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👋 Hey, here’s today at a glance -

Big idea + report of the week :

• Why are LPs fighting for access to the most valuable AI deals?

• Did OpenAI win attention while Anthropic built a better company?

Frameworks & insightful posts :

• What every founder needs to understand about AI economics?

• Where AI seed investors are most likely to find outliers.

• What’s driving the surprising decline in founder dilution?

START WITH

🧠 Big idea + report of the week

Why are LPs fighting for access to the most valuable AI deals?

For years, co-investments were treated as a useful side benefit of backing top venture funds.

Today, they have become one of the most important battlegrounds in private markets.

A recent PitchBook analysis by Jessica Hamlin explains why. As companies like OpenAI, Anthropic, and xAI race toward valuations that could eventually justify trillion-dollar outcomes, limited partners are fighting aggressively for direct exposure alongside their GPs.

The logic is simple: if a handful of frontier AI companies end up becoming the defining businesses of this decade, even a relatively small ownership stake could materially improve an LP’s overall portfolio returns.

That possibility is creating a new divide between institutions with the relationships, resources, and underwriting capabilities to access these deals and those that do not.

The biggest AI companies are attracting unprecedented amounts of capital

The scale of valuation growth in late-stage AI is unlike anything venture investors have seen in recent memory.

According to PitchBook, U.S.-based AI and machine learning startups raising Series D rounds and beyond reached a median pre-money valuation of $4.7 billion in Q1 2026. That is nearly four times higher than comparable non-AI companies and represents a 447.8% increase from 2024.

These numbers explain why LPs are so eager to participate.

If companies like Anthropic and OpenAI eventually grow into multi-trillion-dollar public businesses, direct co-investments made today could become some of the highest-returning allocations in an institution’s portfolio.

The market has shifted from capital scarcity to access scarcity

One of the most interesting insights in the report is how quickly the fundraising environment has changed. In early 2024, startups were competing intensely for capital. By 2026, the opposite is true.

PitchBook’s data shows that the demand-to-supply ratio for U.S. AI venture capital fell from 4.5x in Q1 2024 to just 0.9x in Q1 2026.

In practical terms, investors now have more money available than high-quality AI startups need to raise. That means the challenge is no longer finding capital.

The challenge is gaining access to the very small number of companies attracting extraordinary investor interest.

Fundraising cycles are accelerating

AI companies are also raising capital at a much faster pace than traditional startups. The median time between rounds for AI startups fell to 1.3 years in Q1 2026, compared with 1.9 years for non-AI companies.

This compressed timeline favors LPs with sophisticated co-investment programs that can evaluate opportunities quickly and commit large amounts of capital on short notice.

For smaller institutions, this creates a structural disadvantage.

Without dedicated internal teams and strong GP relationships, it becomes much harder to participate in the most competitive transactions.

Why co-investments matter so much

Co-investments are particularly attractive because they often come with little or no additional management fees and carried interest.

That allows LPs to increase exposure to their highest-conviction opportunities while reducing the overall fee burden on their portfolios.

A survey from Coller Capital found that:

• 44% of LPs said co-investments are becoming more important in their private markets strategy.

• Nearly 20% said they still do not have access to attractive opportunities.

In a market where a small number of AI companies may drive outsized returns, access to co-investments is increasingly becoming a competitive necessity rather than a nice-to-have.

The largest institutions are already positioned

Many of the world’s most sophisticated allocators have moved aggressively to secure exposure.

Examples include:

• Qatar Investment Authority participating in xAI’s $20 billion financing.

• GIC helping lead Anthropic’s $30 billion round.

• UC Investments joining OpenAI’s $122 billion financing.

UC Investments is particularly notable. Over the past decade, it reduced its manager relationships from 280 to just 28, reflecting a deliberate strategy of concentrating capital and increasing direct investment activity.

So - the AI boom is reshaping how LPs think about portfolio construction.

Historically, superior returns came primarily from selecting the right venture managers.

Increasingly, the greatest source of alpha may come from gaining direct exposure to the small number of companies building the foundational infrastructure of the AI economy.

That is why competition for OpenAI and Anthropic allocations has become so intense.

If these companies ultimately fulfill their potential, access to a single co-investment could meaningfully influence an institution’s long-term returns.

And for LPs without the necessary relationships and internal capabilities, the most important investment opportunity of the decade may remain frustratingly out of reach.

Did OpenAI win attention while Anthropic built a better company?

For most people, OpenAI still feels like the default winner of the AI era. It has the consumer brand, ChatGPT, massive usage, and the kind of mindshare very few startups ever reach.

But new PitchBook and Financial Times reporting shows the race is no longer that simple. Anthropic has reportedly signed a term sheet that would value it at $930B, ahead of OpenAI’s $852B valuation for the first time.

The bigger story is not just the valuation flip. It is what investors are choosing to reward.

OpenAI still has unmatched consumer reach, with reported weekly active users near 900M. But much of that usage remains free. Anthropic, on the other hand, is gaining ground inside enterprises, where contracts are stickier and margins matter more.

Ramp’s AI Index showed 34.4% of U.S. businesses using Anthropic versus 32.3% using OpenAI, a major shift from a year ago when Anthropic was below 10%.

That enterprise momentum is why investors may be underwriting Anthropic differently.

Claude Code has helped pull developers into workflows that can expand into broader company-wide contracts. If those accounts retain and expand, Anthropic starts to look less like a chatbot company and more like a high-quality enterprise software platform.

There’s also a capital efficiency angle. PitchBook notes that Anthropic’s expected $930B valuation comes on roughly $103B of equity raised, compared with OpenAI reaching $852B on about $173B.

That implies investors are paying more per equity dollar deployed into Anthropic, because they believe the path to public-market quality may be cleaner.

A few things seem to be driving that confidence:

• Strong enterprise adoption

• Reported gross margins above 70%

• Less dependence on a single strategic partner

• Cleaner corporate structure compared with OpenAI

• Developer-led adoption through Claude Code

The round itself also shows how concentrated late-stage AI investing has become. Greenoaks, Altimeter, Dragoneer, and Sequoia are reportedly leading the deal, each expected to commit at least $2B.

For Greenoaks and Altimeter, that would represent more than 10% of their AUM - a huge concentration bet even by growth-stage standards.

This is where the story gets bigger than Anthropic vs. OpenAI.

The AI frontier labs are now being valued like future public-market anchors. Whoever goes public first at a higher multiple could set the benchmark for the entire category.

That will influence how investors price model labs, AI infrastructure, enterprise copilots, and every company claiming to sit near the AI value chain.

For founders: attention is not the same as enterprise value. OpenAI won the first wave of consumer mindshare, but Anthropic is showing that enterprise adoption, margins, retention, and governance can matter just as much - maybe more when investors start thinking about IPO readiness.

The AI race is no longer just about who has the most users. It is about who can become the highest-quality business.

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SOMETHING MORE

🧩 Frameworks & insightful posts

What every founder needs to understand about AI economics?

For the past two decades, software has been one of the best businesses in the world.

Once you built the product, the cost of serving each additional customer was tiny. That’s what made SaaS so attractive. Companies could spend heavily upfront on engineering and go-to-market, then enjoy exceptionally high margins as they scale.

Domen Jemec recently shared an analysis explaining why AI may fundamentally change this equation.

His core argument is: AI makes software cheaper to build, but potentially much more expensive to operate.

That distinction matters more than most founders realise.

Why traditional SaaS became such a powerful business model

In classic SaaS, most of the cost is fixed.

You invest millions of dollars building the product, hiring engineers, and acquiring initial customers. But once the software is live, each additional user costs very little to support.

That creates a beautiful economic model:

• Revenue grows linearly as customers increase

• Cost per user declines over time

• Gross margins often exceed 70–80%

• Mature companies can reach 20–30% net margins

This dynamic is what made strategies like blitzscaling possible.

A company could lose money early, knowing that each new customer would become increasingly profitable as scale improved.

AI flips the cost structure

AI changes this because every meaningful interaction carries a real variable cost. Each prompt, agent workflow, reasoning step, and API call consumes tokens and infrastructure.

Unlike traditional software, where features become cheaper per user over time, AI-heavy features often remain usage-based and personalised.

That means costs scale much more directly with customer activity. In practical terms:

• More users generate more inference costs

• More complex workflows consume more tokens

• Personalised outputs reduce caching opportunities

• Heavy users can materially impact margins

The result is that software begins to look less like pure SaaS and more like a hybrid of software and services.

A simple way to think about it

Imagine two companies that both charge $10 per user per month.

The first is a traditional SaaS company named, GainZ.

It spends heavily upfront to build the product, but by the time it reaches scale, gross margins stabilise around 80%. At 1 million users, net margins can approach 20%.

The second is an AI-native company named AIBoosts.

It may cost far less to build because a smaller team can move faster with AI tools. But every user interaction incurs ongoing inference costs.

The company becomes profitable earlier because development costs are lower, yet long-term margins remain significantly lower because AI costs continue to grow with usage.

In Domen’s example, an AI-heavy product reaches only about 11% net margin at 1 million users, versus roughly 19% for traditional SaaS.

That difference has major implications for valuation and strategy.

The hidden shift founders need to understand

Many founders focus on how AI reduces engineering costs. That is real and important. Small teams can now build products that once required dozens of engineers.

But lower build costs do not automatically translate into better businesses.

In many cases, the true constraint moves from development cost to operating cost.

The new question becomes: Can you monetise the value of AI faster than your inference costs grow?

That is the central challenge of AI-native software.

Why this matters for valuations

Software historically commanded premium valuations because investors believed margins would expand dramatically over time. If AI compresses those margins, valuation frameworks may evolve as well.

Companies with structurally lower gross margins may be valued more like operational businesses than pure software companies. This does not make them unattractive.

It simply means investors will place greater emphasis on:

• Pricing power

• Proprietary data

• Distribution advantages

• Brand trust

• Operational efficiency

The best AI companies will still be extremely valuable, but the economics may look different from the SaaS playbook of the last decade.

How founders can respond

There are several ways to build durable AI businesses despite lower margins.

1. Align pricing with usage. If costs scale with activity, pricing should reflect that reality through tiers, overages, or premium plans.

2. Focus on high-value workflows. AI works best when it solves problems customers are willing to pay significantly more to address.

3. Build defensible moats. Proprietary data, network effects, and workflow integration matter more than ever.

4. Expand beyond software. Many of the strongest businesses will bundle software with services, compliance, operations, or real-world execution.

The emerging opportunities

Domen highlights several categories that become especially interesting in this new environment:

• Ultra-niche products that were previously too small to justify development costs

• Lower-cost challengers to established SaaS incumbents

• “Luxury software” with strong brand positioning and premium pricing

• Vertical businesses that combine software with real-world services

One of the most compelling implications is that smaller teams can now build profitable lifestyle businesses serving specialised markets that were once uneconomical.

So - AI is lowering the cost to create software, but it is also reshaping the economics of delivering it.

For years, software enjoyed a rare combination of high gross margins and near-zero marginal costs. That assumption no longer holds for many AI-native products.

The winners of the next decade will not simply build impressive AI features.

They will understand how those features affect the P&L, design pricing around real usage, and build businesses that remain profitable as inference becomes a core operating cost.

In other words: The future of software belongs not just to teams that ship AI fastest, but to those that understand the economics deeply enough to scale it sustainably.

Where AI seed investors are most likely to find outliers.

Every AI cycle produces noise. What matters is where outcomes compound, not just where capital is flowing. A recent PitchBook analysis looked at AI subsectors through one narrow but revealing lens: IPO exit predictor scores, a signal for where venture-scale outcomes are statistically more likely.

A few patterns stand out.

• First, agentic commerce infrastructure is quietly emerging as the strongest outlier candidate.

Commerce has historically been the on-ramp for every major platform shift, internet, mobile, cloud, and AI are following the same script. What changes this time is autonomy.

Payments, identity, fraud, loyalty, and inventory systems are being rebuilt so software can transact without humans in the loop. That makes the infrastructure layer, not consumer apps, the long-term value capture point.

• Second, AI-driven drug discovery is moving from promise to economic inevitability.

As AI improves trial design and success rates, the bottleneck shifts downstream. More trials mean more demand for tooling, data infrastructure, and clinical operations software.

Analysts expect this to expand the total addressable market aggressively through the decade, not because drugs get cheaper, but because more drugs actually make it to market.

• Third, AI protection and defence-adjacent systems are benefiting from a similar dynamic: Autonomy replaces humans in high-stakes environments.

Edge computing and real-time decisioning are enabling faster response systems, whether in cybersecurity or physical defence.

The complexity here is not a bug; it’s the moat.

• Finally, autonomous drones and swarms represent a classic early-cycle opportunity.

Fully coordinated land, sea, and air swarms are still hard to execute, but that difficulty creates asymmetry. As AI-first players replace human-piloted systems, legacy hardware and sensor providers face margin and relevance pressure.

Taken together, the signal is consistent:

• Outlier returns are clustering below the application layer

• Infrastructure that enables autonomy, not just intelligence, is where durability lives

• Complexity and regulation are acting as filters, not deterrents

For seed investors, the takeaway isn’t to chase what’s loud. It’s to focus on where AI changes system behaviour, not just user experience. That’s where platform-scale outcomes tend to form.

What’s driving the surprising decline in founder dilution?

One of the most interesting trends in venture right now is hiding behind a simple chart from Carta. Despite all the headlines about startups raising massive rounds, founders are actually giving up less equity than they did a few years ago.

Peter Walker, Head of Insights at Carta, recently shared data from more than 9,000 U.S. software financing rounds, and the numbers tell a fascinating story.

In 2022, the median seed round required founders to sell about 21% of their company. In 2026, that figure has dropped to 18%. The same trend is visible across every stage:

• Series A dilution fell from 20% to 17%

• Series B dilution declined from 17–18% to 13%

• Series C dilution dropped from 13% to 9%

At first glance, this seems counterintuitive. Round sizes are getting larger, especially in AI. Yet founders are keeping more ownership.

So what’s going on?

The fundraising bar has gone up

The most obvious explanation is that fundraising has become much harder.

While social media is filled with announcements of startups raising $40 million or $100 million rounds, those stories represent a very small subset of companies.

Behind the scenes, venture firms are more selective than they have been in years. That means only a small percentage of startups are able to raise institutional capital. But the ones that do often have much stronger leverage.

They may have:

• Exceptional growth

• Breakthrough AI products

• Strong revenue traction

• Elite founding teams

• Multiple term sheets competing for allocation

When investors compete aggressively for a company, valuations rise faster than the amount of money being raised. As a result, founders can raise larger rounds while selling fewer shares.

Venture is becoming more concentrated

This trend reflects a broader shift in venture capital. Investors are increasingly concentrating capital into fewer companies that appear capable of becoming category leaders.

Instead of spreading bets across hundreds of “good” startups, firms are doubling down on a smaller set of “must-own” opportunities.

This creates a barbell dynamic.

At one end are startups that struggle to raise at all. At the other end are standout companies that command premium valuations and highly founder-friendly terms.

There is less middle ground than before.

Founders are staying private longer

Another reason dilution is declining is that companies are spending more years as private businesses.

A startup may now raise Seed, Series A, B, C, D, E, and sometimes even F or G before going public. If founders sell too much equity in the early rounds, they risk becoming overly diluted long before reaching scale.

The market has adapted accordingly.

Early rounds now leave more ownership on the cap table so founders and employees still have meaningful stakes after multiple financings.

This is one of the most practical lessons for founders: financing decisions today need to account not just for the next 12 months, but for the entire multi-stage journey ahead.

Bigger rounds no longer mean bigger dilution

Traditionally, raising more capital meant giving up more of the company. That relationship is weakening.

Today, the best startups can raise substantial amounts of capital while preserving ownership because investors are underwriting future potential more aggressively than ever.

In other words, valuation growth is outpacing check size growth.

That is especially true in sectors like AI, where investors believe a small number of companies could become extremely valuable.

What this means for founders

The decline in dilution is encouraging, but it should not become an obsession.

Ownership matters. But survival matters more.

A founder who preserves two extra percentage points of equity but runs out of cash gains nothing.

The better approach is to optimize for three things:

• Enough capital to reach the next major milestone

• Reasonable dilution

• Flexibility for future rounds

The strongest founders think in terms of long-term ownership, not just the immediate financing event.

So - the fundraising environment is harder than ever, but for exceptional companies, it is arguably better than ever.

Capital is becoming more selective, more concentrated, and more conviction-driven.

If your company demonstrates real traction and strategic importance, investors may be willing to pay dramatically higher prices to participate.

And when that happens, founders retain more ownership while accessing more capital.

That is an incredibly powerful combination.

The venture market may feel tougher on the surface, but the underlying message is optimistic: The best companies are becoming more valuable faster than the dilution required to fund them.

NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Meridian Ventures, a U.S.-based venture firm founded by Harvard Business School graduates Devon Gethers and Karlton Haney, has launched a $35 million debut fund. (Link)

• Haun Ventures, a Menlo Park, CA-based venture capital firm, closed Fund II, at over $1 billion. (Link)

New Startup Deals

• Chromie Health, a NYC-based AI platform for hospital scheduling and operations, raised $2M in Pre-Seed funding. (Link)

• Saile, a NYC-based healthcare staffing and credentialing platform, raised $2.2M in Pre-Seed funding. (Link)

• Flick, a San Francisco-based AI-native filmmaking platform, raised $6M in Seed funding. (Link)

• Graphon AI, a San Francisco-based enterprise AI intelligence platform, raised $8.3M in Seed funding. (Link)

• Ranger AI, a San Francisco-based revenue operations platform for industrial tendering, raised $8.4M in Seed funding. (Link)

• Recursive Superintelligence, a London and San Francisco-based self-improving AI company, raised $650M in funding at a $4.65B valuation. (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)

• Head of Operations - Allocator One | USA - Apply Here

• Vice President, Investor Services - StepStone Group | USA - Apply Here

• Associate / Senior Associate - Stepstone Group | Italy - Apply Here

• Investment Operations Analyst - Polymath Venture | Mexico - Apply Here

• Partner 16, Software Engineer - a16z | USA - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Investment Associate - Struck Capital | USA - Apply Here

• Visiting Engineer - AI Fund | USA - Apply Here

• Finance & Operations Manager - Antler | Singapore - Apply Here

• Investment Team - Antler | Germany - Apply Here

• Investor (Senior Associate/Principal) - Square Peg | USA - Apply Here

• Senior Analyst - ICONIQUE Capital | USA - Apply Here

• Program Manager - a16z | USA - Apply Here

• Senior Associate - ICONIQUE CAPITAL | USA - Apply Here

• Venture Capital Analyst - MicroVentures | USA - Apply Here

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👋 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?

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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)

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📜 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.

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👋 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.

• Where AI seed investors are most likely to find outliers.

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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)

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📜 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.

Where AI seed investors are most likely to find outliers.

Every AI cycle produces noise. What matters is where outcomes compound, not just where capital is flowing. A recent PitchBook analysis looked at AI subsectors through one narrow but revealing lens: IPO exit predictor scores, a signal for where venture-scale outcomes are statistically more likely.

A few patterns stand out.

• First, agentic commerce infrastructure is quietly emerging as the strongest outlier candidate.

Commerce has historically been the on-ramp for every major platform shift, internet, mobile, cloud, and AI are following the same script. What changes this time is autonomy.

Payments, identity, fraud, loyalty, and inventory systems are being rebuilt so software can transact without humans in the loop. That makes the infrastructure layer, not consumer apps, the long-term value capture point.

• Second, AI-driven drug discovery is moving from promise to economic inevitability.

As AI improves trial design and success rates, the bottleneck shifts downstream. More trials mean more demand for tooling, data infrastructure, and clinical operations software.

Analysts expect this to expand the total addressable market aggressively through the decade, not because drugs get cheaper, but because more drugs actually make it to market.

• Third, AI protection and defence-adjacent systems are benefiting from a similar dynamic: Autonomy replaces humans in high-stakes environments.

Edge computing and real-time decisioning are enabling faster response systems, whether in cybersecurity or physical defence.

The complexity here is not a bug; it’s the moat.

• Finally, autonomous drones and swarms represent a classic early-cycle opportunity.

Fully coordinated land, sea, and air swarms are still hard to execute, but that difficulty creates asymmetry. As AI-first players replace human-piloted systems, legacy hardware and sensor providers face margin and relevance pressure.

Taken together, the signal is consistent:

• Outlier returns are clustering below the application layer

• Infrastructure that enables autonomy, not just intelligence, is where durability lives

• Complexity and regulation are acting as filters, not deterrents

For seed investors, the takeaway isn’t to chase what’s loud. It’s to focus on where AI changes system behaviour, not just user experience. That’s where platform-scale outcomes tend to form.

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Big idea + report of the week :

• Is raising at a high valuation actually putting your startup at risk?

• Is AI quietly eliminating customer support jobs?

Frameworks & insightful posts :

• The real AI shift hidden inside 20,000+ enterprise deployments.

• Could AI give enterprises the kind of moat that made Google unstoppable?

• Is the real edge in VC picking the best startup or picking the right category early?

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🧠 Big idea + report of the week

Is raising at a high valuation actually putting your startup at risk?

There’s a growing feeling in venture right now that things have gone a bit… irrational.

That feeling isn’t random.

It comes from data shared by Carta’s Head of Insights, Peter Walker, who analysed nearly 14,000 seed rounds across U.S. companies. His latest breakdown shows that the top 5% of seed valuations have reached ~$115M - a number that would have seemed unrealistic not too long ago.

At the same time, investors like Bryce Roberts have openly said they “can’t relate” to where the VC world is heading.

And when you look at the data, you understand why.

Because what looks like a single market from the outside is actually splitting into two very different realities.

On one end, you have a small group of companies - mostly AI-driven, where everything is moving faster than ever:

• Top-tier founders are raising at record speed, sometimes in days

• Large funds are entering at seed, competing aggressively

• Valuations are being set based on future dominance, not current traction

• $100M+ seed rounds are becoming more common in this segment

This is the part of the market everyone sees, shares, and talks about.

But it’s not the full picture.

Because for the remaining 95% of companies, the environment looks very different.

The median seed valuation is still around ~$24M, which tells you that most startups are still raising in a relatively grounded range. Investors are still asking the same core questions around traction, market, and execution.

In other words, discipline hasn’t disappeared—it’s just not evenly distributed.

This creates a misleading narrative.

When people talk about “seed valuations going crazy,” they’re often looking at blended numbers that combine:

• A small set of extreme outliers is pulling valuations up

• A much larger base of companies is operating normally

And that gap between ~$24M (median) and ~$115M (top 5%) is where the confusion comes from.

The deeper insight here is about the AI cycle itself.

AI can absolutely be a transformational shift, and at the same time, behave like a classic hype cycle where capital floods in faster than outcomes materialise.

Both can exist together. That’s what this data is showing.

For founders, the biggest mistake right now is anchoring to the wrong benchmark.

It’s easy to see these headline valuations and feel like you need to match them. But those companies are exceptions, not the standard.

What actually matters is much simpler:

• Are you building something that can justify your valuation over time?

• Are you optimising for long-term outcomes or short-term optics?

• Can you grow into the expectations you’re setting today?

Because high valuations don’t remove risk. They just delay when it shows up. And in this kind of market, the real advantage isn’t raising at the highest price.

It’s knowing which market you’re actually playing in.

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.

SOMETHING MORE

🧩 Frameworks & insightful posts

The real AI shift hidden inside 20,000+ enterprise deployments.

Most people still think AI is in its “assistant phase” - helping write, summarise, or speed things up.

But inside enterprises, something much bigger is already happening.

A report covering 20,000+ organisations (including over 60% of the Fortune 500) shows a clear shift: companies are moving from using AI as a tool → to building systems where AI actually executes work end-to-end.

This shift isn’t gradual - it’s accelerating fast.

• Multi-agent systems usage grew 327% in just 4 months

• 80% of databases are now created by AI agents

• 97% of database testing environments are handled by agents

This is the key change: AI is no longer sitting on top of workflows - it’s starting to run the infrastructure those workflows depend on.

What’s interesting is how practical the adoption looks.

Despite all the hype, enterprises aren’t starting with bold reinvention. They’re starting with replacing repetitive, necessary work at scale.

Around 40% of use cases today are focused on customer-facing operations like support, onboarding, and personalisation, while others focus on tasks like classification, routing, and summarisation.

In other words, the first wave of AI is not disruption - it’s operational efficiency.

But underneath this, a deeper shift is happening in how systems are built.

Traditional enterprise infrastructure was designed for humans - predictable workloads, manual provisioning, and structured inputs.

Agentic systems break all of these assumptions. They continuously create and destroy environments, generate high-frequency real-time queries, and run multi-step reasoning loops across systems.

That’s why a new infrastructure layer is emerging - systems built not for humans, but for autonomous agents operating continuously.

At the same time, the way software gets built is changing.

The rise of “vibe coding” (describe → generate) is turning software creation into a natural language problem. More than 50,000 AI apps have already been created in a short period, growing at ~250% in just six months. Even non-technical users can now build working prototypes.

This massively increases the speed of building - but also introduces a new problem.

Most AI never makes it to production.

• ~95% of GenAI pilots fail to ship

• Only ~2% of companies report meaningful business results

So the real bottleneck isn’t building AI. It’s operationalising it. And the data shows very clearly what separates teams that succeed:

• Companies using evaluation systems → 6x more AI projects reach production

• Companies investing in governance → 12x more projects reach production

Evaluation ensures quality and reliability. Governance ensures control, safety, and compliance.

Without these, AI stays stuck in demos.

Another subtle but important shift is happening at the model layer.

Enterprises are no longer betting on a single provider - they’re going multi-model by default.

Around 78% of companies now use two or more LLM families, and the share using three or more is rising quickly. This is less about experimentation and more about optimising performance, cost, and flexibility.

Finally, one of the clearest signals of where things are headed: AI is becoming real-time infrastructure.

Nearly all requests (~96%) are processed in real time, not batch. This matters because AI is no longer just analysing data after the fact - it’s becoming part of live systems that directly impact decisions and outcomes.

So -

We’ve moved past “AI as a feature.” We’re entering a phase where AI becomes part of how companies actually operate.

But the winners won’t be the ones using the most AI. They’ll be the ones who figure out how to turn it into reliable, production-grade systems with the right guardrails.

Because in this phase, the edge isn’t access to AI - it’s execution.

Google’s 20-year secret is now available to every enterprise.

For the last 20 years, the biggest consumer companies have built a simple but powerful advantage:

Every user action made the product smarter.

Google, Amazon, Netflix, TikTok - they didn’t just track outcomes. They captured behaviour. What you clicked, ignored, hovered on, and abandoned. That data compounded over time into better recommendations, better products, and ultimately, massive defensibility.

Ashu Garg (Foundation Capital) points out something interesting: Enterprise software never had this loop.

Not because enterprise decisions don’t matter - but because they were almost impossible to capture.

In B2C, decisions happen inside a clean interface. One user, one action, fully observable.

In B2B, decisions are messy.

They happen across sales, finance, legal, ops, and management - each with different incentives. A pricing decision isn’t a click. It’s a negotiation. A contract isn’t a forml it’s a series of trade-offs.

And most importantly, enterprise systems only captured the outcome, not the reasoning behind it.

• A discount field tells you the number, not why it was given

• A contract shows the final clause, not what was negotiated

• A support ticket shows resolution, not the decision path

So while consumer companies built compounding data moats, enterprise software mostly didn’t. That’s starting to change now.

The shift is from behavioural data → decision data.

And this is where things get interesting.

Enterprise work today increasingly happens on “instrumentable surfaces”- Slack threads, docs, tickets, calls, approvals. What used to live in someone’s head is now partially visible across systems.

But visibility alone wasn’t enough. The data was unstructured. Now, AI changes that.

• LLMs can extract reasoning from emails, transcripts, and comments

• Agents introduce structured checkpoints (propose → edit → approve)

• Every human correction becomes a signal of judgment

Example: An AI agent suggests a pricing proposal. The sales rep adjusts it and adds context: “competitive pressure from X.”

That edit is not noise. It’s a decision trace - what the system missed and what actually mattered.

And as more workflows become agent-driven, this kind of signal becomes unavoidable.

What used to be implicit (intuition, experience, judgment) starts becoming explicit, structured, and learnable.

This creates something enterprise software never had before: a compounding loop of decision-making.

But here’s the important part - most incumbents won’t capture this.

Because they sit in the wrong place.

• Systems like Salesforce store the current state, not the decision context

• Warehouses like Snowflake see data after decisions are made

• Neither sits at the moment where decisions actually happen

The real opportunity sits in the write path - the point where decisions are made, modified, and approved.

That’s where reasoning still exists.

And that’s why a new category is emerging: systems that don’t just store data, but capture how decisions are made across an organisation.

If this works, the implications are massive:

• Enterprise value shifts from features → proprietary decision history

• Systems move from “what happened?” → “Why did it happen?”

• Eventually → “What should we do next?” (prediction layer)

And unlike model capabilities, this kind of data compounds uniquely for each company.

The takeaway is simple: Consumer companies built moats by understanding user behaviour. Enterprise companies will build the next generation of moats by capturing how decisions are made.

And for the first time, that data is actually becoming visible.

Is the real edge in VC picking the best startup or picking the right category early?

Most people think venture capital is about selection.

• Finding the best founder.

• Backing the strongest product.

• Winning access to the top deal.

But a widely shared thread by Bessemer investor Aditya Nidmarti flips that idea completely. His argument is simple, but uncomfortable:

The biggest returns in venture don’t come from picking the best company.

They come from being early to the right category.

If you look at how venture returns actually play out, this starts to make sense. Returns don’t distribute evenly - they follow a power law. A small number of sectors end up driving a disproportionate share of outcomes, while everything else contributes marginally.

That’s what the chart above is really showing. A few “power law sectors” generate most of the dollars. The rest of the market matters far less than we like to believe.

And historically, these sectors weren’t hidden. They were visible - but ignored or underestimated.

• In the early 2000s, internet media and marketplaces were clearly emerging

• Around 2010, cloud and mobile were already showing strong signals

• By 2020, AI was visible to anyone paying attention to frontier research

The investors who captured the most value didn’t just pick winners within these categories. They concentrated on the category before consensus formed.

That distinction matters more than it seems.

Because once a category becomes obvious, the game changes:

• More capital flows in

• Competition increases

• Entry prices rise

• Outcomes get diluted across more players

At that point, selection starts to matter more - but the biggest alpha has already been captured.

One of the most interesting things is around the time horizon.

Trying to predict 10 years out is mostly speculation. Trying to invest based on what’s already obvious is too late.

The real edge sits in a narrower window. Around 5 years out. This is where things get interesting:

• Early signals are visible (research, prototypes, early startups)

• Adoption curves are starting to take shape

• But capital hasn’t fully crowded the space yet

It’s early enough to be ahead of consensus, but grounded enough to not be guesswork.

And when you get this right, it changes how you build a portfolio. Instead of asking, “Which is the best company in this category?” You start asking, “Is this category going to matter massively?”

A great example of this is Neil Shen.

In the early 2010s, he developed a strong conviction that e-commerce in China would become even more important than retail had been in the U.S. Instead of trying to pick a single winner, he built exposure across the category by investing in roughly 15 e-commerce companies.

The result wasn’t dependent on one perfect bet. He ended up capturing multiple winners because he was right about the category itself.

That’s the deeper shift this idea points to. In venture, selection still matters - but it’s often secondary.

The first question is: Are you in a category that can produce power-law outcomes?

Only after that does it make sense to optimise for: Founder quality, Product differentiation and Execution.

Because if the category itself doesn’t produce outsized returns, even great companies can lead to average outcomes. And that’s the part most people underestimate.

They spend time trying to pick the best company in a category that may never matter.

Instead of stepping back and asking: Is this a category where outsized outcomes are even possible?

NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Andreessen Horowitz’s crypto-focused arm, a16z crypto, has raised a new $2.2B fund to continue backing blockchain and crypto startups despite the recent slowdown in the crypto market (Link)

• Katie Haun, Former Andreessen Horowitz investor, has raised $1B across new funds to back early- and late-stage startups. (Link)

• Ridgeline Capital Management, an early-stage VC firm, closed Ridgeline Ventures Fund II LP and Ridgeline Ventures Fund II-S L.P., with over $180m in commitments. (Link)

• BMW i Ventures, the corporate venture capital arm of car manufacturer BMW, launched a $300m fund. (Link)

• 137 Ventures, a U.S.-based growth-stage VC firm founded by Justin Fishner-Wolfson, has raised $700M across two new funds to back high-impact startups. (Link)

New Startup Deals

• Mobility Signage, a Munich, Germany-based public transport infrastructure company, raised €1.8M in Pre-Seed funding. (Link)

• Zapdos Labs, an Austin, TX-based AI software company, raised $500K in Pre-Seed funding. (Link)

• Nace.AI, a Palo Alto, CA-based enterprise workflow automation company, raised $21.5M in Seed funding. (Link)

• Barocal Ltd, a Cambridge, UK-based climate tech company, raised $10M in Seed funding. (Link)

• LakeFusion, an Austin, TX-based data platform startup, raised $7.5M in Seed funding. (Link)

• Reserv, a NYC-based insurance infrastructure platform, raised $125M in Series C funding.(Link)

• Dreambase, an Austin, TX-based AI analytics platform, raised $3.7M in Seed funding.(Link)

• Fermeate, a San Francisco, CA-based industrial biotech startup, raised $2M in Seed funding.(Link)

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

• Partner 22 -a16z | USA - Apply Here

• Venture Capital Analyst / Associate - Z57 | Hybrid - Apply Here

• Infra / Platform Engineer - Pear VC | USA - Apply Here

• Investment Associate - Eka Venture | USA - Apply Here

• Associate / Senior Associate - Stepstone Group | Italy - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Investment Associate - Struck Capital | USA - Apply Here

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👋 Hey, here’s today at a glance -

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

• Is software shrinking or about to 10x? - Redpoint’s data reveals the truth.

• How to actually name your startup: a proven framework from leading founders

📜 DEEP DIVE

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 optimise for who feels easiest to start with. Optimise for who you can build with for the next 10–15 years.

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📃 QUICK DIVES

Is software about to shrink or expand 10x? Redpoint’s data reveals the truth.

Every time markets pull back, the same comparison shows up - “this looks like 1999 again.”

Redpoint Venture recently broke this down in their 2026 market update, and the data tells a very different story. What we’re seeing isn’t speculative excess running ahead of reality. Its demand is pulling the entire ecosystem forward.

In the dotcom era, infrastructure was massively overbuilt with almost no usage. Today, it’s the opposite.

• OpenAI and Anthropic are already doing $20B+ ARR

• 90%+ of new data centre capacity is pre-committed before it’s even built

• ChatGPT reached ~1B users in ~4 years (the internet took a similar time to reach just ~70M)

The constraint today isn’t demand - it’s supply. Power, land, and compute are limiting how fast infrastructure can scale. That’s a fundamentally different setup from 2000.

But the more important shift isn’t infrastructure - it’s what AI is doing to the software market itself.

Until recently, most AI products lived in “copilot mode” - they helped people do their jobs better. That meant they competed for existing software budgets.

Now we’re moving into something bigger: agents that actually do the work.

• Software market today → ~$0.5T

• With task-level agents → expands to ~$1.2T

• Fully autonomous systems → could tap into ~$6T+ knowledge worker labor

This is where things change. AI stops being a feature and becomes a substitute for labour.

And that shift is already showing up in how software companies are being valued.

The selloff in software isn’t random - it’s selective.

• Vertical SaaS is holding up → strong data moats, embedded workflows

• Infrastructure is stable → AI increases demand for compute, storage, observability

• Horizontal SaaS is down ~35% → most exposed to AI disruption

The pattern is clear. Products that own data + workflows survive. Products that mainly coordinate tasks are at risk - because coordination is something AI does natively.

This is also visible from buyer behaviour. CIOs aren’t increasing budgets. They’re reallocating.

• 45% of AI spend is coming from existing software budgets

• 54% of companies are consolidating vendors

• Only 3% expect to add more tools

So the pressure isn’t just competition - it’s replacement.

Categories like sales automation, customer support, and IT management are the most exposed - not randomly, but because AI can directly do the job, not just assist it.

At the same time, incumbents aren’t out yet.

More than half of buyers still prefer to add AI to existing vendors rather than switch. But that creates a different challenge: these companies don’t just need to ship features - they need to rebuild themselves.

And that’s where most will struggle.

AI-native companies are built differently:

• They rely on first-principles thinkers, not “pattern-matching” operators

• Product development starts from capability (what models can do), not just customer requests

• The entire org is structured around speed and iteration

This isn’t a feature upgrade. It’s a re-founding. Finally, one subtle but important insight from the data:

Private AI companies look expensive - until you adjust for growth.

• Private growth-stage companies → ~61x ARR

• Public comps → ~9.7x ARR

But growth rates flip the story:

• Private → ~640% growth

• Public → ~29% growth

On a growth-adjusted basis, private companies are actually cheaper per unit of growth.

The takeaway here isn’t just about markets - it’s about timing.

Historically, the biggest companies in every tech wave were founded around years 4–5 of the shift. We’re now in year 4 of the AI cycle.

That window is open - but it’s also the most competitive it’s ever been.

Which makes this moment unusual: It’s not a bubble driven by hype. It’s a market being reshaped in real time, where demand is real, stakes are higher, and the bar to win is significantly tougher.

How to actually name your startup: a proven framework from leading founders.

Most founders think naming is a quick brainstorm or a “let’s run it through ChatGPT” exercise. Arielle Jackson (From First Round) has helped hundreds of startups name companies and products, and her view is simple: naming is emotional, subjective and slow and still one of the most important early brand decisions you make.

The biggest mistake founders make? They want the perfect name on day one, even though the best names often become great over time (Disney, Nike, Square). Your job is to pick a name that fits the strategy today and doesn’t limit you tomorrow.

Here’s the practical way to approach it.

Start with a placeholder

Your incorporation name is not your final name. Most good companies don’t start with the name they end up with. Some founders even intentionally pick ridiculous incorporation names just to avoid attachment. It’s better to incorporate as something unusable and rename once you’re clear about your product.

When should you pick the real name?

• When you’ve hit clarity on positioning

• When you’re about to launch and need a public-facing brand

• When your old name stops fitting your product

• When legal risk forces a change

Founders who rush naming end up boxed in. Founders who delay it slightly make far better choices.

Start with positioning, not creativity

Arielle’s process always begins with a simple question: Who is this for? What space do we occupy? What makes us different?

A clear positioning statement makes naming dramatically easier because you know what your name should signal: sophistication, speed, trust, creativity, community, or something else entirely.

Build a naming brief

Before brainstorming, outline the constraints:

• Who is the target user?

• What tone and personality should the name convey?

• Do you prefer descriptive, suggestive or abstract names?

• Any languages, meanings or connotations to avoid?

• Any domain or trademark constraints?

• Five names you love, five you hate — and why

This reduces randomness and helps others understand your taste.

How to namestorm

This is where founders usually go wrong. They brainstorm alone. Arielle suggests the opposite — widen the surface area. Bring in employees, friends, even linguists. And use AI not to “name your startup” but to explore:

• synonyms

• technical words

• metaphors

• foreign-language variations

• historical references

Keep the prompts specific. AI is better at generating raw material than finished names.

What themes to explore

Use both direct and indirect angles:

• Your core value (expertise → Maven)

• Your product’s motion (gather, connect, accelerate)

• Patterns, metaphors, symbols

• Names from mythology or history

• Simple phrases (PayPal, First Round)

A good brainstorm should produce hundreds of words, not ten.

Narrow the list with structured criteria - Evaluate your list against:

Distinctiveness, Memorability, Trademark risk, Domain feasibility, How it sounds when spoken, How it looks visually, & Whether it will age well.

Then shrink to a shortlist of 10–25, then a top 3.

Signs you’ve found a good name

• It’s polarising — some people love it, some hate it

• It grows on you over a few days

• It works on two levels (simple now, deeper meaning later)

• It avoids the obvious, generic and forgettable

Signs it’s a bad name

• Too many companies already use it

• It’s easy to parody

• Locks you into a narrow future use case

• Sounds like a competitor

User feedback helps, but lightly

Ask small groups of users what each name communicates, not which one they “like.” People tend to choose descriptive names because they’re familiar. You want clarity, not consensus.

A simple memorability test works well: share three names today and see which one people recall tomorrow.

Legal + domain basics

Arielle’s rule: Get the name right first. Then solve the domain. You can use:

• prefixes (usemaven.com, tryfigma.com)

• suffixes (squareup.com, awaytravel.com)

• alternative TLDs (.ai, .co, .io)

• lease-to-own domain deals

Trademark clearance should be done before spending money on identity work. A quick USPTO search rules out obvious conflicts. A trademark lawyer helps determine if you can register the name.

Product naming vs company naming

If you have one product, keep the same name as the company. Once you have multiple products, use a simple structure like: Company + Descriptor

Example: Google Maps, Google Drive, Square Reader.

Use unique product names only when they help establish a separate identity (Cash App, Android).

Naming is messy. It’s emotional. And it takes time. But with the right framework, it becomes structured enough to produce a name that supports your positioning, doesn’t box you in, is easy to remember and ages well.

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👋 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.

• Which AI sectors are most likely to produce venture-scale exits?

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📜 DEEP DIVE

Where AI seed investors are most likely to find outliers.

Every AI cycle produces noise. What matters is where outcomes compound, not just where capital is flowing. A recent PitchBook analysis looked at AI subsectors through one narrow but revealing lens: IPO exit predictor scores, a signal for where venture-scale outcomes are statistically more likely.

A few patterns stand out.

• First, agentic commerce infrastructure is quietly emerging as the strongest outlier candidate.

Commerce has historically been the on-ramp for every major platform shift, internet, mobile, cloud, and AI are following the same script. What changes this time is autonomy.

Payments, identity, fraud, loyalty, and inventory systems are being rebuilt so software can transact without humans in the loop. That makes the infrastructure layer, not consumer apps, the long-term value capture point.

• Second, AI-driven drug discovery is moving from promise to economic inevitability.

As AI improves trial design and success rates, the bottleneck shifts downstream. More trials mean more demand for tooling, data infrastructure, and clinical operations software.

Analysts expect this to expand the total addressable market aggressively through the decade, not because drugs get cheaper, but because more drugs actually make it to market.

• Third, AI protection and defence-adjacent systems are benefiting from a similar dynamic: Autonomy replaces humans in high-stakes environments.

Edge computing and real-time decisioning are enabling faster response systems, whether in cybersecurity or physical defence.

The complexity here is not a bug; it’s the moat.

• Finally, autonomous drones and swarms represent a classic early-cycle opportunity.

Fully coordinated land, sea, and air swarms are still hard to execute, but that difficulty creates asymmetry. As AI-first players replace human-piloted systems, legacy hardware and sensor providers face margin and relevance pressure.

Taken together, the signal is consistent:

• Outlier returns are clustering below the application layer

• Infrastructure that enables autonomy, not just intelligence, is where durability lives

• Complexity and regulation are acting as filters, not deterrents

For seed investors, the takeaway isn’t to chase what’s loud. It’s to focus on where AI changes system behaviour, not just user experience. That’s where platform-scale outcomes tend to form.

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📃 QUICK DIVES

The competitive strategy AI companies are copying from Google.

There’s a pattern that quietly built one of the most dominant companies in tech.

Google didn’t just win because search was better. It won because it removed every layer between the user and search - and then made sure nothing could sit in between again.

Tomasz Tunguz from Theory Ventures recently shared this idea again in the context of AI, and once you see it, you can’t unsee it.

How Google actually built its moat

At its core, Google was always a simple business: Search → data → better results → ads → revenue.

Everything else - Gmail, Maps, Chrome, Android - was never the business. It was a distribution.

Instead of monetising these products directly, Google used them to expand the surface area of search.

• Gmail made storage free → more users stayed in Google’s ecosystem

• Maps removed the need for paid navigation → captured local intent

• Android ensured Google wasn’t blocked on mobile

• Chrome increased browsing speed → more searches per user

This wasn’t a random expansion. It was systematic. Each product removed a friction point and pushed more activity back into search.

Over time, this created a self-reinforcing loop: More usage → more data → better search → more usage.

That loop became the moat.

Anthropic (and AI companies) are doing the same thing

Now replace “search” with “model.” The playbook looks almost identical, but the surface area is much broader.

Anthropic isn’t just building a chatbot. It’s quietly surrounding the model with tools that expand where and how it gets used.

• Claude Design → turns prompts into UI systems

• Claude Cowork → manages files and workflows

• Code Security → scans and fixes vulnerabilities

• MCP → connects AI directly to data systems

• Interactive apps → bundle productivity inside the model

Individually, each of these looks like a product. Collectively, they act as a distribution for the model.

The strategy is simple: the more tasks the model can handle, the more central it becomes to how work gets done.

The new flywheel: usage → data → intelligence

What made Google powerful was not just usage - it was the compounding effect of usage. AI companies are building the same loop, but with a different input.

More use cases lead to more interactions. More interactions generate more data. More data improves the model. A better model drives more usage again.

The important shift here is that AI isn’t competing for a single action like search queries. It’s competing for entire workflows.

That makes the upside - and the risk much bigger.

Why this strategy quietly kills categories

One of the most misunderstood parts of this strategy is that it doesn’t require building the best product. It only requires building something good enough and bundled.

That’s usually enough to change user behaviour. We’ve already seen this movie before.

• Gmail made paid email irrelevant.

• Google Maps killed standalone navigation tools.

• Chrome weakened third-party browsers.

Now the same pressure is showing up again, but across a much wider set of categories.

• design tools becoming features inside AI

• Basic coding tools are being replaced by agents

• workflow tools getting absorbed into copilots

• Security checks are becoming automated layers

The shift doesn’t happen overnight. It starts slowly, then becomes default.

Where this gets more interesting (and overlooked)

Here’s something most people miss: Commoditization increases usage… but it also reshapes where value lives.

When complements become free, the value doesn’t disappear - it moves. In the AI stack, we’re already seeing this happen.

Value is shifting upward into distribution, brand, and trust. And downward into infrastructure, compute, and proprietary data.

The middle layer - tools that don’t own either- is where things get squeezed. This is why many AI wrapper products feel fragile. They sit exactly in the middle.

What this means for founders

If you’re building today, the real question isn’t “is this a good product?” It’s: where do I sit in the flywheel?

Because that determines whether you benefit from this shift or get absorbed by it.

• Horizontal tools → face direct pressure from AI platforms

• Deep, niche workflows → more defensible

• Proprietary data or distribution → real leverage

• API-dependent products → replaceable over time

The opportunity hasn’t gone away. It has just become more specific.

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 you can use if you’re breaking into a market you don’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 founders can 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?

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👋 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 :

• If 80% of companies are using AI agents, why can’t they prove ROI yet?

• Are startup exits actually dying or just concentrating in the US and AI?

Frameworks & insightful posts :

• How to angel invest (even if you have no money).

• What are the smartest founders building right now (according to YC)?

• The marketing framework ClickUp used to scale to a $4B company.

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🧠 Big idea + report of the week

If 80% of companies are using AI agents, why can’t they prove ROI yet?

There’s a strange disconnect happening inside enterprises right now. AI agents are moving fast - from experiments to production, but the systems to measure their impact are still catching up.

A recent report from CB Insights highlights this gap clearly: in a survey of executives, 80% said AI agents are a priority… yet 40% admitted they either can’t track ROI or don’t even know how to measure it.

That’s not a small problem. It’s a signal. Whenever adoption moves faster than measurement, a new infrastructure layer is built.

And that’s exactly what’s happening here.

The next wave of AI startups won’t just build agents - they’ll build the systems that explain whether those agents are actually working.

Three categories are quietly emerging as the backbone of this shift:

• Observability & evaluation (knowing what your agents are doing)

• Memory infrastructure (helping agents retain and use context)

• Cost + ROI attribution (linking spend to real business outcomes)

Each of these solves a different bottleneck - but together, they define how AI agents scale inside real companies.

Take observability first.

Right now, agents fail silently more often than people think. They hallucinate, mis-handle workflows, or break in edge cases - and without proper tooling, no one even notices.

That’s why this has become the most active AI market right now by deal count. What’s interesting is how fast this layer is becoming “must-have”:

• Startups are building automated evaluation systems that test agents before deployment

• Others are using reinforcement learning from real-world failures to continuously improve agents

• Simulation tools are running thousands of scenarios to stress-test agents at scale

Even incumbents like Snyk and Coralogix are acquiring in this space - clear signal that monitoring won’t stay a feature, it becomes core infrastructure.

Then comes memory.

Most AI agents today are still surprisingly “forgetful.” They handle tasks well in isolation but struggle in real enterprise environments where context matters - past conversations, internal data, workflows across teams.

That’s why memory is emerging as a separate category. Not just storing chat history - but building systems where agents can:

• Decide what information matters

• Update their memory over time

• Retrieve the right context across sessions and systems

This directly addresses one of the biggest adoption blockers companies report: integration complexity and lack of internal expertise.

If agents can “remember” the business, they become far more useful. Finally, the hardest piece: cost and ROI attribution.

Most companies today track AI success using proxy metrics:

• Time saved

• Productivity gains

• Cost reduction

But only ~25% are actually measuring revenue impact. That’s a huge gap.

Because AI doesn’t become a budget priority until it ties to outcomes. This is where a new generation of tools is emerging:

• Platforms that map model usage and token spend to business metrics

• Systems that track costs across multiple AI providers and agents

• Tools that show how changes in prompts or models impact revenue, latency, and quality

Right now, this category is still early - but it’s arguably the most important one because visibility without outcomes doesn’t drive decisions.

What’s really happening here is a shift in where value gets created. The first wave of AI was about building capabilities.

The next wave is about making those capabilities measurable, reliable, and economically justifiable.

And that’s where the biggest opportunities now sit. If you’re building in AI today, it’s worth asking a simple question:

Are you building the agent… Or the layer that helps companies trust, scale, and pay for those agents?

Because history suggests the second layer often ends up just as valuable.

Are startup exits actually dying or just concentrating in the US and AI?

For the last two years, one narrative has dominated venture: exits are broken.

But the latest Q1’26 data from CB Insights tells a more nuanced story. Yes, global exits have slowed. But they haven’t disappeared - they’ve become highly selective, and increasingly concentrated in specific regions and sectors.

At a headline level, the numbers look concerning.

Exit activity dropped 15% in Q1’26, hitting a near two-year low. IPOs were cut almost in half, falling from 196 to just 111 in a single quarter. M&A activity also declined.

But when you zoom in, the story changes. This isn’t a uniform global slowdown - it’s a regional divergence.

• US exits were nearly flat (just -2% QoQ)

• Europe saw a 21% decline

• Asia dropped even more sharply at 25%

In other words, the US is holding steady while the rest of the world is pulling back.

That gap is starting to matter more than most people realise.

In Asia, regulatory pressure - especially in China - continues to slow liquidity. In Europe, cross-border deal friction and weaker IPO pipelines are adding drag. Meanwhile, US markets remain relatively more open, deeper, and more liquid.

The result: exits are concentrating where capital markets actually work. At the same time, there’s another layer to this shift - what kinds of companies are exiting?

AI is quietly taking over the exit market.

• AI M&A deals hit 266, near record highs

• AI IPOs reached a record 21 in a single quarter

• AI companies made up 15.4% of all US exits - the highest ever

So even as total exits decline, AI exits are increasing their share. That’s a critical signal. Because it suggests the market isn’t shutting down - it’s filtering.

Capital is still flowing. Buyers are still active. Public markets are still opening. But only for companies that meet a much higher bar:

• Clear category leadership

• Strong revenue visibility

• Strategic importance (especially in AI)

You can see this in the largest deals of the quarter.

Massive, high-conviction transactions are still happening - like the Google-Wiz deal or the broader consolidation across AI infrastructure and platforms.

So, we’re not in an “exit drought.” We’re in a selective liquidity environment. And that has real implications for founders:

• Building a “good” company is no longer enough - you need to be category-defining

• Geography matters more than before - where you build affects how you exit

• AI tailwinds are real - they’re influencing not just funding, but actual liquidity

For investors, it explains why DPI has been slow - not because exits are impossible, but because only a narrow slice of companies are clearing the bar right now.

And for the ecosystem as a whole, it points to something bigger: The venture market isn’t contracting evenly.

It’s concentrating on fewer regions, fewer companies, and fewer breakout outcomes. Which makes one thing clear: The exit market isn’t broken. It’s just a lot more unforgiving.

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SOMETHING MORE

🧩 Frameworks & insightful posts

How to angel invest (even if you have no money).

Most people think angel investing starts with capital. Big checks. Strong networks. Years of experience.

But that assumption is outdated.

Sarah Chieng shared a simple but powerful insight after her first angel investment (which later got acquired by Jeff Bezos): angel investing today is less about capital - and more about value.

And once you understand that, the barrier to entry drops significantly. We’re in a capital-abundant era.

In 2025 alone, over $160B was deployed globally into AI. Pre-seed rounds that used to be $500K are now closer to $2M. Founders aren’t struggling to find money - they’re struggling to decide which money actually matters.

That changes the role of an angel completely. Because if capital is abundant, then the only thing that differentiates you is:

• what you know

• who you can connect with

• and how you can help

In many cases, early-stage operators can add more value than traditional investors - especially in AI, where execution speed and insight matter more than capital access.

So how do people actually get started without money? The most common path today is through scout programs.

Instead of investing your own capital, you invest on behalf of a VC firm. Firms like a16z, Sequoia, Index, and others give scouts an allocation (say $50K-$200K per year) to invest in startups, and you get a share of the upside if those companies succeed.

The structure varies:

• Some give full autonomy (“deploy this capital however you want”)

• Others are more gatekept (“bring deals, partners decide”)

But either way, it solves the biggest problem - access to capital.

Once you’re in, the real question becomes: how do you actually become a good angel?

Sarah breaks it down into three simple ideas - and they’re more practical than most investment frameworks.

1. Your value is your leverage

Angel investing is not about writing checks. It’s about earning the right to be in the round. And that only happens if you bring something founders actually need.

• if you’re strong at GTM → help with distribution

• if you work in a top AI company → unlock access or insights

• if you have a network → make the right introductions

If you don’t think this way, you’ll only get access to weak deals.

Good founders don’t need money. They need useful people.

2. Who you learn from matters more than where you invest

Most people optimise for brand, which fund they scout for.

But the real advantage is who you’re learning next to. Angel investing is mostly judgment under uncertainty. So your learning curve depends on whether you have someone you trust to sanity-check your thinking.

Someone you can call and say:

“This looks like every other AI startup… but I think this one is different. Am I missing something?”

That feedback loop compounds fast.

3. Only invest in two types of founders

This is the sharpest filter in the piece. There are only two categories worth investing in:

• people you know extremely well (your “best friends”)

• or people who are undeniably exceptional (“Michael Jordan”)

Anything in between is noise. Because as an angel, you don’t have the luxury of large portfolios or long timelines like VCs. You need a much higher bar for conviction.

What ties all of this together is a simple shift in mindset. Angel investing used to be about access to capital.

Now it’s about access to value.

The people who win in this game aren’t the ones writing the biggest checks. They’re the ones founders actually want on their cap table because they make the company better.

What are the smartest founders building right now (according to YC)?

There’s a clear shift happening. AI is no longer a feature you add to software - it’s becoming the foundation everything is built on. And when YC publishes its Requests for Startups, it’s usually a good signal of where the next wave is forming.

This comes from Y Combinator’s latest Summer 2026 RFS, where they outline the ideas they want founders to go after based on what they’re seeing on the frontier.

What stands out isn’t just individual ideas - it’s the pattern underneath them.

The big shift: from tools → systems → infrastructure

For the last couple of years, most startups were building AI copilots - tools that help people do work faster. That phase is ending.

Now, YC is clearly pointing toward something bigger:

• replacing entire services (not just improving them)

• Rebuilding software from scratch as AI-native systems

• creating new infrastructure layers for agents and automation

• pushing AI into the physical world (robots, chips, agriculture, space)

This is less about adding AI to workflows… and more about redesigning the entire stack.

1. AI replacing services (not software)

One of the clearest opportunities YC highlights is AI-native service companies. Instead of selling SaaS tools, these companies directly deliver outcomes:

• accounting, tax, compliance

• insurance brokerage

• healthcare admin

Services markets are far bigger than software markets, and a lot of that work is already outsourced.

So the winning companies won’t say “here’s a tool.” They’ll say: “we’ll just do it for you.”

2. The rise of the “company brain”

Another major theme is the idea that data isn’t enough - context is missing. Every company today runs on fragmented knowledge:

• Slack threads

• emails

• internal docs

• people’s memory

YC calls the solution a “company brain” - a system that:

• pulls knowledge from everywhere

• structures it

• keeps it updated

• turns it into something AI can actually execute on

This becomes the missing layer between raw data and real automation. And if this works, it changes everything - because AI stops assisting work and starts doing it reliably.

3. Software is being rebuilt for agents

YC makes a bold point: the next billion “users” of software won’t be humans - they’ll be AI agents. That breaks how software is designed today.

Current software assumes:

• Humans clicking buttons

• visual interfaces

• manual workflows

But agents need:

• APIs, CLIs, machine-readable systems

• structured documentation

• programmatic access

So a massive opportunity opens up:

build software that agents can use, not humans.

And that likely won’t come from incumbents retrofitting old systems — it’ll come from startups building from scratch.

4. SaaS is vulnerable (and that’s the opportunity)

YC directly calls this out: if AI really weakens SaaS moats, this is one of the biggest startup opportunities in a decade. Why? Because the old advantage of SaaS was:

• expensive to build

• hard to replicate

• required large teams

AI changes that equation:

• 10–100x faster development

• smaller teams can compete

• legacy codebases lose their edge

That opens multiple attack paths:

• build cheaper alternatives (10x cheaper)

• bundle multiple tools into one

• Rethink workflows entirely with AI-native design

The next generation of big software companies will likely come from here.

5. AI is moving into the physical world

A lot of founders still think AI = software. YC is clearly saying that’s incomplete. Some of the biggest opportunities are in:

• agriculture (precision farming, low pesticide systems)

• robotics and physical AI

• hardware supply chains

• space infrastructure

• semiconductor systems

The reason is simple: software is getting easier… but atoms are still hard.

So whoever combines AI with real-world systems - sensors, robotics, energy, manufacturing - gets a much stronger moat.

6. New infrastructure layers are being built

Several ideas point to a deeper shift: entirely new infrastructure is needed for this AI world. Examples YC highlights:

• Inference chips built specifically for agent workflows

• semiconductor supply chain visibility tools

• AI operating systems for companies

• dynamic software interfaces that users can reshape

These aren’t features - they’re foundational layers.

And historically, building at the infrastructure layer is where massive value gets created.

7. Selling to big companies just got easier

One underrated shift YC calls out: early-stage startups can now sell directly to Fortune 100 companies. That used to be nearly impossible.

Now:

• small teams can ship enterprise-grade products quickly

• buyers are actively looking for AI solutions

• deals are closing earlier (even during YC batches)

This flips an old startup rule on its head. Instead of avoiding big companies… you can build for them from day one.

What this actually means for founders

If you step back, the RFS isn’t just a list of ideas - it’s a map of where things are going:

• AI is collapsing the cost of building software

• value is shifting from tools → outcomes

• data alone isn’t enough — structured context is the moat

• agents will become primary users of software

• physical-world applications will define the next frontier

• infrastructure layers are being rebuilt from scratch

And maybe the most important takeaway:The next wave of great companies won’t look like SaaS companies. They’ll look like:

• service providers powered by AI

• systems that run entire workflows

• infrastructure that other AI systems depend on

• companies that blend software + hardware + data

That’s the direction YC is pointing toward. And historically, when YC starts talking like this… it’s usually early, not late.

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 a $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.

NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Mighty Capital, a San Francisco, CA-based venture capital firm specialising in product-led investing, closed Fund III at $91m. (Link)

• Oncology Ventures, an Austin, TX-based venture capital firm investing in startups transforming cancer care, closed its $62m Fund II. (Link)

• Eclipse, a Palo Alto, CA-based venture capital firm, raised $1.311 billion for two funds. (Link)

• Futurepresent, a NYC, Berlin, and Munich, Germany-based venture capital firm backing AI startups in the US and Europe, launched with its first $300M vehicle. (Link)

• Zero Shot, a U.S.-based VC fund founded by former OpenAI leaders, has raised $20M in its first close toward a $100M target to invest in early-stage AI startups. (Link)

New Startup Deals

• Iridius, a Seattle, WA-based compliance-by-design AI platform, raised $8.6M in Seed funding. (Link)

• RE Joule (VREY), a Berlin, Germany-based solar solutions startup, raised €3.3M in Seed funding. (Link)

• Astor, a San Francisco, CA-based AI-native investment advisory platform, raised $5M in Seed funding. (Link)

• Ferrosa Therapeutics, a Basel, Switzerland-based biotech company, raised $3.5M in Seed funding. (Link)

• DOJO AI, a London & Lisbon-based agentic marketing platform, raised $6M in Seed funding. (Link)

• Almanack Health, a Boston, MA-based clinical AI platform, raised $10M 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)

• Partner 18, Healthcare - a16z | USA - Apply Here

• Associate - DN Capital | Germany - Apply Here

• Investment Intern - DTCP | UK - Apply Here

• Venture Scout - First Momentum Venture | UK - Apply Here

• Analyst, Global Investment Team - 500 Global | USA - Apply Here

• Associate, Data Operations - Iconiq Capital | USA - Apply Here

• Investor - AI - Samsung Next | USA - Apply Here

• Finance Associate - RA Capital | USA - Apply Here

• Fund Controller - NFX | USA - Apply Here

• Vice President, Investor Relations - General Atlantic - Apply Here

• Senior Associate - RA Capital | USA - Apply Here

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

• Partner 22 -a16z | USA - Apply Here

• Infra / Platform Engineer - Pear VC | USA - Apply Here

• Associate / Senior Associate - Stepstone Group | Italy - Apply Here

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👋 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 price your AI product in a post-SaaS world?

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

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

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Mode Mobile recently received their ticker reservation with Nasdaq ($MODE), indicating an intent to IPO in the next 24 months. An intent to IPO is no guarantee that an actual IPO will occur.

The Deloitte rankings are based on submitted applications and public company database research, with winners selected based on their fiscal-year revenue growth percentage over a three-year period.

VENTURE CURATORS’ FINDING

📬 My Favourite Finds

Media Posts:

• How to angel invest (even if you don’t have money) (Link)

• Excel template: early-stage startup financial model for fundraising. (Link)

• Tim Cook is stepping down as CEO. Meet John Ternus, the new CEO of Apple. (Link)

• Jeff Bezos is raising $10B for a new AI lab quietly targeting industrial AI. (Link)

Reports/Articles:

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

• VC in 2026: 75% of all the Money Is Going to just 5 VC Funds. And To Just 5 “Startups.” (Link)

• How George Orwell predicted the rise of “AI Slop” in nineteen eighty-four (1949) (Link)

• The growth metric Silicon Valley loves most is also its least trusted. (Link)

• Deedy Das visually clusters what VCs are investing in right now (Link)

📜 DEEP DIVE

How to price your AI product in a post-SaaS world?

Most founders are thinking about AI pricing completely wrong.

They obsess over features, distribution, and even model performance, but treat pricing like a packaging decision. In the AI era, pricing isn’t packaging. It’s a strategy. If you get it wrong, your growth can look impressive while your margins quietly collapse.

This AI pricing playbook from Bessemer Venture Partners lays out something many founders don’t want to admit: AI businesses are structurally different from SaaS, and that changes how you monetise.

AI broke SaaS economics

In classic SaaS, marginal cost was almost zero. Once the software was built, adding one more user barely cost anything. That’s why founders could aggressively chase ARR growth and worry about margins later.

AI changes that equation. Every query carries real costs:

• Inference and computation

• GPU usage

• Model upgrades

• Sometimes human review

• Ongoing support overhead

This means gross margins in AI often sit in the 50-60% range (sometimes lower), compared to 80–90% in SaaS. If your pricing model doesn’t reflect this from day one, scaling only amplifies the problem.

One practical test: if your unit economics don’t work with 10 customers, they won’t magically work with 1,000. Many startups scale into negative margin territory because they never fully modelled their true costs, including founder time and hidden support work.

That’s the first mindset shift: pricing must absorb cost reality while capturing value.

The real strategic choice: what are you charging for?

Most AI founders jump to usage pricing because it mirrors their cost structure. But pricing isn’t about your costs, it’s about how customers perceive value.

Across Bessemer’s research and portfolio companies, three AI-native business models are emerging. Each one changes how value is delivered, and therefore how pricing must work.

Before we go deeper into charge metrics, here’s how the landscape is actually shaping up:

1. Consumption-based (tokens, API calls, usage)

Clean and predictable for infrastructure companies. Works well for technical buyers who understand granular usage. But non-technical customers don’t think in tokens; they think in outcomes.

2. Workflow-based (per task completed)

Charging per document drafted, per ticket resolved, per case processed aligns better with how businesses measure work. It reduces translation friction and makes ROI clearer.

3. Outcome-based (per successful result)

The boldest version. You get paid only when the job is done. Intercom charging per AI resolution is a strong example - not per message, but per resolved ticket.

As you move from consumption → workflow → outcome pricing, you accept more cost variability but gain stronger alignment with customer value.

The best founders don’t choose what’s easiest to implement; they choose what customers emotionally understand and are willing to pay for.

The soft ROI trap

Here’s where many AI companies will struggle in 2026.

Copilots that “assist” but don’t close the loop create soft ROI. They help, suggest, draft, but don’t finish the job. When renewal time comes, customers ask: “Did this actually move the needle?”

Agentic products that fully complete workflows have stronger pricing power because their ROI is measurable.

You can think of AI products across two dimensions:

• Revenue generation vs. cost reduction

• Hard ROI vs. soft ROI

Products sitting in hard ROI territory, clear revenue uplift or measurable cost savings, can justify premium pricing. Soft ROI products will face budget scrutiny once the hype fades.

That’s a practical insight founders should act on now, not later.

Hybrid pricing: the early-stage advantage

For most startups, especially early on, hybrid models are a smart middle ground:

• Base platform fee (predictability for the customer)

• Plus usage or outcome-based expansion (upside capture)

This protects you from unpredictable compute spikes while allowing revenue to scale with value delivered. It also gives enterprise buyers budget clarity, which matters more than founders assume.

Hybrid models are not a compromise; they’re a hedge against uncertainty in both demand and cost structure.

Pricing shapes your entire company

One of the strongest insights: pricing isn’t a finance decision. It determines how your company behaves.

If you charge per outcome:

• Sales sell results, not seats

• Customer success focuses on realised value

• Product optimises for completion and accuracy

• Engineering prioritises reliability

If you charge per seat:

• Teams may optimise adoption over actual impact

• Value conversations get diluted

Pricing becomes your operating system.

How to actually find your price

Most founders undercharge. Not because they lack data, but because asking for more feels uncomfortable.

The playbook recommends something counterintuitive: find your pricing through friction.

• Start with a price.

• If customers immediately say “yes,” you’re likely to be too cheap.

• Raise incrementally until you hear “we need to think about that.”

• Stop just before it becomes a blocker.

That tension zone is often where true willingness-to-pay lives.

And don’t default to cost-plus pricing. If you’re creating $100K in measurable value and charging $12K because it “covers compute,” you’re anchoring to cost instead of value.

The bigger shift is underway

Every major software shift changed monetisation:

• Client-server monetised licenses

• SaaS monetised access

• AI is monetising outcomes

AI isn’t just another feature layer. In many cases, it’s replacing or autonomously completing work. That means charging for access alone underestimates what your product is actually doing.

The founders who win in this era will be the ones who design pricing systems that reflect:

• Real compute economics

• Clear customer ROI

• Scalable expansion logic

• Operational simplicity

AI pricing is not about clever billing mechanics.

It’s about making sure your product earns what it’s worth and that your margins survive long enough to compound.

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📃 QUICK DIVES

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.

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 18, Healthcare - a16z | USA - Apply Here

• Associate - DN Capital | Germany - Apply Here

• Investment Intern - DTCP | UK - Apply Here

• Venture Scout - First Momentum Venture | UK - Apply Here

• Analyst, Global Investment Team - 500 Global | USA - Apply Here

• Associate, Data Operations - Iconiq Capital | USA - Apply Here

• Investor - AI - Samsung Next | USA - Apply Here

• Finance Associate - RA Capital | USA - Apply Here

• Fund Controller - NFX | USA - Apply Here

• Vice President, Investor Relations - General Atlantic - Apply Here

• Senior Associate - RA Capital | USA - Apply Here

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

• Partner 22 -a16z | USA - Apply Here

• Infra / Platform Engineer - Pear VC | USA - Apply Here

• Associate / Senior Associate - Stepstone Group | Italy - Apply Here

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Find the right investors to pitch - with Evalyze

Most founders pitch the wrong investors and waste weeks sending cold emails that go nowhere.

I’ve seen this happen a lot - the problem usually isn’t your startup. It’s not knowing who’s actually a fit.

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👋 Hey, here’s today at a glance -

Big idea + report of the week :

• Stanford AI Index Report reveals: Is AI progress plateauing or quietly compounding?

• Do you really need Stanford or Harvard to build a unicorn?

Frameworks & insightful posts :

• Is your AI feature quietly killing your saas margins?

• This is the safest job in the age of AI - Shopify team analysis?

• Is the traditional seed investing playbook already broken?

START WITH

🧠 Big idea + report of the week

Stanford AI Index Report reveals: Is AI progress plateauing or quietly compounding?

There’s a narrative floating around that AI might be plateauing. That we’ve seen the peak, and now progress will slow.

The 2026 AI Index Report from Stanford tells a very different story.

If anything, AI isn’t slowing - it’s compounding across every layer at once: capability, adoption, investment, and global competition. And the gap between perception and reality is widening fast.

Start with the most important signal: capability.

In 2025, 90%+ of frontier AI models were built by industry, not academia.

And these models aren’t just incremental upgrades anymore. They’re now solving PhD-level science questions, performing near-perfectly on coding benchmarks, and even winning gold-medal-level math competitions.

But at the same time, they still fail at simple tasks like reading analogue clocks reliably.

That contradiction is what researchers call the “jagged frontier.” Progress isn’t linear- it’s uneven, unpredictable, and happening in bursts. Which makes it harder to evaluate how “good” AI really is.

Now layer in adoption.

AI has reached 53% population adoption in just 3 years, faster than both the internet and the PC.

In parallel, 88% of organisations are already using AI, and 4 out of 5 university students rely on it regularly.

This is no longer early adoption. This is infrastructure-level penetration.

And interestingly, much of this value is being created before it’s fully monetised. In the U.S. alone, generative AI tools are estimated to deliver $172B in annual consumer value, much of it through free or underpriced products.

Then comes the geopolitical layer.

The long-standing assumption that the U.S. is far ahead in AI is no longer true in the same way.

• The U.S. still leads in top-tier models and private investment

• But China has closed the performance gap significantly

• In some periods, Chinese models have matched or even led U.S. models

• China dominates in research output, patents, and industrial deployment

At the same time, the entire AI supply chain depends heavily on a single chokepoint: TSMC in Taiwan, which manufactures the majority of advanced AI chips.

So while software is globalising, hardware remains highly concentrated, creating a very different kind of risk.

Another underappreciated shift is happening in talent.

Despite leading in funding and startups, the U.S. is losing its dominance in attracting global AI talent, with an 89% decline in researcher inflow since 2017. Meanwhile, countries like the UAE, Chile, and South Africa are seeing rapid growth in AI skill development.

This suggests the next wave of AI innovation may be far more geographically distributed than the last. But not everything is keeping pace.

This report highlights a growing gap between AI capability and AI responsibility.

• AI incidents jumped sharply to 362 in 2025

• Most companies report performance benchmarks - but not safety benchmarks

• Improving one dimension (like safety) can degrade another (like accuracy)

In other words, we’re scaling our capability faster than we’re managing its consequences. And finally, there’s the perception gap.

AI experts are overwhelmingly optimistic - 73% believe it will positively impact jobs. But only 23% of the general public agrees. That 50-point gap reflects something deeper: people are experiencing AI differently than those building it.

Add to that fragmented global trust in institutions, and you get a landscape where technology is advancing faster than consensus around it.

Put all of this together, and the picture becomes clearer. AI isn’t one trend. It’s multiple curves compounding at once:

• Capability is improving rapidly but unevenly

• Adoption is spreading faster than any prior technology

• Investment concentrating heavily in a few regions and companies

• Talent and participation are slowly globalising

• Governance and trust lag behind

For founders and investors, this changes how you read the market. It’s no longer about asking, “Is AI big?” That’s already settled.

The real questions are:

• Where is the next layer of value forming?

• Which parts of the stack are underbuilt relative to adoption?

• And where are the bottlenecks that will define the next phase?

Because in cycles like this, the biggest opportunities don’t come from following the trend. They come from understanding where the trend is about to break next.

Do you really need Stanford or Harvard to build a unicorn?

There’s a strong belief in startups that the path to building a billion-dollar company runs through a handful of elite universities.

Stanford. Harvard. MIT. And to be fair, the data does show they matter.

In an expanded analysis covering unicorn founders from 2017 to 2025, Stanford’s Ilya Strebulaev tracked which universities consistently produce founders of billion-dollar companies.

Only three schools showed up in the top 10 every single year across all nine vintages: Stanford, Harvard and MIT

Stanford alone took the #1 spot in six out of nine years, which tells you how deeply embedded it is in the startup ecosystem.

But the more interesting insight isn’t that these schools dominate. It’s that they don’t dominate as much as people think.

When you zoom into the 2025 ranking, the picture becomes much more distributed:

• Stanford and Harvard still lead

• But schools like Cornell, UT Austin, Michigan, and UC San Diego are right there in the top 10

• Public universities are consistently producing meaningful numbers of unicorn founders

• The Ivy League advantage is real - but far less concentrated than brand perception suggests

Even historically, schools like Berkeley - which appeared in 8 out of 9 years - show that proximity to ecosystems can matter as much as prestige itself.

There’s also a global layer that often gets overlooked. A few non-US universities show up repeatedly across vintages: Tel Aviv University, Technion (Israel Institute of Technology) and University of Alberta.

These aren’t random appearances. They reflect something structural.

Israel continues to produce a disproportionate number of founders in deep tech and cybersecurity. Canada, especially through places like Alberta, is quietly becoming a major hub for AI research and talent.

So what does this actually tell you? The naive takeaway would be: “Top schools increase your chances.”

That’s true - but incomplete. The deeper takeaway is about the environment, not just education.

Top universities tend to provide:

• Access to ambitious peers

• Early exposure to cutting-edge research

• Proximity to capital and startup ecosystems

• A culture that normalises building companies

But those advantages are no longer exclusive. They’re increasingly being replicated across other universities and geographies. That’s why you’re seeing a broader distribution of founders over time.

For founders, this should be reassuring. Your university might influence your starting point - but it doesn’t define your ceiling.

What matters more is whether you can access the same underlying advantages:

• Are you close to people building interesting things?

• Are you plugged into emerging ideas and technologies?

• Are you operating in an environment that compounds ambition?

Because the data is clear. Stanford, Harvard, and MIT may consistently show up. But they’re no longer the only places where unicorn founders are built. And over time, that gap is only getting smaller.

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SOMETHING MORE

🧩 Frameworks & insightful posts

Is your AI feature quietly killing your saas margins?

Most founders think about AI like a product upgrade. Better outputs. Better UX. Higher engagement.

But very few think about what it’s doing to their P&L.

Ben Murray (The SaaS CFO) recently highlighted a mistake that’s becoming common across AI-native and AI-enabled SaaS companies: treating AI costs like traditional SaaS COGS.

That assumption breaks your financial model faster than you expect.

Traditional SaaS was built on a simple foundation - high gross margins and low marginal cost.

You build the product once, serve many customers, and as you scale, your margins expand. That’s why 70-80% gross margins became the benchmark.

AI changes that dynamic. Every prompt, every generation, every workflow now carries a real, variable cost - inference, routing, vector databases, infrastructure. What used to be mostly fixed now behaves like a usage-sensitive cost layer.

And the impact is already showing up in the data.

• Traditional SaaS: ~70–80% gross margins

• AI-heavy SaaS (2026 estimate): ~52% gross margins

That gap isn’t small. It’s structural. The tricky part is how quietly this happens.

Teams ship AI features, see usage spike, celebrate adoption… and only later realise their cost to serve has fundamentally changed.

A simple example makes this clear.

You have a SaaS product doing $100 in revenue with $20 in COGS → 80% margin.

Now you layer AI features that add $15 in variable cost.

Same revenue. New COGS = $35. Your margin drops to 65% - instantly.

And that’s before: heavy users scale usages, poor prompt design increases cost, expensive models get overused, and pricing doesn’t reflect usage.

You’ve improved the product - but weakened the business.

This is why AI COGS is not just a finance detail. It’s the core lever that affects everything downstream: CAC payback, burn efficiency, Rule of 40 and ultimately, valuation

If gross margin compresses, every other metric gets tighter.

Where most companies go wrong is in visibility. They bury AI costs inside a generic “hosting” or “DevOps” bucket, which makes it impossible to answer a critical question:

What is the actual margin of the AI-driven part of the product?

That’s why the structure of your P&L suddenly matters a lot more. At a minimum, you need clarity across:

• revenue streams (subscription vs usage vs services)

• corresponding COGS layers

• margins by each revenue stream

Because what’s happening in many companies right now is:

• legacy SaaS → still high margin

• AI layer → materially lower margin

And the blended number hides the problem.

Another shift that founders underestimate is pricing.

If your cost scales with usage, your pricing has to acknowledge it - even if you don’t expose raw token pricing. The models that are emerging look like:

• platform fee + usage layer

• tiered usage buckets or credits

• overage pricing or soft caps

• feature-based packaging for heavier AI workflows

The goal is simple: price on value, but protect against heavy usage that destroys margin.

Because AI cost problems don’t show up for average users. They show up in power users and edge cases.

But even with pricing fixes, investors don’t expect perfection right now. They expect clarity. What matters is whether you can show a credible margin roadmap:

• better model routing

• using smaller models where possible

• prompt optimization

• caching and infra efficiency

• better packaging and higher ACVs

The point isn’t “we’ll get back to 80% margins tomorrow.” It’s showing you understand the economics and are actively improving them.

One subtle but important metric shift here: Don’t just track average cost. Track distribution.

• top-decile users vs median users vs low-usage users

Because a small group of heavy users can quietly destroy your margins while averages still look fine.

Sp, AI is not just a product decision. It’s an economic shift. You can build something far more valuable and still end up with a worse business if you don’t adjust your model.

So the right questions aren’t: “Do customers want this feature?” or “Is adoption growing?”

They are:

• Where does this sit on the P&L?

• How does it change our gross margin?

• Is pricing aligned with usage and cost?

Because the companies that win in AI SaaS won’t just be the ones shipping the fastest.

They’ll be the ones who understand the economics well enough to scale it profitably.

Is entrepreneurship actually safer than a 9-5 in the age of AI?

For years, the default advice was simple: take the stable job, avoid the risk, and maybe start something on the side later.

That logic is starting to break.

A recent analysis by Shopify’s Data Science team - based on millions of merchants across 175 countries - paints a very different picture. The data doesn’t just suggest that entrepreneurship is growing. It suggests the risk equation itself is shifting.

The first signal is hard to ignore.

More people are starting businesses than ever before.

Since 2018, the number of Shopify merchants making their first sale has increased 7x. And this isn’t happening in isolation. It’s happening alongside a weakening traditional job market. In 2025 alone, U.S. companies announced 1.2 million job cuts, with AI cited as the primary reason in a significant share of them.

You don’t need to over-interpret the causation here to see the pattern:

• Traditional employment is getting more uncertain

• Entrepreneurship is getting more accessible

When you make something easier, you get more of it. That’s exactly what’s happening with building businesses. But the more interesting insight isn’t just that more people are starting.

It’s that people who do it again get significantly better.

Repeat founders on Shopify generate more than 2x the sales per shop compared to first-time founders. And this aligns with broader research - entrepreneurial performance compounds over time.

This is where the “entrepreneurship is too risky” argument starts to weaken.

Because, unlike traditional jobs - where skills can become obsolete - entrepreneurial skills tend to compound:

• finding customers

• identifying opportunities

• operating under uncertainty

• building distribution

These don’t reset. They stack.

The first venture is the hardest. But each one after that becomes more informed, faster, and often more successful.

The third shift is even more subtle, but powerful.

Entrepreneurs aren’t just starting more - they’re earning more over time.

Shopify merchants who started between 2017 and 2020 saw their average sales grow 25% between 2022 and 2025. This isn’t just individual improvement. It’s also because the underlying market is expanding.

E-commerce itself has grown from 14% of global retail in 2019 to over 20% in 2025, and Shopify now powers more than 14% of U.S. e-commerce.

So founders today aren’t fighting for a fixed pie. They’re building inside a growing one.

And that’s the real shift most people are missing. Entrepreneurship used to be risky because:

• Tools were limited

• Distribution was expensive

• operations were complex

Now, a lot of that friction has collapsed. AI tools, better logistics, global payments, and platforms like Shopify have effectively turned entrepreneurship into something closer to a skill-driven system rather than a pure gamble.

So the takeaway isn’t that entrepreneurship is easy. It’s that the definition of “safe” is changing.

The traditional path is no longer as stable as it once was. And the alternative path - building something of your own - is no longer as unpredictable as it used to be.

More people are choosing it. The ones who repeat it are getting better at it. And over time, they’re earning more from it.

The safest job used to be working for someone else. Now, increasingly, it might be learning how to build for yourself.

Is the traditional seed investing playbook already broken?

For years, early-stage investing followed a simple belief:

Invest early. Invest cheap. Diversify widely. And let one 1000x outlier return the fund.

That model is quietly breaking.

Lucas Vaz recently laid this out using fresh data, and the shift is hard to ignore. Seed valuations have exploded. The top 5% of seed rounds are now regularly happening at $150M–$175M+ valuations, up nearly 3x in a short period. Even median-ish rounds have moved up meaningfully.

At the same time, something else is happening, and it’s even more important. The biggest winners are no longer returning what they used to.

Take the OpenAI example.

Early investors collectively put in around $10M and are now sitting on roughly $1.4B in paper value. That’s ~140x.

In any other era, that sounds incredible. But for venture math, it’s not enough.

If your fund had only a small position in the biggest company of the decade, even a 140x outcome may not return your fund meaningfully. The combination of high entry prices, heavy dilution, and massive capital flowing into winners is compressing returns at the very top.

Which leads to a non-obvious conclusion:

The era of 1000x outcomes isn’t disappearing because companies are weaker.
It’s disappearing because the structure of venture has changed.

Most emerging managers react to this the same way. They go earlier. They go cheaper. They try to find $5M0$10M entry points where big funds aren’t competing.

That feels like a smart adjustment. Lucas argues it’s a trap. Because the market isn’t just getting expensive - it’s splitting.

What’s happening is a clear bifurcation of the venture ecosystem. At the top, a small set of companies:

• raise at high valuations

• have real traction or strong signals early

• attract top-tier capital immediately

• compound advantages through network, talent, and follow-on access

These are not “overpriced hype deals.” They’re increasingly predictable winners with clearer paths to scale.

Below that is a much larger pool of companies that look cheap - but structurally struggle to break out. They might go from: $5M → $25M → $100M on paper

But paper markups are not real outcomes. Most of these companies never reach meaningful liquidity - not because they’re bad, but because:

• Capital is concentrating on fewer winners

• Talent follows those winners

• The bar for Series A keeps rising

• Exit markets (IPO/M&A) are tighter than before

So what looks like a “cheap entry” is often just a value trap. This is where most portfolio strategies break. The old model said: diversify broadly and wait for one outlier.

But if outliers now return 50–150x instead of 1000x, that math stops working. Because:

• a 3% position needs ~30x+ to return the fund

• A small ownership in a winner isn’t enough anymore

• Spreading capital across 40–50 bets dilutes impact

So the shift isn’t just about valuation. It’s about portfolio construction.

The new reality is much simpler - and harder. You need to make money on 10–20x outcomes. Not hope for a miracle.

That changes how you think about everything:

• price becomes relative to quality, not absolute cheapness

• Concentration matters more than ownership %

• conviction matters more than diversification

Owning 10% of a mediocre company doesn’t matter. Owning 2-3% of a real $5B+ outcome can.

This is why “fund size is your strategy” is also flawed thinking. Smaller funds often default to lower prices to get higher ownership. But ownership alone doesn’t create returns. What matters is:

• the quality of the company

• probability of a large outcome

• And how much of your fund is allocated to it

In every other asset class, this is obvious. In venture, it’s often ignored. So what actually works now? The path forward is narrower — but clearer.

You need to operate in two lanes at once:

1. Get into the best companies you have the right to access

Even if they’re expensive. Even if ownership is lower initially. Because these are the only assets with real path-dependency and liquidity potential.

2. Find high-conviction opportunities before the market sees them

Your edge gives you early insight and then helps those companies become “obvious” to the next round of capital.

And most importantly - size your bets correctly. Because the biggest mistake now isn’t missing deals. It’s under-allocating to the right ones.

So, the old venture playbook optimised for randomness. Cheap entries. Wide portfolios. Hope for a breakout. The new environment rewards precision. Fewer bets. Higher conviction. Willingness to pay for quality.

Because in this market, picking well isn’t just an advantage anymore. It’s the entire game.

NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Treehub & AI Health Fund, U.S.-based healthcare-focused initiatives launched by Mary Minno, are building an accelerator + venture model to back early-stage AI healthcare startups. (Link)

• Sideline Group, a NYC-based investment firm that partners with brands and experiences at the intersection of consumer, sports, and media & entertainment, closed its debut fund, Sideline Group Fund I, with $155 million in committed capital. (Link)

• Accel, a global venture firm, has raised $5B in fresh capital to back late-stage startups, with $4B allocated to its Leaders Fund and $650M to a sidecar vehicle. (Link)

• A‑Street, a Bentonville, Ark.-based multi-stage investment fund focused on improving PK-12 student learning and achievement, announced a $675m capital raise. (Link)

• Eclipse, a Palo Alto, CA-based venture capital firm, raised $1.311 billion for two funds. (Link)

• Futurepresent, a NYC, Berlin, and Munich, Germany-based venture capital firm backing AI startups in the US and Europe, launched with its first $300M vehicle. (Link)

• Zero Shot, a U.S.-based VC fund founded by former OpenAI leaders, has raised $20M in its first close toward a $100M target to invest in early-stage AI startups. (Link)

New Startup Deals

• 10x Science, a San Francisco, CA-based AI protein research startup, raised $4.8M in Seed funding. (Link)

• Nox Mobility, a Berlin, Germany-based night train company, raised €2M in Pre-Seed funding. (Link)

• VAST Data, a NYC-based AI operating system company, raised ~$1B in Series F funding at a $30B valuation. (Link)

• Ray Therapeutics, a Berkeley-based biotech company, raised $125M in Series B funding. (Link)

• Coral, a NYC-based healthcare automation platform, raised $12.5M in funding. The round was led by Lightspeed and Z47. (Link)

• Ultralight, a San Francisco-based AI healthcare OS startup, raised $9.3M in Seed funding. (Link)

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• Partner 18, Healthcare - a16z | USA - Apply Here

• Associate - DN Capital | Germany - Apply Here

• Investment Intern - DTCP | UK - Apply Here

• Venture Scout - First Momentum Venture | UK - Apply Here

• Analyst, Global Investment Team - 500 Global | USA - Apply Here

• Associate, Data Operations - Iconiq Capital | USA - Apply Here

• Investor - AI - Samsung Next | USA - Apply Here

• Finance Associate - RA Capital | USA - Apply Here

• Fund Controller - NFX | USA - Apply Here

• Vice President, Investor Relations - General Atlantic - Apply Here

• Senior Associate - RA Capital | USA - Apply Here

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

• Partner 22 -a16z | USA - Apply Here

• Infra / Platform Engineer - Pear VC | USA - Apply Here

• Associate / Senior Associate - Stepstone Group | Italy - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

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👋 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.

• 5 predictions that reveal where AI agents will actually make money.

• Wispr Flow’s framework to reach 100,000+ daily users - The CEO’s exact consumer playbook.

• Mark Cuban’s AI Thesis: The $495B AI opportunity that most founder missing.

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VENTURE CURATORS’ FINDING

📬 My Favourite Finds

Media Posts:

• It’s time to expose a huge scam in AI startups: Contracted ARR. (Link)

• Excel template: early-stage startup financial model for fundraising. (Link)

• Tim Cook is stepping down as CEO. Meet John Ternus, the new CEO of Apple. (Link)

• Jeff Bezos is raising $10B for a new AI lab quietly targeting industrial AI. (Link)

Reports/Articles:

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

• The SaaS Rout of 2026 is even worse than you think? (Link)

• Venture Capital has a starting line problem. (Link)

• The growth metric Silicon Valley loves most is also its least trusted. (Link)

• Deedy Das visually clusters what VCs are investing in right now (Link)

📜 DEEP DIVE

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.

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📃 QUICK DIVES

Wispr Flow’s framework to reach 100,000+ daily users - The CEO’s exact consumer playbook.

Most consumer startups don’t fail because the UI was ugly or the ads didn’t convert. They fail because they never became a habit.

Tanay Kothari, CEO of Wispr Flow (raised $81M), recently shared the internal playbook behind scaling to 100,000+ daily active users and over 10 billion words dictated. What’s powerful is not that it’s complicated. It’s that it’s simple, but brutally disciplined.

If you’re building a consumer, this is worth studying carefully.

(Wispr Flow is voice-first productivity software that helps people write, code, message, and work hands-free by speaking instead of typing )

1. Psychology: You can only change one behaviour at a time

This is the most expensive lesson for consumers.

Before Wispr Flow became software, the team built a hardware earpiece that could read neural signals from silent speech. It worked. Technically impressive. But it required two behaviour changes:

• Wear a new device

• Speak instead of typing

That’s where most products die.

Look at the pattern: Humane AI Pin asked users to wear a new device and interact with a projector. Google Glass asked users to wear a camera and talk in public.

Two behaviour shifts at once are too much cognitive friction.

When they pivoted to software, they asked for exactly one behaviour change: speak instead of type. Everything else stayed the same: same apps, same workflows, same screen.

That constraint became the growth engine.

90% of their growth is word of mouth. Not because of virality tricks but because they engineered around one psychological moment.

2. Product: Dependency beats demo

Most products impress in a demo. Very few become dependent. Two things moved the needle for Wispr.

First: they talked to 500+ people.

Not surveys. Not Typeform. Real conversations. Watching users struggle with existing dictation tools. Noting the exact moment frustration hit.

That’s where product clarity came from.

Patterns became obvious:

• Names are constantly misspelt

• Tone inconsistencies

• Tools failing when users corrected themselves mid-sentence

Every core feature traces back to something someone said in those conversations.

Second: they built a product that learns about the user.

Not just AI dictation. Personalization:

• Personal dictionary — if you fix a word once, it remembers

• Tone controls — match capitalization, punctuation, style

• Self-correction — “Let’s meet tomorrow, no wait, Friday” outputs correctly

These seem small. But they signal care.

And care compounds.

When users feel that a product improves with them, it stops feeling like software and starts feeling like infrastructure.

3. Team: How 200 people out-ship much larger companies

The product doesn’t matter if the team moves slowly.

Wispr’s team principles are unusually sharp.

1. Two-person pods: Two engineers per project. Full ownership. No handoffs. No ambiguity.

Communication overhead scales exponentially with team size. Pods contain it.

2. Hire ex-founders Not for prestige. For instinct.

Ex-founders don’t file tickets. They fix problems. They can’t ignore broken things.

That ownership bias compounds velocity.

3. Hire for taste, not just skill

In consumer, taste is leverage.

• The person who knows an animation is 50ms too slow.

• The designer who rejects something technically correct but emotionally wrong.

• The engineer who refactors invisible code because it feels wrong.

You can train skill. Taste is rare.

The underlying pattern

What’s striking is that none of this is a growth hack.

It’s alignment.

• Psychology → one behaviour change

• Product → remove friction repeatedly

• Team → maximise ownership and taste

When these align, you don’t need constant marketing tricks. The product carries itself.

And the most important line Tanay shared:

Don’t build something people want. Build something people can’t live without.

Consumer products don’t win on features. They win on feelings. And the companies that understand that gap build habits instead of installs.

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

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• Associate - DN Capital | Germany - Apply Here

• Investment Intern - DTCP | UK - Apply Here

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• Associate, Data Operations - Iconiq Capital | USA - Apply Here

• Investor - AI - Samsung Next | USA - Apply Here

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• Fund Controller - NFX | USA - Apply Here

• Vice President, Investor Relations - General Atlantic - Apply Here

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• Partner 22 -a16z | USA - Apply Here

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Big idea + report of the week :

• Are $175M seed valuations the new normal?

• Do analysts actually become partners?: What 12,000 VC careers reveal about making partner.

Frameworks & insightful posts :

• Where is the $435b AI market really making money?

• a16z on AI pricing wars: what AI startups get wrong about enterprise pricing?

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

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• See where competitors get their traffic from

• Identify their top-performing pages and keywords

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• Find gaps you can capture immediately

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START WITH

🧠 Big idea + report of the week

Are $175M seed valuations the new normal?

It’s starting to feel familiar again. The speed. The pricing. The conviction at the very top of the market.

And the data now backs that feeling.

Peter Walker, Head of Insights at Carta, recently shared updated numbers showing that top 5% seed valuations have surged to ~$175M - nearly 3x growth in just 12 months. Even for a market that’s used to cycles, this kind of acceleration stands out.

But what’s more interesting isn’t just how high valuations have gone.

It’s how unevenly they’re distributed.

At the very top of the market, things look almost surreal:

• $100M–$175M seed rounds are getting done in days, sometimes hours

• Top-tier founders (especially in AI) are raising pre-product or early traction

• Large funds are aggressively moving into seed, compressing timelines and pushing prices up

• Valuations are being set based on perceived future dominance, not present metrics

This is the part of the market everyone sees and reacts to. But step back, and a very different picture emerges.

The broader seed market hasn’t moved nearly as dramatically.

• Median valuations are still relatively stable and disciplined

• Most companies are still raising based on traction, not narrative

• Capital is more selective outside the top tier

What this creates is a massive bifurcation.

The “average” seed valuation has effectively become meaningless because it blends two completely different markets:

1. A small group of AI-driven outliers is pulling valuations sharply upward

2. A much larger base of companies operating under normal constraints

And the gap between these two is widening faster than ever.

Walker himself describes it as having “a whiff of 2021-era ridiculousness.”

The pattern does feel similar:

• Mega funds are pushing earlier into the stack

• Deals are closing faster than diligence cycles

• Secondary activity increasing

• Narrative and momentum are playing a larger role in pricing

But there’s one key difference this time. The underlying technology, AI, is actually delivering real traction.

And that’s what makes this cycle more complex. Because both of these things can be true at the same time: AI is a genuinely transformative shift, and the capital flowing into it can still be mispriced.

That duality is what founders and investors need to understand. The biggest risk right now isn’t just overvaluation. It’s misreading the market you’re in.

If you’re a founder, the takeaway isn’t “raise at $100M+, or you’re behind.”

It’s understanding that:

• Those deals exist - but they’re outliers

• They come with extremely high expectations

• And they often require growing into a narrative that hasn’t played out yet

If you’re an investor, the question shifts as well. It’s no longer just about getting access. It’s about asking:

• Are you underwriting real outcomes or just momentum?

• Is this valuation justified by category potential or driven by competition?

• And most importantly, are you investing in a market… or in a moment?

Because cycles like this don’t break evenly. They correct at the edges first. And right now, the edge of the market is exactly where the most capital is flowing.

Do analysts actually become partners, or is the VC career path broken from the start?

Most people trying to break into venture think the safest path is obvious: start as an analyst, work your way up, and eventually make partner.

But the data suggests something very different.

In one of the largest studies of VC careers ever conducted, Stanford’s Ilya Strebulaev analysed 12,600+ venture professionals over nearly three decades. And the conclusion is uncomfortable for anyone planning a traditional path into VC:

The biggest predictor of making partner isn’t skill, pedigree, or even deal success.

It’s where you enter the industry. If you start too junior, the odds are stacked against you from day one.

The data shows that professionals who enter VC at the analyst or associate level are 70-80 percentage points less likely to become partners compared to those who join at mid-level roles like VP or Principal.

That’s not a small gap. That’s structural.

Which means the “start early and climb” narrative is often misleading. In reality, many partners don’t grow up inside VC—they enter it laterally after building credibility elsewhere.

And when you look at what actually increases your odds, a pattern starts to emerge. Operational experience matters more than almost anything else.

People who have worked at VC-backed startups - whether as founders, CXOs, or even early employees are significantly more likely to make partner. They’ve seen company building firsthand, understand decision-making under uncertainty, and bring context that pure investors often lack.

In contrast, purely financial or academic paths don’t perform as strongly as people expect.

• MBAs increase your chances of getting promoted

• But they actually correlate with worse investment outcomes on average

• Non-MBA advanced degrees (MS/PhD) perform better across both promotion and deal success

• Only top-tier MBAs (Stanford, HBS) seem to offset this gap

This creates a strange dynamic inside VC firms. The people most likely to get promoted aren’t always the ones making the best investment decisions.

And the ones making strong investment decisions often didn’t follow the “clean” path into venture. There’s another layer here that’s harder to ignore.

Gender remains the only consistently negative predictor in the dataset. Female investors are significantly less likely to make partner - even after controlling for background, experience, and performance. It’s not a pipeline issue. It’s structural.

Put all of this together, and the VC career path looks very different from how it’s usually portrayed. It’s less of a ladder and more of a filter.

• Starting too early can actually limit long-term upside

• Real-world operating experience compounds your advantage

• Credentials help with access, but not necessarily with outcomes

• And structural biases still shape who ultimately makes it

For anyone thinking about entering a venture, the takeaway isn’t “don’t start.” It’s understanding when and how to enter.

Because the fastest way to become a partner might not be getting into VC early.

It might be building something valuable outside it first and then coming in when you actually have leverage.

SOMETHING MORE

🧩 Frameworks & insightful posts

Where is the $435b AI market really making money?

For the past two years, the narrative around AI has been simple: this is the biggest tech wave since the internet.

More companies, more products, more users, more funding.

But if you zoom out and ask a different question - where is the money actually going?, The answer is surprisingly concentrated.

Apoorv Agrawal from Altimeter recently shared his analysis of the AI stack, and it’s counterintuitive. Despite all the innovation happening at the application layer, most of the value is still captured elsewhere.

Two years ago, the AI value chain looked inverted. The compute layer (semiconductors) captured the overwhelming majority of both revenue and profit, while applications - despite being closest to users - captured very little.

The expectation was that this would eventually flip, just like it did in previous tech cycles, such as cloud.

But two years later, even after massive growth, the structure hasn’t changed much.

• The AI ecosystem grew ~5x (from ~$90B → ~$435B)

• Yet semiconductors still capture ~70% of total revenue

• And ~79% of total gross profits

So while everything is growing, the distribution of value remains heavily skewed.

To understand this better, break the stack into three layers.

At the bottom is semiconductors (~$300B), which is essentially dominated by NVIDIA. Their data center business alone has scaled to a massive run rate, and when you add players like Broadcom and memory providers, the concentration becomes even clearer. NVIDIA alone controls roughly ~80% of this layer.

In the middle is infrastructure (~$75B), cloud providers like AWS, Azure, and Google Cloud, along with newer inference players. This is the most competitive layer, with value distributed more evenly across players.

At the top is applications (~$60B), where most founders are building. But even here, the market is highly concentrated. OpenAI and Anthropic alone account for a large portion of total revenue, with the rest spread across coding tools and emerging AI-native startups.

What’s more striking is not just revenue, but profitability.

• Semiconductors run at ~73% gross margins

• Infrastructure at ~55%

• Applications at ~33%

When you translate that into actual dollars:

• Semis generate ~$225B in gross profit

• Infra generates ~$40B

• Apps generate only ~$20B

In other words, the layer with the most innovation is capturing the least profit.

This is why Apoorv summarises it simply: The most profitable strategy in AI today is still selling the shovels.

Another important insight is how growth is being distributed.

Yes, the application layer is growing the fastest in percentage terms (roughly 10–12x in two years). But in absolute terms, the majority of value creation is still happening in semiconductors.

• Semis added ~$225B in new revenue

• Apps added only ~$55B

• NVIDIA alone added ~$175B

That’s nearly 3x the size of the entire application layer today.

This also explains the massive capex cycle we’re seeing. Hyperscalers are spending aggressively to secure compute:

• ~$443B in capex in 2025

• Expected to exceed ~$600B in 2026

• ~75% of that (~$450B) is going into AI infrastructure

They’re effectively racing to control supply in a market where demand is exploding faster than capacity.

At the same time, every major player is trying to reduce dependence on NVIDIA by building custom chips - Google TPUs, Amazon Trainium, Microsoft Maia, Meta’s internal chips. But so far, none have fully matched NVIDIA’s dominance at scale.

So what happens next?

The long-term expectation is still that value shifts upward - from infrastructure to applications - just like it did in the cloud era.

But the timeline may be much longer than people expect.

• Cloud took ~15 years to flip from hardware → software dominance

• AI may follow a similar trajectory

• At the current pace, it could take over a decade for apps to capture the majority of profits

For that to happen, two things need to accelerate:

• Compute needs to get cheaper (compressing margins at the bottom)

• Applications need to capture more differentiated value (expanding margins at the top)

Both are happening - but not fast enough yet.

So, especially for founders - AI may feel like an application-driven revolution. But economically, it’s still a compute-driven market.

Which means:

• The biggest profits today sit at the bottom of the stack

• The most competition sits in the middle

• And the biggest long-term opportunity still sits at the top

But getting there will take time. Because in every platform shift, the early winners sell infrastructure. The enduring winners build applications that capture value later.

And right now, we’re still very early in that transition.

a16z on AI pricing wars: what AI startups get wrong about enterprise pricing?

Everyone assumes the same thing right now: AI is getting crowded, products look similar, and the only way to win enterprise deals is to go cheaper.

That assumption feels logical. It’s also quietly wrong.

Tugce Erten recently broke this down after speaking with multiple enterprise buyers, and the insight is uncomfortable but important - most AI startups are not losing because they’re too expensive. They’re losing because they haven’t become indispensable enough yet.

And once you see that, the entire pricing conversation changes.

One of the biggest misconceptions founders carry into enterprise sales is that budgets are tight and buyers are extremely price-sensitive. But what’s actually happening inside companies looks very different.

Many large enterprises already have meaningful AI budgets allocated, and instead of choosing one tool, they’re often deploying multiple tools for the same workflow. This isn’t confusion - it’s strategy.

They are hedging against model volatility, testing performance across vendors, and avoiding dependence on a single provider in a fast-moving market.

Which means in many cases, you’re not competing to win the budget - you’re competing to survive the eventual consolidation.

Because when that happens, teams don’t keep three tools. They keep one. And the one they keep usually isn’t the cheapest. It’s the one that:

• works reliably under real conditions

• integrates deeply into existing workflows

• improves fast based on feedback

• feels like it’s actively being built for them

This is why premium pricing still exists in AI, even in a crowded market.

If your product is genuinely better or even clearly perceived to be better, you can often charge a 10-20% premium without meaningful resistance. Some buyers even structure usage this way: premium tools for high-stakes workflows, cheaper ones for simpler tasks.

But this advantage doesn’t last automatically.

In AI, perception shifts fast. A new product with better outputs, cleaner UX, or stronger distribution can reset expectations within months. So premium pricing isn’t something you “have.” It’s something you keep earning.

What most founders underestimate is that pricing isn’t just about the number - it’s about the structure.

How you charge often shapes the entire buying conversation:

• per-seat pricing → easy to compare → pushes price competition

• per-outcome pricing → harder to compare → shifts focus to value

• Consumption pricing → reframes discussion around usage efficiency

Enterprise buyers are already pushing for this shift. They want pricing that reflects value, but they also need predictability for planning.

So the smartest companies aren’t choosing one model - they’re offering flexibility. Fixed pricing for teams that need certainty, and performance-linked models for teams that want tighter value alignment.

That flexibility often wins deals without touching the headline price.

Another place where most teams go wrong is during the sales process itself.

Deals don’t usually fail because of the final contract value. They fail because getting started feels heavy - long procurement cycles, security reviews, internal approvals.

So instead of discounting the product, the smarter move is to reduce the cost of entry.

You see this playbook everywhere now:

• generous credits or usage during POCs

• expanded free tiers that act like acquisition channels

• over-delivering early to prove real value fast

The goal isn’t to win on price. It’s to win on adoption - to get embedded before the market consolidates.

But the most important shift is this:

The real price war isn’t with your competitors. It’s with your customer’s own engineering team.

As model costs fall and APIs get easier, companies start asking a different question - not “which vendor is better?” but “can we just build this ourselves?”

And increasingly, the answer is yes. That’s where shallow products break.

Because if your product is easy to replicate internally, pricing pressure never really goes away. It just gets delayed.

The only real defence is building something expensive to rebuild. In practice, that usually means:

• deep workflow integration into day-to-day operations

• accumulated data and context that improve outputs over time

• domain-specific tuning that generic models can’t match

• continuous iteration based on real customer usage

If you step back, the pattern becomes clear.

Enterprise buyers aren’t rewarding the cheapest product. They’re rewarding the one that proves itself in real workflows, reduces risk, and becomes difficult to remove.

Which means the real question for founders isn’t “how do we win this deal on price?” It’s whether your product is strong enough that price stops being the deciding factor.

Because once that happens, you’re no longer in a price war. You’re in a position of leverage.

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.

NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Accel, a global venture firm, has raised $5B in fresh capital to back late-stage startups, with $4B allocated to its Leaders Fund and $650M to a sidecar vehicle. (Link)

• A‑Street, a Bentonville, Ark.-based multi-stage investment fund focused on improving PK-12 student learning and achievement, announced a $675m capital raise. (Link)

• Eclipse, a Palo Alto, CA-based venture capital firm, raised $1.311 billion for two funds. (Link)

• Futurepresent, a NYC, Berlin, and Munich, Germany-based venture capital firm backing AI startups in the US and Europe, launched with its first $300M vehicle. (Link)

• Zero Shot, a U.S.-based VC fund founded by former OpenAI leaders, has raised $20M in its first close toward a $100M target to invest in early-stage AI startups. (Link)

New Startup Deals

• Traza, a NYC-based AI-native procurement platform, raised $2.1M in Pre-Seed funding. (Link)

• SolvaPay, a Stockholm-based AI payments platform, raised €2.4M in Pre-Seed funding. (Link)

• Helical, a London, UK-based virtual AI lab for pharma, raised $10M in Seed funding. (Link)

• ActionAI, a NYC-based enterprise AI automation company, raised $10M in Seed funding. (Link)

• DeepCyte, a Wilmington, DE & Copenhagen-based techbio company, raised $1.5M in Seed funding. (Link)

• Flashpass, a Columbus, OH-based digital skills platform, raised $4.25M 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)

• Analyst, Global Investment Team - 500 Global | USA - Apply Here

• Associate, Data Operations - Iconiq Capital | USA - Apply Here

• Finance Associate - RA Capital | USA - Apply Here

• Fund Controller - NFX | USA - Apply Here

• Vice President, Investor Relations - General Atlantic - Apply Here

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

• Partner 22 -a16z | USA - Apply Here

• Infra / Platform Engineer - Pear VC | USA - Apply Here

• Associate / Senior Associate - Stepstone Group | Italy - Apply Here

• Investment Analyst - Lunicorn Venture | UK - Apply Here

• Investor (Senior Associate/Principal) - Square Peg | USA - Apply Here

• Program Manager - a16z | USA - Apply Here

• Chief of Staff - Greycroft | USA - Apply Here

• Associate - Aditum Bio | USA - Apply Here

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

• Venture Fellow - age1 | USA - Apply Here

• Investment Associate - 500 Global | USA - Apply Here

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👋 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.

• The three sales frameworks every early-stage founder needs.

• Is faster activation actually hurting saas retention in the AI era?

• OpenAI internal memo to beat the competition, including Anthropic.

FROM OUR PARTNER - SEMRUSH

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With Semrush, you basically do.

• See where competitors get their traffic from

• Identify their top-performing pages and keywords

• Track their presence across search and AI

• Find gaps you can capture immediately

It’s like having a live dashboard of your market. Use it to move faster, smarter, and with more confidence.

Good marketing is creative. Great marketing is informed.

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VENTURE CURATORS’ FINDING

📬 My Favourite Finds

Media Posts:

• Bootstrapping an AI Agent Business to $9M ARR. (Link)

• The current danger zone in VC. (Link)

• Who to hire at startups: antifragile people. (Link)

• Excel template: early-stage startup financial model for fundraising. (Link)

Reports/Articles:

• The SaaS Rout of 2026 is even worse than you think? (Link)

• Venture Capital has a starting line problem. (Link)

• The growth metric Silicon Valley loves most is also its least trusted. (Link)

• Why are executives enamoured with AI, but ICs aren’t? (Link)

• What is GTM as a product? (Link)

• Deedy Das visually clusters what VCs are investing in right now (Link)

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📜 DEEP DIVE

The three sales frameworks every early-stage founder needs.

Sales at an early-stage startup often feel like wandering in the dark with a flashlight that barely works. You hear a lot of vague advice: “Just hustle,” or “Go get ‘em!” But when you’re the founder or first salesperson trying to land those critical first customers, what you need are tactics, not motivational quotes.

I came across an interesting post shared by Whitney Sales, founder of ThoughtForge. She shared three powerful sales frameworks that I believe every founder should know and use.

1. Start with your own founder story

Why? Because at this stage, you are the first customer.

According to Whitney, “The inception of any company is inevitably linked to the challenge the founder first faced and addressed. This part of the narrative is too often forgotten, and it’s key to connecting with a customer.”

Before you’ve got fancy logos and metrics, you’ve got your pain and your reason for building the product. Use it.

The Exercise: Use this simple story-building template to craft a value-based founder narrative:

“[SUBJECT] [ONCE UPON A TIME], [SITUATION] [CUSTOMER PROBLEM]. [CUSTOMER] and realized [FEATURES OF PROBLEM]. [COST]. [SUBJECT] learned [IDEATION PROCESS]. As a result, [SOLUTION].”

This isn’t fluff. It’s how you relate, build trust, and show the human reason behind your product.

Example: The TalentIQ founder started his first company and lost time and money because a recruiter couldn’t send qualified candidates fast enough. He saw the chaos of 50 tabs open across scattered data sources, and realised recruiters lacked one clean source of truth. That pain led to the product.

You’ve already told this story to yourself, your early hires, and maybe investors. Tell it again, but now to your customers.

2. Tell value-based customer stories

Why? Because the founder’s story won’t always resonate with every buyer.

Whitney says, “If you only have one customer, extract the elements of their use case that are most relatable to your prospect. A beta customer’s story can be as valuable as a paid customer’s story.”

This is where your product’s real-world value starts to come through. It builds credibility and defuses objections.

→ The Exercise: Here’s Whitney’s customer story framework: