to soften language, over-explain, and write long rational justifications, exactly the kind of negotiation emails that get ignored.

The trick isn’t just asking AI to write the email.

It’s telling AI what NOT to do:

• Don’t argue logically.

• Don’t justify.

• Don’t plead.

• Don’t ask directly for the concession.

Strip the logic. Add empathy. Ask the calibrated question.

You’re not trying to overpower the other side. You’re trying to create a moment where they pause and think: “That’s fair. How do we solve this?”

That’s when you’ve shifted control.

For founders, this matters more than it seems

Founders negotiate constantly:

• Pricing with vendors

• Terms with investors

• Discounts with customers

• Equity splits with hires

• Strategic partnerships

Most of these negotiations fail not because of numbers, but because of positioning.

The founders who win aren’t the most aggressive. They’re the ones who frame the conversation so the other party feels understood and then must justify their own position.

The question isn’t whether this tactic works.

The question is whether you’re disciplined enough to stop explaining and start asking: How am I supposed to do that?

You can use this prompt -

Role: Act as Chris Voss (FBI Negotiator).

Task: Rewrite the negotiation email with extreme tactical empathy.

Specific Instructions:

- The Label (Pressure): Start by hypothesizing that the rep is being pushed by management to hit quarterly revenue targets. Label that emotion.

- The Label (Value): validate that they probably think their software is worth more than I'm paying.

The Reality: Mention that my usage is flat.

The Calibrated Question: Combine the price increase and the flat usage into a "How" question that makes the increase impossible to accept without saying "no."

Style constraints:

- Remove all fluff. No greetings. No sign-offs.
- Do not be aggressive, be confused but deferential.

EXPLORE MORE

💡 Reports, Articles and a few interesting stuffs

• Fintech VC valuations race ahead in Europe. (Link)

• The $135B agent effect: Why VCs aren’t pulling back from SaaS. (Link)

• From 43 failed projects to an OpenAI acquisition in 90 days. (Link)

• Check out these prompts that Anthropic, Google and OpenAI researchers use on a daily. (Link)

• How Anthropic’s marketing team uses Claude code. (Link)

• Who’s left in the 10x ARR club? The incredible shrinking elite of public B2B companies. (Link)

• Anthropic economic index report: economic primitives. (Link)

NEWS RECAP

🗞️ This week in startups & VC

New In VC

• First Cressey Ventures, a Nashville, Tenn.-based healthcare venture capital firm, closed Fund IV at $425m. (Link)

• Dragonfly Capital, an Oakland, CA-based venture capital firm focused on backing blockchain and fintech startups, raised $650m for its fourth fund. (Link)

• Battery Ventures, a global, technology-focused investment firm, closed a $3.25 billion fund. (Link)

• Thrive Capital, a New York-based venture firm founded by Josh Kushner, has raised $10B for Thrive X, its largest fund to date. (Link)

• Kindred Ventures, a San Francisco, CA-based venture capital firm, raised approx. $227m across two funds. (Link)

New Startup Deals

• Fireplace, a Hong Kong-based provider of a professional trading terminal for prediction markets, raised $1.5M in Pre-Seed funding. (Link)

• ARCYN Defense, a Washington, DC-based defense technology company, raised an undisclosed amount in Seed funding. (Link)

• The Compression Company, a San Francisco, CA-based provider of a data compression platform, raised $3.4M in Pre-Seed funding. (Link)

• Maestro AI, a Parkland, FL-based provider of a vertical artificial intelligence (AI)-powered platform built for mortgage origination, raised $1.2M in Pre-Seed. (Link)

• The Bland Company, a London, UK-based developer of high-performance plant-based proteins, raised $2.67M in pre-seed funding. (Link)

• Project Omega, a Washington, D.C.-based developer of advanced nuclear recycling technology, raised $12m in Seed funding. (Link)

TODAY’S JOB OPPORTUNITIES

💼 Venture capital & startup jobs

All-In-One VC Interview Preparation Guide: With a leading investor group, we have created an all-in-one VC interview preparation guide for aspiring VCs. Don’t miss this. (Access Here)

• Ventures Associate - Plug and Play Tech Centre | USA - Apply Here

• Visiting Analyst - Redalpine | UK - Apply Here

• Operations Associate - Initialised Capital | USA - Apply Here

• Associate - Primary | USA - Apply Here

• Investor Relations - SC Founders Fund | India - Apply Here

• VC Analyst - Alstin Capital | Germany - Apply Here

• Analyst - Infrastructure & Real Assets - Stepstone Group | Australia - Apply Here

• Social Media Freelancer - Initialised Capital | USA - Apply Here

• Analyst / Associate - Saints Capital | USA - Apply Here

• Investment Analyst Intern - NAV Capital | Dubai - Apply Here

• PE & VC Partner Manager - Dealhub | UK - Apply Here

• VC Platform & Events Manager - 1001 VC | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

• MBA Venture Capital Intern - Cerity Partners | USA - Apply Here

PARTNERSHIP WITH US

Get your product in front of over 102,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Refer a friend

Subscribe now

👋 Hey, Sahil here — Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

• Annoying questions from VC & how to answer them.

• How to price your AI product in a post-SaaS world?

• Is PE no longer your exit plan in 2026?

FROM OUR PARTNER - NUME