What real traction looks like (before product-market fit happens). | VC & Startup Jobs.
GEO is the new SEO & Understanding the investor thesis.
đ Hey, Sahil here! Welcome to this bi-weekly venture curator newsletter, where we dive into the world of startups, growth, product building, and venture capital. In todayâs newsletter -
Deep Dive: What real traction looks like (before product-market fit happens).
Quick Dive:
GEO is the new SEO: What Founders need to know.
Understanding the investor thesis: the filter behind every ânoâ.
Major News: Nvidia hit with $8B revenue loss from H20 chip export ban, Telegram signs $300M xAI deal to integrate Grok, Apple rejected Elon Muskâs satellite offer, now its plans are in jeopardy & Meta loses 78% of original Llama AI team.
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đ TODAYâS DEEP DIVE
What real traction looks like (before product-market fit happens).
You know that awkward âitâs kind of workingâ phase? Where the productâs not failing, but itâs not taking off either? Where every dashboard sort of shows progress, but nothing feels definitive?
Rob Go shared an insightful mental model (In 2016, still relevant today) for exactly this phase the shape of traction before product-market fit and itâs one every founder should understand.
Hereâs the breakdown and my thoughts on this:
Most early-stage founders believe that the more you improve your product, the more traction youâll get. And that sounds reasonable. If the product gets better, more people should use it, right?
In theory, this would show up as a straight line on a graph product quality on the X-axis, traction on the Y-axis.
But thatâs not what happens in real life.
The actual shape of traction, especially before product-market fit, is more like an S-curve. In the beginning, you make improvements but only see a modest bump in traction. Then, if youâre on the right path, things suddenly start to click traction accelerates quickly.
Eventually, growth levels off again once you hit saturation or diminishing returns. But the tricky part is the beginning, before the curve bends upward. That flat part is what traps so many teams.
The most dangerous part? You can squint and convince yourself that progress is happening. That youâre close.
That one more feature or one more marketing push will tip the balance. You see a few signs of life users using the product, some positive feedback, maybe a handful of sales and you convince yourself PMF is just around the corner.
But what if itâs not?
Two common founder misreads often make this phase even more confusing.
First, many founders overestimate how polished or âcompleteâ the product needs to be to generate traction. Itâs incredible how often raw, even borderline broken tools get early love, if theyâre solving something people truly care about. Founders worry too much about perfection. But real early adoption often happens in spite of flaws. If the pain is real, and your solution is fresh, people will use it.
Second, founders often underestimate how obvious it becomes when things start working. When you actually hit product-market fit, everything moves. Usage grows fast. Word-of-mouth spreads. Customers convert quickly. Money shows up in your Stripe account. You're hiring support and sales people just to keep up. It's not subtle. It doesn't require interpretation. You know it's happening. So if youâre sitting there wondering if youâre close to PMF, chances are, youâre probably not.
And that brings us to a better mental model. Most founders imagine theyâre slowly climbing one S-curve and just need to keep going. But Rob suggests flipping the X-axis from product quality to time.
Because what actually happens for most companies is that you try one curve, it flattens, and then you jump to another. Each curve represents a new product idea, a new target audience, a repositioning, or a fresh attempt at traction. This isnât about making slow steady progress on a single path. Itâs about running experiments, resetting, and switching tracks until something clicks.
The takeaway here is critical: traction doesnât usually happen on a continuous line it comes after a series of pivots, retries, and sharp learning loops.
So how should you navigate this phase if youâre still pre-PMF?
First, shorten the feedback loop. Donât wait months to know if something is working. Run faster experiments. Measure quickly. Decide faster. Speed is your advantage here.
Second, stay lean. A large team increases burn and slows learning. It also makes it harder to pivot when you need to. You might have the money to hire fast â but donât. Before PMF, you want focus, not scale.
Third, donât stick to incremental changes if the curve is flat. If your current direction isnât working, donât keep tweaking the surface. Consider radical shifts a different customer segment, new pricing, a reworked core feature. Sometimes what you need isnât polish itâs a reset.
Fourth, raise accordingly. If you havenât found PMF, donât raise with a mindset of scaling. Raise with the goal of learning. Youâre not building a company yet youâre still finding out what kind of company to build.
This way of thinking is especially helpful for teams still figuring out their first real product-market fit. Everything that comes after scaling, hiring, optimizing plays out differently. But in the early days, the shape of traction is one of the few honest signals you have. It tells you where you actually are, not just where you want to be.
And the truth is, itâs often the rough, simple products that spark the strongest reactions. When you finally land on something that really works, you wonât have to overanalyze it. Youâll just know. It shows up in your users, your numbers, your gut.
Read originial article here: https://robgo.org/2016/09/07/the-shape-of-traction/
đ QUICK DIVES
1. GEO is the new SEO: What Founders need to know.
For 20+ years, if you wanted people to find your company online, you learned the rules of SEO: keywords, backlinks, rankings. You played the Google game. And that worked, until now.
In 2025, the game is changing. Fast.
Search isnât happening on browsers the way it used to. Itâs happening inside models. People are asking ChatGPT, Claude, and Perplexity instead of Google. Apple is baking AI-native search into Safari. Thatâs a tectonic shift.
And with it comes a new playbook: Generative Engine Optimisation (GEO).
SEO helped you rank.
GEO helps you get remembered.
Classic SEO was about getting on the first page of search results. GEO is about getting mentioned in the answer itself, the paragraph ChatGPT gives your potential customer when they ask, âWhatâs the best waterproof winter jacket?â
In that response, is your brand even mentioned? If not, you're invisible.
Where SEO is optimised for Googleâs algorithm, GEO is optimised for how LLMs think, how they synthesise, cite, and share information from across the internet.
LLMs donât list. They reason. They remember. They compress. So instead of fighting to rank on page one, youâre now fighting to become part of the modelâs memory.
GEO â Gaming the system
This isnât about hacks or stuffing keywords. Itâs about building content and presence that earns you a place in the AI layer.
And hereâs where the model era is different:
LLMs are paywalled, not ad-driven.
Theyâre personalised, not one-size-fits-all.
They summarise, not paginate.
That means brands need to think differently. Youâre not bidding for ad slots or traffic. Youâre trying to become a trusted reference, something the model brings up organically because youâre seen as legit.
So what matters in GEO?
You want to be cited by the model. That means:
Creating dense, well-structured content that models can parse.
Using summary formats, bullet points, and context-rich explanations.
Being referenced in authoritative sources and public knowledge bases.
This is about model relevance, not search rank.
Weâre seeing the rise of platforms like Profound, Daydream, and Goodie that help brands understand how theyâre showing up in model outputs and why.
Some startups are even fine-tuning their own mini-LLMs to simulate how major models think, allowing them to test prompts at scale and adjust content strategy accordingly.
Canada Goose did this. They werenât just looking at traffic. They wanted to know:
âDoes the model think of us when someone asks about premium winter jackets?â
Thatâs the new game: unaided model awareness.
From clicks to citations
You donât need 10 blue links anymore. You need one line in the AIâs answer.
New metrics are emerging:
Reference Rate: how often youâre cited by LLMs.
Sentiment Memory: how youâre framed in model outputs.
Competitive Share of Voice in AI-generated responses.
Ahrefs has already launched Brand Radar to track mentions inside AI Overviews. Semrush is building an AI toolkit to monitor and improve your visibility in generative platforms.
This is SEO 2.0 but deeper, more dynamic, and closer to the conversion moment.
Early-stage? This is your edge.
For founders and growth teams, GEO is a first-mover opportunity.
Just like Adwords in 2003 or Facebook targeting in 2013, this is a new channel. The difference? The channel is the model.
And the model is becoming the new front door to commerce. If your startup isnât present there, if the model doesnât remember you, youâre not just missing traffic. Youâre missing trust.
The startups that win will:
Understand how to create GEO-friendly content
Use new tools to monitor model perception
Adapt quickly as LLM behaviour evolves
GEO isnât static. Every model update may change the rules. Just like Google updates used to wreck SEO rankings overnight, youâll need to keep testing, tracking, and iterating.
This isnât just about visibility. GEO is a wedge into something bigger:
Real-time brand management inside LLMs
Autonomous marketing loops that optimise messaging daily
Full-stack platforms that own the loop from monitoring to creation to response
The companies that get this right wonât just sell insights. Theyâll become the channel itself.
Just like Shopify became more than a tool for e-commerce, the GEO winners will become the system of record for brand-model interaction.
In a world where AI answers everything, the real question for your startup is: âWill the model remember you?â
Because if it doesnât, your next customer might not either.
Read the original article here: https://a16z.com/geo-over-seo/
2. Understanding the investor thesis: the filter behind every ânoâ.
Your pitch deck is tight. Your traction looks good. You're solving a real problem. So why did that investor still say no? Itâs not always because your startup isnât good enough. Often, itâs because you donât fit their investment thesis.
An investment thesis is a VCâs internal filter â their ârulebookâ for deciding what to fund.
Sometimes itâs formal (a PDF sent to LPs), other times itâs just an instinct theyâve honed over years. But it always exists.
VCs use it to raise money from their limited partners (LPs). If they veer too far off thesis, LPs get nervous. And nervous LPs mean no next fund.
Hereâs what a typical thesis includes:
1. Check size + round size
âWe invest $2Mâ$4M in $5Mâ$10M rounds.â If you're raising too little or too much, you're out.
2. Lead vs. follow
Some VCs lead (set terms, take board seats).
Others follow (join after someone else leads).
Many donât have bandwidth to lead too many deals.
3. Business model
B2B: high-ticket sales to fewer customers
B2C: mass market, more users, smaller tickets
B2B2C: infra that powers someone elseâs consumer play
Misalignment here can end the conversation fast.
4. Vertical focus
Some funds go deep in fintech, healthtech, AI, climate, etc.
Others avoid certain areas entirely (edtech, crypto, adtech, surveillance).
You may be a great company â just in the wrong space for them.
5. Ownership targets
âWe aim to own at least 10â15% in every deal.â If your valuation or round structure doesnât get them that, they may pass.
6. Founder background
Some funds back founders from certain alumni networks (e.g., Stanford, YC, ex-Stripe, etc.).
Some only invest in âunderrepresentedâ founders by geography, race, gender, or background.
7. Geography
Some invest only in the US.
Some only in Europe.
Some anywhere, but still prefer proximity. Yes, location still matters even in 2025.
8. Market size & return potential
A $100M fund writing $5M checks expects each company could return the full fund (i.e., become a $500Mâ$1B exit).
If your company looks like a solid 3x return but not a fund-returner, thatâs a pass even if itâs a great business.
So⌠what should you do as a founder?
Before reaching out to any investors, check out their website, oftern time you will find investmetn criteria on their website. If not then ask early.
A few simple questions can save you a lot of wasted time:
âWhat kinds of companies do you typically invest in?â
âDo we fit your thesis?â
âIf not, is there one specific area where we donât align?â
Founders often get rejected for the wrong reasons or worse, no reason at all.
Sometimes itâs a misunderstanding. Sometimes you just donât fit the filter.
But when you understand how investors think and how tightly they stick to their thesis you can stop taking rejection personally⌠and start focusing on the investors who can say yes.
Remember, even the best pitch can fall flat when itâs aimed at the wrong target. So do your all research before reaching out to any investor.
THIS WEEKâS NEWS RECAP
đď¸ Major News In Tech, VC, & Startup Funding
New In VC
NYC-based VC firm Work-Bench has closed a $160M Fund IV to invest in seed-stage enterprise software companies. (Read)
Humain, the Saudi state-backed AI company, is planning a $10 billion venture fund named Humain Ventures to invest in startups across the U.S., Europe, and Asia. (Read)
Major Tech Updates
Apple claims to have prevented $2B in fraudulent transactions and blocked nearly 2M risky app submissions in 2024, part of $9B in fraud stopped over the past five years. (Read)
Eleven of the fourteen authors from Meta's foundational Llama paper have now left the company, raising questions about its AI talent retention and momentum. (Read)
Apple declined Elon Musk's 2022 demand for $5 billion upfront for exclusive SpaceX satellite connectivity on iPhones, leading Musk to partner with T-Mobile. (Read)
Teslaâs European sales fell 49% year-over-year in April despite the launch of a refreshed Model Y, signaling weakening brand strength. (Read)
Elon Muskâs xAI is partnering with Telegram to distribute its chatbot Grok across the messaging app for one year. (Read)
Chinese AI startup DeepSeek released an updated version of its R1 reasoning model on Hugging Face with a permissive MIT license for commercial use. (Read)
New Startup Deals
Alba Health, a Stockholm, Sweden-based gut health startup, raised $2.5m in seed funding. (Read)
Frinks AI, a Bangalore, India-based provider of a vision AI platform for manufacturing, raised $5.4M in Pre-Series A funding. (Read)
Pallet, a San Francisco, CA-based company behind CoPallet, the AI workforce for logistics, raised $27M in Series B funding. (Read)
Context, a Palo Alto, CA â based AI-native office suite, raised $11m in seed funding at a $70m valuation. (Read)
Creatify, a Mountain View, CA-based provider of an AI platform for video advertising, raised $15.5M in Series A funding. (Read)
Palla, a Miami, FL-based provider of a platform that enables instant cross-border payments for global financial institutions and fintech firms, raised $14.5M in Series A funding. (Read)
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