On the Relentless podcast he said it flatly: “We are now, like, in the singularity. This is the moment.” Not future tense. Present tense. And present tense changes what counts as a reasonable bet.

That one reframe explains the rest. Why OpenAI survived a fight it should have lost. Why he flew to 28 countries in 35 days. Why a ten-week-old startup can now out-build a company that took a decade to reach the same point.

The proof is in the shipping cadence. Anthropic went from 444 days between frontier models to gaps you now count in weeks, shipping Opus 4.6, 4.8, Fable 5, and Opus 5 inside a single year. That is what an exponential looks like from the inside.

I watched the full interview so you do not have to. Here are the ten that matter.

1. The Day ChatGPT Crossed a Million Users (And What He Told His Partner That Night)

Growth curves lie, right up until the day they don’t.

Four days of watching ChatGPT usage spike, dip, and spike higher. His own researchers called it a fluke each time. He did not believe them. Years of running Y Combinator office hours had taught him what organic, compounding growth looks like on sight.

On day five, ChatGPT crossed a million users. He went home and told his partner the opposite of good news: their quiet life was ending.

▫️ Track the shape of growth, not just the size of it.
▫️ Discount groupthink from the people closest to the product.
▫️ Treat organic compounding as a signal, not a coincidence.
▫️ Prepare for the personal cost before the metric forces it on you.

Why it matters: most founders wait for a press cycle to confirm something is real. By then the shape has been visible for days. Learn to read the curve yourself.

2. Plan for the Curve to Keep Climbing

What used to be a joke at lunch is now the news.

The idea Altman used to joke about at the lunch table is now the actual state of the world. A decade ago transformative AI felt improbable. Today it is operational.

He is clear-eyed about the risk in that same sentence. His worry is not a rogue machine. It is a small number of companies or people deciding they should control the outcome for everyone else.

Why it matters: if you are building anything that assumes AI capability plateaus, you are planning for a world that already ended. Plan for the curve to keep climbing.

3. Ask for the Hard Thing Before Assuming No

Everyone told them not to bother, so they asked the team anyway.

OpenAI was badly behind on coding tools. The standard advice: once a competitor owns a category, move on. They asked anyway, and framed it as a mission that mattered. Codex became the product many of the best engineers he knows now use daily.

The lesson is narrower than “always fight the incumbent”:

▫️ Ask for the hard thing before assuming the answer is no.
▫️ Frame the mission as important, not just possible.
▫️ This only works when the team believes the stakes are real.

Why it matters: founders self-select out of categories that look occupied. Occupied is not closed, not when the technology still moves fast enough to reshuffle who is actually ahead.

4. Watch Power Concentration, Not Rogue Machines

The scary version of AI is not a machine waking up. It is a small group deciding who gets access.

His top concern, stated plainly: a handful of firms deciding they should run this technology for everyone else. His counter is abundance. Make it cheap, powerful, and widely distributed instead of scarce and permissioned.

▫️ Decentralized access matters more than centralized safety theater.
▫️ Power concentration is the risk to watch, not just capability jumps.
▫️ Every historical trade of liberty for safety has been a long-term loss.

Why it matters: if you build on foundation models, the distribution strategy of the labs you depend on is a business risk to track, not a philosophical debate to ignore.

5. Your Startup Is a Structure, Not Just a Product

Altman says the real breakthrough behind modern prosperity was not a machine. It was a piece of paper.

As a kid he debated his parents over which Industrial Revolution technology mattered most. His answer today is not a technology at all. It is the joint stock corporation. Before it existed, business ran on personal trust between people who knew each other. No shareholders, no legal shell able to raise capital from strangers.

Once it existed, capital could pool, risk could be shared, and speculative technologies had a vehicle to fund them. He ties this directly to the collapse of extreme poverty over the last century.

▫️ Liability protection let people take real risks.
▫️ Capital pooling let strangers fund unproven ideas together.
▫️ Specialization let separate companies interlock instead of duplicating effort.

Why it matters: your startup is an incentive-alignment machine. That structure, more than any single technology, is what actually compounds.

6. The Opposite of a Bad Day Is No Day at All

Most people are optimizing for the wrong opposite.

You can spot new founders in office hours by how they react when something goes badly wrong. Experienced founders have already survived nine company-threatening moments. The tenth does not hit the same way.

Most people treat a good day as the opposite of a bad one. Altman argues the real opposite is no day at all. He credits Naval Ravikant with the version that stuck: if your life had a fast-forward button, your life would be over. The boring stretches and the painful ones still beat skipping to the end.

Why it matters: this is an operating principle for founders about to have a very bad week. Gratitude for the bad day is trainable. It is not a personality trait.

7. When Your Product Outruns Understanding, Show Up in Person

When world leaders started asking whether to shut AI down, he got on a plane instead.

After GPT-4 launched, public reaction turned chaotic almost overnight. Leaders were openly asking whether someone needed to halt the technology. OpenAI was still small, and Altman decided showing up in person was the only way to keep things from escalating. Brian Chesky, who had done the same for Airbnb at smaller scale, told him to go big.

Altman took on 28 countries in 35 days. He hated it. No jet lag cure works, and by the end he was having half-asleep visions of his childhood bedroom, which he read as his brain telling him to stop.

Why it matters: when your product outruns public understanding of it, distance from the people affected becomes a liability. Sometimes the fastest way to de-escalate is to physically show up.

8. Betting Against Incumbent Sclerosis Is Not Reckless

By his own logic, Google should have crushed this company by 2019.

Altman is blunt about how improbable OpenAI’s survival was. By 2019 a company with Google’s resources should have pushed it out of relevance entirely. It did not happen. He borrows Jeff Bezos’s description of AWS’s seven uncontested years as a business miracle, and applies the same label to his own company.

His explanation: groupthink at large incumbents, the rarity of true exponential windows, and a level of internal conviction he admits worried him at the time as possible self-deception. In hindsight, the conviction was simply correct.

Why it matters: big companies get set in their ways for structural reasons, not because their people lack intelligence. That sclerosis is a real, repeatable opening. Betting against it is not reckless if your conviction is earned.

9. AI Is a Genie. Math Is the Wish It Grants Too Fast.

The upside of AI is obvious. The unsettling part is what happens right after it says yes.

Altman frames the industry’s real challenge in stages: AI is close to granting enormous requests, the first ones need to broadly benefit humanity, and the space of possible requests is vast. Then a fourth stage nobody talks about enough. You make the wish, it is granted, and you are left unsure what you just unlocked.

He points to mathematics as the clearest live example. A model cracked a decades-old open problem in days. He expects most jobs to adapt better than people fear. Math is the exception, because the capability arrived faster than the field could absorb it.

Why it matters: track which domains start resembling math instead of resembling coding. That is the signal for where adaptation stops keeping pace with capability, and where a two-week-old startup can rewrite a field before incumbents notice.

10. Corporate Conditioning Leaves Ambition Catastrophically Low

The average new founder is not short on talent. They are short on permission.

Altman describes his first office hours with founders who had spent years in corporate America. The ambition and self-belief he saw: catastrophically low. Not an insult, a diagnosis. Most had never gone a stretch of life without a boss, teacher, or parent shaping what they were allowed to want, and most cultures carry a social penalty for wanting too much out loud.

Untraining that takes real time. The fix is not a speech.

▫️ Give people a task slightly beyond what they think they can do.
▫️ Let the win, however small, speak louder than encouragement.
▫️ Repeat until the belief updates on its own.

Why it matters: if you hire out of big companies, budget real time for unwinding this. It is not a character flaw. Years of incentive training built it, and years is what it takes to unbuild it. The founders who move fast in this market already untrained it in themselves.

The Singularity Playbook: 5 Things to Steal

Altman’s thesis across all ten moments is consistent. The exponential is real, it compounds faster than institutions adapt, and the advantage goes to whoever builds forward from today’s state instead of waiting for permission or certainty.

• For founders. Stop benchmarking your team against what a startup looked like ten years ago. A ten-week-old company today can outbuild a year-old company from 2015. Build for the models you will have in six months, not the ones you have now, and run it on a one-person operating system if that is your headcount. Start this week, not next quarter.

• For investors. The market still has not priced in that model progress compounds the same way founder growth does. Altman calls this the single most important thing he would want a founder to internalize. Underwrite for the exponential, not the current snapshot, before your comps go stale. The frameworks in what top VCs look for in 2026 already assume it, and the investor lists are where the founders who get it are raising.

• For people in tech. The sclerosis Altman describes at Google circa 2019 is structural, not a personality flaw of any one leader. If you are inside a company that size, that same blind spot is probably sitting in your own roadmap right now. Go find it before a two-week-old startup does.

• For other industries. The same logic applies to any field where cost and cycle time are compressing, not just software. Watch for the signature Altman describes: organic, compounding usage that experts keep calling a fluke. Take it seriously the first time you see it, not the fifth.

The five to steal:

1. Read the curve yourself. Do not wait for consensus to confirm what the data already shows.

2. Ask for the hard thing. Momentum belongs to whoever asks, not whoever waits for an opening.

3. Distribute power, do not concentrate it. Abundance beats gatekeeping over any long horizon.

4. Treat pain as data, not failure. The opposite of a bad experience is no experience at all.

5. Plan forward from a few convictions. Do not plan backward from a rigid twenty-year picture.

The ground is shifting faster than most people are willing to admit.

Build for where the curve is going, not where it sits.

The founders who win this decade will be the ones who asked anyway.

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▫️ What top VCs look for in 2026 · the VC database · the investor lists
▫️ The full AI Corner library and the VC Corner resources

If this breakdown saved you an hour, share it with one founder or investor who needs it.

Every founder who has run out of money remembers the exact moment the balance hit zero. That’s when you know the game’s over.

CB Insights tracked 431 venture-backed startups that closed since 2023 and found capital exhaustion cited in 70% of the shutdowns. That number describes the moment of death, not the disease. 43% point to poor product-market fit as the deeper cause, the slow failure to turn interest into revenue long before the money ran out.

Product-market fit rarely fails in a boardroom. It fails in the first twenty sales conversations with a stranger who owes you nothing, and the damage is done long before anyone calls it a crisis.

Ten mistakes show up again and again in those conversations. None are about sales skill. They’re about founders avoiding the one thing that tells them what’s real fastest: a stranger holding a checkbook.

Speaking of what it actually takes to close:

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Table of Contents

1. Two Kinds of Avoidance That Look Like Discipline

2. Reading the Room You’re Actually In

3. What a Price Actually Communicates

4. Building a Pipeline That Can Survive a No

5. The Process Nobody Should Hand Off Yet

1. Two Kinds of Avoidance That Look Like Discipline

Two of the most common founder mistakes aren’t sales errors at all. They’re the same underlying fear, wearing different clothes.

The Polish Stall

Founders wait to start selling until the product feels finished. It never does, because the founder is the only person positioned to see every flaw still sitting inside it.

Readiness behaves like a confidence problem far more than a product problem, and confidence comes from repetition, not another sprint of polish.

CB Insights‘ own dataset shows the median time from a startup’s last fundraise to its death is 22 months, which is not a lot of runway to spend waiting for a version of the product that feels safe enough to show a stranger.

The deeper mistake is believing the product is what’s being tested first. Get rid of this thought as early as you can. .

The founder’s hypothesis about the problem is, and every week spent refining in isolation is a week spent guessing instead of learning.

The fix isn’t shipping something broken and hoping for the best. It’s reframing what’s actually on offer.

It might not be a finished output, but a partnership in solving something painful, with the founder as an engaged, available collaborator.

Customers forgive a rough product. They rarely forgive a founder who was too afraid to show up with one.

The Reluctant No

The opposite fear shows up once conversations actually start. Founders stay in dead deals long after the signal has clearly turned, because ending a conversation can feel like admitting defeat.

Forrester’s research on B2B purchasing found that 86% of purchases stall at some point before closing, which means most of what looks like a stalled deal is already effectively over, whether or not the founder has accepted that yet.

Staying in it doesn’t revive it. It just delays the founder’s next real conversation.

The better instinct is to ask the disqualifying question early and directly: how high a priority is solving this problem right now?

If the honest answer is not very, believe it immediately.

A fast no is worth more than a slow maybe, because the one resource a founder can’t get back at this stage is the hours spent chasing a prospect who was never going to sign.

Once a founder actually gets a real conversation going, a different, and more expensive, set of mistakes takes over.

2. Reading the Room You’re Actually In

Getting the meeting is rarely the hard part. Knowing who’s actually in it, and what to do once you’re there, is.

The Champion Isn’t the Buyer

Every early sales cycle produces a hero inside the target company, someone who understands the product immediately and pushes for it internally. Founders routinely mistake that enthusiasm for progress.

Gartner’s research on B2B purchasing puts the average buying group at six to ten stakeholders, each carrying a different priority and a different kind of veto power.

A champion can open a door and translate the pitch internally, but rarely holds the budget authority to close what’s behind it.

Founders also tend to log polite interest as momentum. “This is really interesting” and “we’d love to stay in touch” usually mean a stranger being kind after thirty minutes of someone else’s life’s work.

The only reliable cue that cuts through the politeness is money changing hands, or a specific next step tied to a named decision-maker. One direct question fixes most of the confusion early: whose budget would this actually come from?

Sell Less, Listen More

Technical founders in particular tend to sell the same way they debug, by explaining everything, thoroughly, in order, until the logic feels airtight. But that instinct runs exactly backwards in a sales conversation.

Gartner and Forrester’s more recent research on B2B buying puts somewhere between 70% and 80% of a buyer’s journey as already complete before they ever speak to a seller.

That means the person on the call has largely formed their view already, and the conversation that follows is for extracting information, not delivering it.

A useful rough guideline is an 80/20 split. 80% listening, 20% talking. The pareto principle.

Founders who leave a call with pages of a prospect’s own words about their pain, their internal politics, and their timeline build a sharper pitch than founders who leave a call having said the most articulate things.

Even once a founder is talking to the right person and listening well, most still get the next part wrong.

And that is, of course, the number.

3. What a Price Actually Communicates

A price is the first real piece of information a buyer receives about how seriously to take the person sending it.

Founders assume a low number removes friction from a sale. In practice it usually swaps one objection for a worse one: invisibility.

A price that doesn’t require a real internal conversation on the buyer’s side isn’t being evaluated so much as tolerated.

Marc Andreessen’s advice to the founders he backs is famously blunt. He’s said he’s considered hiring a skywriter to put two words above San Francisco: “Raise Prices.”

The logic isn’t about greed. Raising prices is a fast way to find out whether a company actually has a moat, since customers with no real alternative will still pay.

Companies that charge more can also fund the distribution and R&D that companies charging less simply can’t.

A core piece of that advice is pricing by value rather than by cost. That can be achieved by charging as a percentage of what the product is actually worth to the business buying it, not what it cost to build.

Founders who treat pricing psychology as a serious discipline in its own right, rather than an afterthought bolted on after launch, consistently end up with more durable margins.

If a number doesn’t make a founder wince slightly saying it out loud, it’s probably too low.

The hidden risk: Underpricing

Then coems another risk in pricing, which is underpricing.

This is a scary one because it can lock founders into contracts they can’t easily escape. Most founders tend to overthink this.

If a discount is genuinely unavoidable to land a first logo, keep the term short. A cheap monthly deal is recoverable, while a cheap three-year deal is a tax paid long after the mistake becomes obvious.

At the end of the day, solving pricing doesn’t fix a more structural problem sitting behind it, which is the shape of the pipeline itself.

4. Building a Pipeline That Can Survive a No

The healthiest early-stage sales motions are boring in a specific way. They’re wide, not deep, and they never rest their full weight on a single account.

One Great Logo Is a Story, Not a Business

A recognizable name shows real interest, and founders start spending the deal in their head long before a contract exists.

It shows up in your home page. It ends up in the board update. It starts steering the roadmap.

But anchoring a company’s story to a single deal distorts judgment well before that deal ever falls through, and most of them do fall through for reasons that have nothing to do with the founder.

It could be budget freezes, reorgs, a champion who changes jobs. Anything.

A pipeline with one deep relationship and no width is a single point of failure sitting quietly in someone else’s inbox.

Founders who want real investor traction heading into their next raise keep five to ten live conversations running at once, on purpose, even after a promising one shows up.

It’s a healthier read on unit economics, too. One logo tells a founder almost nothing about whether the model works at scale; a dozen smaller ones, closed on comparable terms, tell them almost everything.

Bigger Isn’t Better When You’re This Small

The instinct to chase the biggest possible logo is understandable, and often wrong, because large organizations are built for stability, not speed.

Forrester’s benchmarks put the average enterprise deal above $100,000 in annual contract value at 11-17 months to close. That is an eternity relative to the feedback loop an early founder actually needs.

A smaller, more agile customer can go from first call to signed contract in the time it takes an enterprise prospect to schedule its second internal meeting.

That speed matters more than the logo’s name recognition at this stage, because velocity is the feedback loop.

Founders eyeing an upmarket or international push tend to do better when they hire slow into that motion and prove the faster, smaller version of the sales cycle first: win decisive customers, build the case studies, then move upmarket deliberately instead of prematurely.

Nevertheless, getting the pipeline shape right still doesn’t solve the last problem, which is who’s actually running the process, and how.

5. The Process Nobody Should Hand Off Yet

The final set of mistakes shows up after the conversation ends, in what founders do with what they learned and who they hand it to next.

A Pitch That Hasn’t Changed Is Already Stale

Founders often treat their pitch as a finished asset, polished once and then deployed unchanged across the next hundred conversations.

Let’s be real here. A pitch that’s stopped evolving is a pitch that’s stopped listening.

Watch for where a prospect leans in, where they get confused, where they interrupt with a question nobody anticipated. That’s the market editing the story, for free.#

Reception to feedback is what separates a great founder from the average.

60 seconds of notes after every call, repeated over a few dozen conversations, builds something no internal brainstorm ever could. It builds a pitch shaped by real buyer reactions instead of founder assumptions.

But the same instinct has to hold inside the room, not just after it. If a prospect suddenly lights up at one specific feature, the founder should follow that thread instead of returning to the script.

The Hire That Arrives Too Soon

Handing sales to a new hire before the founder has personally closed a meaningful number of deals doesn’t accelerate anything. Even the outbound sales system that helped scale Salesforce was built only after its architects proved the motion themselves.

It removes the founder from the only channel currently teaching them what’s actually true.

So, when should you hire?

OpenView’s 2023 Product Benchmarks report found that fewer than half of product-led SaaS companies, had a dedicated sales hire before crossing $1 million in ARR, a figure that only climbed to 73% once they’d passed that mark, well after the founders had personally proven the model worked.

Sterling Road, a pre-seed B2B investor, uses a rougher rule of thumb for the same idea. Roughly a 100 closed deals for consumer products, 30 for SMB, 15 for mid-market, and 5 for enterprise, before a hire makes sense.

Startup Genome’s research into high-growth failure found premature scaling, spending and hiring ahead of a validated model, implicated in 70% of the cases it studied.

An early sales hire without a founder-tested go-to-market playbook to hand them is exactly that kind of premature scaling with a job title attached.

A hire can’t execute a playbook that doesn’t exist yet. Founders who delegate before they’ve built one are asking someone else to guess at a process the founder never wrote down.

Every one of these nine patterns is really one mistake wearing different clothes: treating the first sales cycle like a performance instead of a research project.

Founders who struggle are usually optimizing for looking competent: the polished deck, the comfortable price, the pitch that never changes, a hire who takes the awkward calls off their plate.

Founders who break through are optimizing for learning fast instead, even when that means an uncomfortable price, a call that ends in a clean no, or another month running the sales motion personally before anyone else touches it.

The first ten sales conversations a company ever has are the cheapest, highest-signal research it will ever run. Guard them accordingly.

From top insights a

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• Anthropic Raised $580M With a 10-Slide Deck 💰
  The company raised funding before launching a product, backed by its research team, safety focus, and growing customer demand. Its presentation emphasized long-term execution over product demos, showing investors bought into the team’s direction.

• Top SaaS Vendors on Ramp (July 2026)💳📈
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• Travis Kalanick Raises $1.7B for Atoms 💰
  Atoms secured $1.7 billion led by a16z, unified its businesses under one equity structure, and added Ben Horowitz to its board. Kalanick is building industrial AI systems focused on sectors such as mining, construction, and food production. [Travic Kalanick]

• Moonshot AI CEO Says Teams Matter More Than Models ⚙️
  Zhilin Yang argues organizational design creates stronger models by enabling faster execution and better decisions. He points to unrestricted user access and long context as strategic advantages over short-term optimization.

• Google’s Gemini 3.6 Flash Nearly Halves Agent Costs 💸
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Interesting Reports 📊

• UK Startup Funding Doubles in H1 2026 📈
  UK startups secured $17B in the first half of 2026, marking the strongest start since 2022 as AI accounted for $12.6B across 297 rounds. The country led Europe in venture funding, with late stage deals driving most activity and Deep Tech remaining a core strength. [Dealroom.co]

  

• Europe’s Founder Factories Keep Compounding 🏭
  Successful startups are creating a steady pipeline of new founders who launch the next generation of VC-backed companies. London remains a leading hub, while firms like Deliveroo, Wise, and Klarna continue producing high numbers of startup alumni. [Dealroom.co]

• Nordics Build Bigger With Less Capital 💰
  Nordic startups have reached $561B in enterprise value while advancing to later stages with significantly less funding than Bay Area peers. International investors now finance much of the region’s late stage growth, with US firms leading many AI investment rounds. [Dealroom.co]

  

Recently Launched Funds 💸

• Fresenius launched a €200M corporate venture capital fund to invest in breakthrough healthcare technologies and drive healthcare innovation.

• Transition VC launched its second £155M venture capital fund to back early-stage climate tech startups accelerating the energy transition.

• Supercharger Ventures launched €10M Fund I to invest in early-stage edtech and future-of-work startups across Europe.

• EHE Ventures opened the second cohort of its $15M venture fund, continuing to back high-potential early-stage founders.

• Primo Capital launched Primo Space 2, its second venture capital fund focused on supporting European space technology startups.

The AI Corner

The Claude Code system that replaces a 5-person team

Most Claude Code articles list a few hacks and stop there…

Read more

3 months ago · 45 likes · Ruben Dominguez

VC Jobs 💼

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• Cathedral raised $160M in funding at a $1.4B post-money valuation to expand its enterprise AI platform. (read more)

• Humanoid raised $152M in Series A funding to accelerate the development of next-generation humanoid robots. (read more)

• Crystalys Therapeutics raised $130M in Series B funding to advance its precision oncology pipeline. (read more)

• Emergent raised $130M in Series C funding at a $1.5B valuation to expand its AI infrastructure platform. (read more)

• Assort Health raised $120M in Series C funding to scale its AI-powered healthcare platform. (read more)

• Neo raised $100M in funding to expand its fintech platform and product offerings. (read more)

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✅ The Startup MIS Template: A Excel Dashboard to Track Your Key Metrics

✅ The Go-To Pricing Guide for Early-Stage Founders + Toolkit

✅ DCF Valuation Method Template: A Practical Guide for Founders

✅ How Much Are Your Startup Stock Options Really Worth?

✅ How VCs Value Startups: The VC Method + Excel Template

✅ 2,500+ Angel Investors Backing AI & SaaS Startups

✅ Cap Table Mastery: How to Manage Startup Equity from Seed to Series C

✅ 300+ VCs That Accept Cold Pitches — No Warm Intro Needed

✅ 50 Game-Changing AI Agent Startup Ideas for 2025

✅ 144 Family Offices That Cut Pre-Seed Checks

✅ 89 Best Startup Essays by Top VCs and Founders (Paul Graham, Naval, Altman…)

✅ The Ultimate Startup Data Room Template (VC-Ready & Founder-Proven)

✅ The Startup Founder’s Guide to Financial Modeling (7 templates included)

✅ SAFE Note Dilution: How to Calculate & Protect Your Equity (+ Cap Table Template)

✅ 400+ Seed VCs Backing Startups in the US & Europe

✅ The Best 23 Accelerators Worldwide for Rapid Growth

✅ AI Co-Pilots Every Startup & VC Needs in Their Toolbox

Your brain runs out of unique opening hooks somewhere between message five and eight.

That single fact explains more about failed raises than any advice about writing better DMs.

You block Thursday morning for 50 investors. Forty-seven minutes in, you have six.

The first is sharp. The third is the first one with two words swapped. By six you are recycling a sentence from message two.

Forty-four to go.

So you push through. The fund receives forty-one messages that read like filler, and that is how a partner learns to skip your name for the next two years.

Founders think the hard part of a raise is finding the partners.

It sits one step later.

What Breaks Underneath

Three structural reasons, and writing ability is none of them.

▫️ Personalization decays. By message twenty you are writing the same DM with a different name at the top, even holding full context on every recipient.

▫️ Timing is asymmetric. A partner posts Tuesday at 11 AM. That hook stays golden for about 36 hours. By Friday the post is buried, and “I saw your post about X” tells them you skim.

▫️ Response velocity decides the funnel. Six partners reply. You answer 90 minutes later because you were doing other work. Two go cold on the delay, two on a rushed answer, two book.

Interesting reply to booked call should run 60 to 70%. By hand it runs 25 to 35%.

The fix has nothing to do with writing better DMs. It is wrapping a loop around the work so you become the judgment layer instead of the typing layer.

The founders booking calls this quarter stopped typing them months ago.

The Three Rules Underneath Everything

1. Discovery feeds personalization. Your list needs each partner’s last 3 to 5 public posts attached. A list without posts sets the AI up to write generic.

2. One hook per message. One post, one reference, one message. Multi-hook DMs read like marketing and convert worse in every dataset worth trusting.

3. You approve before anything sends. Generation is automated. Sending is approved. This is what stops you messaging a partner who left the firm last week.

The Four Layers

Setup runs about 60 minutes. Delivery runs 14 days, because LinkedIn rate limits decide the pace.

Everything below is the full build.

Inside the playbook:

▫️ The list enrichment prompt that attaches every partner’s recent posts and hooks

▫️ The personalization system prompt that writes 50 DMs without drift

▫️ The four-step sequence with safe rate limits

▫️ The response classification loop that converts replies to calls

▫️ The worked example, hour by hour

▫️ The paste-ready CLAUDE.md and all five templates

The Investor Outreach Loop

Everything below runs on your own list, in about an hour.

Layer 1 is the part most founders get wrong. Your subscription solves it.

The loop only works when the list is qualified, named, and current. That is exactly what sits inside:

📂 Investor Lists — 10,000+ investors, filterable by stage, sector, and geography ▫️ 200+ US AI angel investors, all builders, all searchable

▫️ 2,500 SaaS angels

▫️ 590+ US women angels

▫️ The US VC database and the global VC database

▫️ Family offices that cut pre-seed checks

▫️ 80+ non-dilutive funding sources

📈 Pitch Decks — the decks you attach on day 3

▫️ 26 decks that raised $400M in 2026

▫️ 16 unicorn decks, before the billions

▫️ The leaked Anthropic 2022 deck

▫️ Rippling’s $200M investor memo

📊 Financial Models — what they ask for after the call

▫️ The SaaS financial model

▫️ The 13-week cash flow and runway system

▫️ The SAFE conversion calculator

▫️ The startup valuation tool

🧰 The Playbooks — the systems behind every raise

▫️ What top VCs look for in 2026

▫️ Self-improving fundraising system with Claude, 14 steps

▫️ What top VCs check in due diligence

▫️ This loop, plus every playbook published since

One subscription. Every list, deck, model, and playbook.

Start your free 7-day trial

Cancel anytime. First subscribers get 50% off forever.

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Read more

A Skill fixes that. It’s a small, reusable capability you save once. Claude loads it automatically when the task matches, and every session starts with Claude already knowing your standard, your rules, and the shape of the answer you want.

Think of it like this:

A prompt is a one-time instruction. A skill is a permanent job description.

This piece provides the ten best skills built specifically for the founder/builder journey.

together with Wispr Flow:

You are about to copy 10 skills. The good ones get edited into your standard, your rules, your voice, and that is a lot of typing.

Wispr Flow is a voice-to-text tool that works everywhere you write, Claude included. Talk through the edits, finished text appears.

▫️ Speaking runs far ahead of typing, so the detailed version gets written

▫️ Change your mind mid-sentence and it keeps up, where Siri keeps the stumble

▫️ Spells your product and teammate names right the first time

A skill is a permanent job description. Dictate yours:

Get 1 month of Wispr Flow Pro free

→ code RUBENDOMINGUEZ

Table of Contents

1. What Actually Makes A Skill Good

2. How To Actually Install Them

3. Steal These 10 Skills Now

4. Bonus: The Skill That Builds Skills

5. Recap: Build Once, Run Forever

1. What Actually Makes A Skill Good

Every skill below follows the same four-part structure, and that structure is the real lesson. Once you see it, you can write your own for anything:

1. A specific persona. Not “an assistant,” but “a pricing strategist who’s repriced 40 SaaS products.”

2. A fixed, labeled output, so you get the same rigorous shape every time instead of a wall of prose.

3. Hard rules, including what to never do. Constraints are what separate a sharp tool from a chatbot.

4. A forcing function, one mandatory output the model isn’t allowed to skip. “Name the assumption that, if wrong, kills this.” “Name what you’re cutting.” This is the part that drags Claude past the obvious answer into the useful one.

2. How To Actually Install Them

In Claude.ai / desktop:

Settings → Capabilities → turn on Skills. Then go to Skills → “+” → Create skill, and paste the block (or upload it as a SKILL.md file).

The text between the --- lines at the top is the frontmatter Claude uses to decide when to load the skill, so keep the description precise.

In Claude Code: save each one as .claude/skills/skill-name/SKILL.md in your project. Claude finds them automatically.

That’s it. Build once, run forever.

One security note up front:

Skills are just text and (sometimes) scripts, which is also why they can be dangerous. Audits of the public skill ecosystem have found prompt injection and credential-stealing payloads in a meaningful share of community skills.

The ten below are plain instructions with no scripts, so there’s nothing to exfiltrate. But if you download skills from strangers, read the SKILL.md and any bundled scripts before installing, and favor known authors.

3. Steal These 10 Skills Now

1. The Idea Executioner

Why. The most expensive thing a founder does is build the wrong thing for six months. The job before building is not to fall in love with the idea. It’s to try to kill it cheaply, this week, before it costs you a quarter.

What it does. Strips your idea down to its single riskiest assumption and hands you the fastest, cheapest experiment that could prove you wrong, plus a clear go / no-go line.

---
name: idea-executioner
description: Use before building anything. Pressure-tests a startup idea by finding the assumption most likely to kill it and the cheapest test to check it. Run this instead of writing code.
---

You are a pre-seed investor and twice-failed founder. You have watched dozens of smart people build things nobody wanted. You are not here to be encouraging. You are here to find the fastest way to save me six wasted months.

Take the idea I describe and return:

1. THE BET (1 sentence): restate the core bet this idea is making about the world. Not the product, but the belief that has to be true for the product to matter.
2. THE RISKIEST ASSUMPTION (1 sentence): the single assumption that, if false, makes everything else irrelevant. Not the most likely to be wrong, the most fatal if wrong.
3. THE CHEAPEST KILL TEST (specific): the fastest, cheapest experiment that could prove that assumption false. It must cost under $100 and take under one week. No "build an MVP." No "run a survey." Name a real, concrete test.
4. WHAT A "NO" LOOKS LIKE (1 sentence): the specific result from that test that means stop. Define the kill line before I run it, so I can't rationalize past it.
5. THE VERDICT: BUILD / TEST FIRST / DROP. One word, one sentence of reasoning.

Rules:
- Do not validate the idea. Try to kill it. If it survives, that means something.
- The kill test must be runnable by one person this week. If you can't name one, say "this idea can't be cheaply tested yet, here's why," and that's the finding.
- Never suggest building the product as the test.

Run it when: the moment you catch yourself opening an editor to “just start building.”

2. The Customer Microscope

Why. “Our customer is everyone” is the most expensive sentence in startups. A fuzzy customer produces fuzzy positioning, fuzzy copy, fuzzy pricing, and fuzzy product decisions all the way down. Clarity here compounds everywhere.

What it does. Forces a vague target audience into one specific person hiring you for one specific job: the functional, emotional, and social dimensions of what they’re actually trying to get done.

---
name: customer-microscope
description: Use when your target customer is vague or "everyone." Turns a fuzzy audience into one specific person and the job they're hiring your product to do. Based on Jobs to Be Done.
---

You are a customer researcher who has run hundreds of discovery interviews. You think in Jobs to Be Done: people don't buy products, they hire them to make progress in a specific situation.

Take the product and audience I describe and return:

1. THE ONE PERSON (2 sentences): the single most specific version of my customer. Role, situation, and the moment they realize they have this problem. Not a demographic. A person in a moment.
2. THE JOB (1 sentence): "When [situation], I want to [motivation], so I can [desired outcome]." Fill this in precisely for that person.
3. THE THREE DIMENSIONS:
   - Functional: the practical task they need done.
   - Emotional: how they want to feel (or stop feeling).
   - Social: how they want to be seen by others.
4. WHAT THEY FIRE TO HIRE YOU (1 sentence): the current solution, whether a competitor, a spreadsheet, a workaround, or doing nothing, they'd have to abandon to use you.
5. THE STRUGGLING MOMENT (1 sentence): the specific trigger that pushes them from "this is annoying" to "I need to fix this now." This is when you can reach them.

Rules:
- "Everyone," "businesses," or "people who want X" are not answers. Name one person.
- The job must be something they were trying to do before your product existed.
- If the audience I gave you is too broad to do this, pick the single sharpest segment inside it and tell me why you chose it.

Run it when: you’re about to write a headline, a cold email, or an ad, and you can’t picture exactly who reads it.

Together with Wispr Flow:

Every skill below is just instructions written well. Wispr Flow is the voice-to-text tool I use to draft them: talk the skill through out loud, and it lands as clean, formatted text ready to paste into a SKILL.md

Get 1 month of Wispr Flow Pro for free

3. The Positioning Forge

Why. Positioning is the highest-leverage, most-neglected work a founder does. Get it right and your copy, sales, and pricing write themselves. Get it wrong and no amount of polish saves you. You’re a better answer to a question nobody’s asking.

What it does. Locates the category you’re competing in, the alternative buyers would actually use instead of you, and the one-liner that makes a stranger instantly understand what you do and whether it’s for them.

---
name: positioning-forge
description: Use when buyers say "interesting" and don't buy, or when you can't explain your product in one clean sentence. Clarifies category, competitive alternative, and your wedge.
---

You are a positioning strategist. You believe positioning is about context: a product means nothing until the customer knows what to compare it to.

Take my product and return:

1. THE COMPETITIVE ALTERNATIVE (1 sentence): what the customer would genuinely use if my product didn't exist. Often a spreadsheet, a manual process, or "nothing," not the competitor I'd name. This sets the frame for everything else.
2. THE CATEGORY (1 sentence): the market context I want the customer to file me under, chosen so my strengths become the obvious buying criteria.
3. MY UNIQUE WEDGE (1 sentence): the one thing I do that the alternative can't, stated as a customer benefit rather than a feature.
4. WHO IT'S FOR / NOT FOR (2 bullets): the segment that will love this, and the segment I should actively turn away. Turning people away sharpens positioning.
5. THE ONE-LINER: "[Product] helps [specific person] [achieve specific outcome] without [the pain of the alternative]." Make it concrete and free of jargon.
6. THE CLARITY TEST: rewrite the one-liner one more time as if explaining it to a smart stranger at a dinner party who has 5 seconds of attention.

Rules:
- The competitive alternative is the most important output. If you get it wrong, the rest is built on sand. Pick the real one, not the flattering one.
- No buzzwords. If a line contains "platform," "solution," "seamless," or "leverage," rewrite it.
- The one-liner passes only if a stranger could read it and correctly guess what you sell. If it can't, say so and fix it.

Run it when: people keep saying “cool” and not pulling out their wallet. That’s almost always a positioning problem, not a product one.

4. The Spec Sharpener

Why. Builders bleed time on under-specified work and scope creep. The feature you imagined is rarely the feature you should ship first. The discipline isn’t writing more spec. It’s cutting ruthlessly to the smallest thing that proves the idea.

What it does. Turns a feature idea into a buildable spec with an explicit “what’s out” list, the riskiest unknown surfaced early, and acceptance criteria you can actually check against.

---
name: spec-sharpener
description: Use before building any feature. Turns a rough idea into the smallest shippable spec, names what to cut, and surfaces the riskiest unknown before you write code.
---

You are a staff product manager paired with a pragmatic engineering lead. You have shipped enough to know that the first version should do one thing well and that most scope is fear in disguise.

Take the feature I describe and return:

1. THE ONE PROBLEM (1 sentence): the single user problem this feature solves. If it solves several, name the one that justifies building it at all.
2. THE SMALLEST SHIPPABLE VERSION (3-5 bullets): the minimum that delivers real value to a real user. Not a prototype, but something you'd let a customer touch.
3. EXPLICITLY OUT OF SCOPE (3 bullets): the tempting things we are NOT building in v1. Name them so they stop haunting the build.
4. THE RISKIEST UNKNOWN (1 sentence): the technical or product question most likely to blow up the estimate, and the smallest spike to resolve it first.
5. ACCEPTANCE CRITERIA (3-5 checkboxes): concrete, testable conditions that define "done." Each must be verifiable by someone who didn't build it.

Rules:
- Something must always be cut. If your "out of scope" list is empty, the scope is too big. Look harder.
- No acceptance criterion can be "works well" or "is intuitive." Make it checkable.
- Resolve the riskiest unknown before anything else gets built.

Run it when: a feature idea has more than one sentence of ambition and you’re tempted to start coding it whole.

5. The Ship Inspector

Why. Solo founders and small teams ship fast, often with AI-written code, and roughly half of AI-generated code ships with security issues. A breach can end a company faster than any competitor. You don’t need to be a security expert; you need a fast, honest gate before merge.

What it does. Reviews a diff for bugs, security holes, missing tests, and the specific risks of machine-written code, then flags the one architectural decision worth a real conversation before it’s locked in.

---
name: ship-inspector
description: Use on any pull request or AI-generated code before merging. Returns bugs, security issues by severity, missing tests, and the one decision worth discussing.
---

You are a senior engineer who has watched a startup die from a single leaked credential. You review fast, say only what matters, and you are especially wary of code that was generated rather than written.

Read the diff or code I give you and return only findings:

1. BUGS (if any): exact location, one sentence on the wrong behavior.
2. SECURITY (if any): hardcoded secrets, unvalidated input, injection risks, over-broad permissions, leaked keys. Tag each Critical / High / Medium.
3. MISSING TESTS (if any): the behaviors that will break silently with no coverage.
4. AI-CODE RISK (1 bullet, mandatory): the most likely place this code is subtly wrong because it was generated: plausible-looking logic that doesn't hold, a hallucinated API, a copied insecure pattern. If you find none, say so explicitly after actually looking.
5. THE CONVERSATION (1 bullet): the one architectural or design decision here that deserves a human discussion before merge. Not necessarily a bug, just a decision.

Rules:
- Be terse. One sentence per finding. Don't praise code that's merely fine.
- Never suggest suppressing or silencing an error. Find the cause.
- If the code is clean, say "No blocking issues, ready to merge" in one line, but still deliver the AI-code-risk bullet and the conversation bullet.

Run it when: before every merge, and especially before shipping anything Claude or another model wrote for you.

6. The Conversion Builder

Why. Your landing page is usually the first and only thing a prospect sees. The gap between a 2% and a 7% conversion rate isn’t design polish. It’s whether the page answers the buyer’s real objections in the right order. That gap is the difference between a business and a hobby.

What it does. Builds a hero-to-CTA page structured around buyer psychology, maps where each objection gets handled, and names the single biggest thing currently killing conversions.

---
name: conversion-builder
description: Use to write or audit a landing page. Builds hero-to-CTA copy around buyer objections and names the single biggest conversion killer. Optimizes for clarity, not cleverness.
---

You are a conversion copywriter and CRO specialist. You know that a page converts when it answers the visitor's objections faster than they can raise them, in the order they raise them.

Using the product and customer I describe, return:

1. THE HERO:
   - Headline: the outcome the customer wants, stated specifically. No cleverness.
   - Subhead: who it's for and how it works, in one line.
   - Primary CTA: action-specific button copy. Never "Submit" or "Learn More."
2. THE OBJECTION MAP (4-5 bullets): the objections a real buyer raises, in order, and the section of the page that answers each one. ("Is this for me?", "Does it actually work?", "Can I trust them?", "Why now?")
3. PROOF PLACEMENT (1-2 bullets): where social proof or evidence belongs to defuse the biggest doubt at the moment it peaks.
4. THE SECTION TO CUT (1 bullet): the part most pages include that adds friction or dilutes focus here. Cutting is part of conversion.
5. THE #1 CONVERSION KILLER (1 sentence, mandatory): the single biggest reason this page (or this product's typical page) loses the sale, and the one fix.

Rules:
- Clarity beats cleverness every time. If a headline is clever but ambiguous, kill it.
- Every section must answer an objection or it doesn't belong on the page.
- The conversion killer must be specific. Not "improve the copy." Name the leak.

Run it when: you’re writing a launch page, or traffic is coming but nobody’s converting and you don’t know why.

7. The Pricing Adversary

Why. Pricing is the fastest lever on revenue and the one founders test least. Most pricing is set by gut, copied from a competitor, or anchored to costs, and it quietly communicates the wrong thing to buyers. You need an adversary, not a cheerleader.

What it does. Attacks your price from three hostile angles: the skeptic who thinks it’s too expensive, the buyer who reads cheap as low-quality, and the competitor planning to undercut you. Then it tells you what your price actually signals and the one change to make.

---

name: pricing-adversary

description: Use before launching or changing pricing. Stress-tests your price from three hostile buyer angles and names what it actually communicates. Attacks, does not validate.

---

You are a pricing strategist who has repriced SaaS, services, and physical products. Your job today is to attack my pricing, not reassure me about it.

Take the pricing I describe and run three attacks:

ATTACK 1 (THE SKEPTIC): a buyer who’s been burned before. Too expensive for what it is, or suspiciously cheap. What objection does the price raise that the page never answers?

ATTACK 2 (THE VALUE BUYER): a buyer who reads price as a quality signal. Does this price make me look like the cheap, risky option? What would make the price feel *earned*?

ATTACK 3 (THE COMPETITOR): a rival studying my pricing right now. Where is it exposed? What single move could they make to make my price look wrong overnight?

Then:

1. WHAT YOUR PRICE ACTUALLY SIGNALS (1 paragraph): not what you intend, but what a first-time visitor reads from it about quality, audience, and confidence.

2. THE ONE CHANGE (1 sentence, mandatory): the single pricing or packaging change that fixes the biggest weakness found above. Be specific: a number, a tier, a reframe, not “reconsider your pricing.”

Rules:

- Do not validate the price. Each attack must surface a real, specific concern.

- “What the price signals” is about perception, not your intentions. Be honest.

- The one change must be concrete enough to implement this week.

Run it when: you’re about to publish pricing, or you keep losing deals and can’t tell whether price is the reason.

8. The Numbers Truth-Teller

Why. Most founders avoid their own unit economics because the math feels like someone else’s job, until a metric they trusted turns out to be lying to them. You don’t need a finance degree. You need the few numbers that decide whether the business works, and someone willing to point at the one that’s fake.

What it does. Computes your core SaaS metrics from whatever inputs you have, names the vanity metric flattering your dashboard, gives you the honest runway picture, and tells you the single number to move next.

---
name: numbers-truth-teller
description: Use to sanity-check unit economics and growth. Computes core SaaS metrics, exposes the vanity metric that's misleading you, and names the one number to fix. No finance jargon left unexplained.
---

You are a SaaS CFO who has talked founders out of celebrating fake growth. You care about whether the business actually works, not whether the chart goes up.

From the numbers I give you (MRR, growth, churn, CAC, ARPU, cash, whatever I have), return:

1. THE CORE METRICS (computed): work out what you can from my inputs: ARR, growth rate, LTV, LTV:CAC, CAC payback in months, net revenue retention, runway in months, and a Rule of 40 score if possible. Show the simple math. If an input is missing, tell me exactly what to give you.
2. THE METRIC THAT'S LYING (1 sentence, mandatory): the number on my dashboard most likely flattering me, whether a vanity metric, a misleading average, or growth masking churn. Name it and explain what it's hiding.
3. THE RUNWAY REALITY (1 sentence): how long the cash actually lasts at the current burn and trajectory, not the optimistic version.
4. THE VERDICT (1 line): does this look like a healthy trajectory, a fixable one, or a leaking bucket? One honest sentence.
5. THE ONE NUMBER TO MOVE (1 sentence): the single metric that, if improved, most changes the trajectory, and the lever that moves it.

Rules:
- Explain every metric in plain words the first time you use it. No unexplained acronyms.
- If growth is being propped up by something unsustainable, say so directly.
- The "metric that's lying" is mandatory. If you genuinely can't find one, say which number you'd most want to verify and why.

Run it when: before a board update, a fundraise, or any month where the top-line chart looks great and you want to make sure it’s real.

9. The Pitch Surgeon

Why. Decks rarely fail on design. They fail on narrative. Investors fund a story they believe, with a “why now” they can feel and a founder who can answer the hard question without flinching. The deck is the easy part; the believability is the whole game.

What it does. Builds the narrative arc a raise needs, finds the slide that loses the room, surfaces the question you can’t yet answer, and names the believability gap an investor will probe.'

---
name: pitch-surgeon
description: Use before a fundraise or investor meeting. Builds the narrative arc, finds the slide that loses the room, and names the one question that kills the meeting. Operates on story, not slide design.
---

You are a former venture partner who has sat through thousands of pitches and funded a handful. You know money follows narrative and conviction, not templates.

Take my pitch (deck, summary, or rough idea) and return:

1. THE NARRATIVE ARC (5-7 beats): the story this raise should tell, in order: problem, why now, insight, what you built, traction or proof, the wedge, the vision. One line per beat, in plain language.
2. THE WHY NOW (1 sentence): the specific reason this is the right moment for this company. If you can't make this compelling, the whole pitch is weaker, so say so.
3. THE SLIDE THAT LOSES THE ROOM (1 sentence): the weakest point in the story where an investor's attention or belief drops, and how to fix it.
4. THE QUESTION YOU CAN'T ANSWER (1 sentence, mandatory): the single hardest question an investor will ask that this pitch currently can't survive. Naming it now is the only way to be ready for it.
5. THE BELIEVABILITY GAP (1 sentence): the claim in the pitch most likely to trigger quiet skepticism, and the proof that would close it.

Rules:
- Work on the story and the conviction, not fonts and layout.
- "Why now" and "the question you can't answer" are the two highest-value outputs. Spend the most care there.
- Be the investor in the room, not the cheerleader at the demo day.

Run it when: you’re a week out from investor meetings, early enough to fix the narrative and rehearse the hard question, not the night before.

10. The Focus Forge

Why. A founder’s scarcest resource isn’t time or money. It’s focus. The default failure mode is doing ten reasonable things instead of the one thing that matters. Most “should I do A or B” agony is really a missing framework, and a framework takes ten minutes.

What it does. Reframes the decision you’re actually facing, surfaces the real options (including ones you didn’t mention), names the single highest-leverage move, and forces you to evaluate the most ignored option: doing nothing.

---
name: focus-forge
description: Use when stuck on a decision or drowning in priorities. Reframes the real decision, names the one move that matters most, and honestly evaluates doing nothing. For focus, not more options.
---

You are a founder coach and former chief of staff. You've watched capable founders fail by spreading themselves across ten good ideas instead of one decisive one.

Take the decision or pile of priorities I describe and return:

1. THE REAL DECISION (1 sentence): restate what I'm actually deciding. It's often not the question I asked, so find the decision underneath it.
2. THE REAL OPTIONS (3 max): the genuinely distinct paths, including any I didn't mention. If two of my options are secretly the same, collapse them and say so.
3. THE ONE MOVE (1 sentence, mandatory): the single highest-leverage action right now, the thing that, if I only did this, would matter most. Everything else waits.
4. THE DO-NOTHING DEFAULT (1 sentence): what happens if I make no decision and let things ride. Sometimes that's fine; usually it's the option nobody evaluates honestly. Name it.
5. THE SECOND-ORDER EFFECT (1 sentence): the consequence of the chosen move that won't show up immediately but will shape what happens next.

Rules:
- The real decision is usually not the stated one. Look for it before answering.
- "The one move" must be a single action, not a list. Forcing the choice is the entire point of this skill.
- The do-nothing default is mandatory and must be evaluated honestly, not dismissed.

Run it when: you’ve been circling a decision for more than a week, or your to-do list has fifteen “important” things and you can feel yourself doing none of them well.

4. Bonus: The Skill That Builds Skills

Once these click, you’ll probably want your own. A skill that captures your exact brand voice, your weekly investor update from your metrics, your industry’s compliance checklist.

Anthropic ships an official skill-creator skill that scaffolds new skills correctly (frontmatter, structure, the right description wording most people get wrong). Turn it on, describe the workflow you repeat constantly, and let it write the skill for you.

The best thing you a founder can do is build a few that fit their work exactly, once, and stop starting from zero.

5. Recap: Build Once, Run Forever

Ten skills. One afternoon of setup.

Every founder task that used to start with a blank prompt now starts with Claude already operating at your standard: killing bad ideas before you build them, attacking your own pricing, telling you which number is lying.

Remember: The prompt expires when the tab closes, but the skill is still there tomorrow.

Build once. Run forever. Now go save them.

The best ones turn a powerful capability into a business outcome, then carry the customer with them until the customer can run alone. They are scarce, expensive, and brutal to interview for, because the thing that makes one great resists a standard rubric.

Insight Partners recently put four leaders running FDE teams at ServiceNow, Databricks, Workato, and Wonderful in one room. Their answers agreed more than they disagreed, and the pattern underneath is buildable.

So I turned that pattern into an operating manual. The charter, the job description, the interview loop, the take-home, the scorecard, the comp bands, the career ladder, the pod model, the deployment runbook, the retention system, and the budget math. Everything below is built to be copied and run this quarter.

Inside the playbook:

▫️ The one-line charter and the full FDE job description, copy-paste

▫️ The complete interview loop, the “break-it” test script, the take-home, and the reference-check questions

▫️ The weighted scorecard with a calibration guide

▫️ The five-level career ladder and the comp blend with real bands

▫️ The pod model, the RACI, and staffing by deal size

▫️ The 90-day deployment runbook that ships the first outcome

▫️ The retention system, the anti-burnout rotation, and the 1:1 template

▫️ The bench budget model, so you can size the team before you hire

The FDE Operating Manual

Everything below is built to run inside your company this quarter. Copy the templates, adapt the brackets, ship:

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Read more

Drew Houston recorded a three-minute screencast anyway, posted it to Hacker News, and watched the beta waitlist climb from 5,000 to 75,000 in a single day.

Behind that clip sat a demo, and the demo did the entire job.

That should unsettle every founder who spent a month perfecting a deck. DocSend studied thousands of them and found the average investor gives a seed deck about 3 minutes and 44 seconds. Only 58% get viewed to the final slide.

Weeks of work, skimmed and dropped before the ending.

together with Granola

A great demo ends with a dozen follow-ups. The deck, a reference, a specific number, a second call. Then you walk into the next meeting and the last one is already fading.

That is a meeting problem, not a memory problem.

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That’s because even the best pitch deck can only tell an investor what a founder claims to have built. A live demo proves the thing was actually built, and proves the founder can run it in real time while someone watches.

One is a description. The other is evidence.

Most founders still treat the demo as the easy part, the piece to sort out the night before. That habit explains why so many demos land flat in rooms nobody could afford to lose.

A top-tier demo is not a feature tour. It works as a compressed argument for why someone should believe you, and any argument can be built on purpose instead of left to luck and nerves.

Here is how the best founders build it.

Table of Contents

1. Why the Demo Carries Weight the Deck Cannot

2. Decide the One Belief You Want to Install

3. Open on the Pain, Then Show the Old Way

4. Run It Live and Kill the Dead Air

5. End With the Ask, Never a Shrug

1. Why the Demo Carries Weight the Deck Cannot

Investors are not buying an idea. They are buying a private bet on whether this founder can build the thing the idea demands.

A live demo gives them the cleanest read on that bet they will ever get.

A slide promising real time sync across every device costs nothing to write. A working version of it, running in the room, costs the one thing a slide cannot fake: proof that it got built.

The Dropbox clip won over a crowd of skeptical engineers for exactly that reason.

By Houston’s own account, the early product ran only on Windows and strained under more than a few users. The screencast still showed the core magic clearly enough that Hacker News believed the rest would follow.

They were not reacting to a promise. Instead, they were reacting to proof of the single moment that made the product worth wanting.

The same logic rules a room with a partner across the table.

Jina Yoon wrote up what she saw after watching more than 50 hackathon demos back to back, and one pattern jumped out. Teams that had already shipped something, even to a thin slice of real users, carried a credibility that no amount of polish could fake for the teams who had only described their plans.

One team announced their project was already live for a quarter of PostHog’s users, with real data moving through it. That one fact reframed how the room heard everything else they said.

Shipping before the demo is the strongest version of this, and it points at the principle under every good demo. A founder stands in that room to be believed, and belief follows evidence.

With the demo established as the best evidence on offer, the work becomes deciding what it should prove.

2. Decide the One Belief You Want to Install

Before the laptop even opens, a founder should pick the single belief they want lodged in the audience’s head at the moment it closes.

Not three beliefs. One.

The whole demo then becomes evidence for that one claim, and anything failing to serve it gets cut, however clever it happens to be.

This discipline earns its place mechanically, not aesthetically, because of how decisions get made once the founder leaves the room.

The person watching a demo rarely holds the power to say yes alone.

A junior partner has to re-pitch the company at the Monday meeting. A champion inside a customer has to sell it upward to a VP who controls the budget.

They will carry one sentence out of that room, maybe two, and they will carry it imperfectly.

A demo with a single sharp point survives the retelling. A demo with 5 points dies in a secondhand summary the founder never gets to hear or correct.

The demos people actually remembered were built around one point that every other part served, usually the problem and the one move that solved it.

Get to that point fast. Context, if any is truly needed, belongs in a sentence or two, never in the founding story.

The violation test

A point that cannot be said in a single plain sentence before the screen turns on is not a demo yet. It is a walkthrough, and a walkthrough earns a polite pass.

But, knowing the one point is only half the job. The harder craft involves making an audience feel that point rather than simply hear it.

3. Open on the Pain, Then Show the Old Way

Audiences do not care about a solution until they feel the problem it answers, so the strongest demos open on pain rather than product.

The first seconds should drop the audience into a situation they recognize so completely that they are nodding before anything built has appeared on screen.

A founder earns the right to demo by proving they understand the problem first.

One small framing choice does most of the heavy lifting here, and a racing pulse makes it easy to forget.

Saying “we built a tool that manages support tickets” keeps the audience as spectators, watching a company from the outside.

Saying “imagine you are on call, and six incidents are firing at once” drops them inside the scenario, feeling the stakes from where they sit.

It’s true that demos using this second person framing are able to pull the room in far more reliably than the ones narrating what the team had made.

Then comes the move that separates a memorable demo from a forgettable one: showing the product directly against the thing it replaces.

A one step workflow means nothing in isolation, because the audience has no yardstick for whether one step is any good.

Show the painful current version first, the six clicks and the copy and paste and the spreadsheet rotting in someone’s downloads folder. Then fold all of it into the single new step, live, in front of them.

The comparison manufactures stakes the product alone never could, and it does the selling on its own, because the audience reaches the obvious conclusion instead of hearing anyone assert it.

Sequenced together, those two ideas form the spine of most great product demos.

Open on the pain everyone has lived. Make it personal with second person framing. Walk through the old slog, then resolve it.

By the time the product appears, the audience is no longer weighing a feature. They are watching relief arrive.

4. Run It Live and Kill the Dead Air

The word demo carries an obligation founders too often let themselves dodge

If the thing got built, then show it doing the thing.

Anyone can describe a product on a slide, and a slide about a product reads as a far weaker object than the product itself in motion.

A rough live demo beats a beautiful slide about a product that may not exist yet.

Running live raises the stakes, because live things break at the worst possible second. Retreating to slides fixes nothing. Invisible preparation does.

The best demos pre-load and cache everything that can stall, because dead air is where a room’s attention drains away.

The usual culprits are slow queries, agent responses, long builds, and a founder typing a URL by hand instead of bookmarking it.

Yoon compares this to television chefs prepping their ingredients before the camera rolls, so every second on air stays interesting.

Keep a folder of screenshots ready for the moment the WiFi dies, because on a long enough timeline it will.

Two delivery habits cost nothing and change the result.

The first habit

Stop apologizing. “Sorry, this is still rough” trains a room to discount everything before they have even seen it.

Founders run a subtler version constantly, with throwaway lines like “ignore the design” or “this part is not really built yet.” Each hedge lowers the ceiling on how impressed anyone is allowed to be.

When something breaks, push forward to the next path rather than narrating the failure on the way down.

The second habit

This is all about the energy. A great demo is often just a good one delivered by someone with real conviction behind it.

An audience borrows its confidence from the founder’s. Flatness spreads in the wrong direction.

None of this means being loud. It means visibly believing the thing on screen, which, for a product the founder actually built, should not have to be an act.

A live run that lands cleanly leaves exactly one mistake still capable of wasting all of it.

5. End With the Ask, Never a Shrug

A demo without an ending leaks every drop of momentum at the precise second attention peaks.

The room is finally leaning in, and then far too many founders close with “so, yeah, that’s it,” and hand the energy straight back.

Those final five seconds carry more weight than any other moment in the demo. They should point at one specific next step.

What that step looks like depends entirely on who sits across the table, and this is the part most founders skip.

An investor needs the shape of the round, the check size, and a clear sense of what the next twelve months buy. A prospective customer needs the path to a pilot and a name to contact on Monday. A recruit needs to feel what they would own by joining.

The arc of the demo stays identical across all three. Only the closing ask changes, tuned to the single decision this particular person can make.

Houston closed his screencast with one form to join the waitlist and nothing else competing for the click.

The cleanest demos resolve into a single obvious action rather than a menu, because a menu invites people to pick the easiest item on it, usually “let me think about it.”

When a room knows exactly what to do next, a real share of them go and do it.

Stripped back, a top-tier demo follows one plain arc.

Open on a pain the audience feels, framed as their own. Raise the stakes with the costly old way. Resolve it into the single point, live and uninterrupted, delivered without hedging. Then say precisely what to do next.

The software was never the real point.

A demo is a five minute test of whether a founder understands the customer’s problem, can build, and can make another person believe. That happens to be the whole job, performed at speed in front of an audience.

Founders who treat the demo as an afterthought are throwing away the best audition they will ever get.

From top insights a

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• The 2026 Deep Tech Report by Drumbeat Capital 🔬
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• The $1 Billion Race to Automate Warehouses 📦
  Physical AI funding in the first half of 2026 surpassed the total invested across all of 2025. Large warehouse automation deals accelerated, with Sequoia leading investment activity and NVIDIA expanding its safety initiatives. [PitchBook]

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Recently Launched Funds 💸

• Lionlight Capital closed its inaugural $215M fund to invest in high-growth technology companies across Europe.

• Uplift Investors closed its first $670M fund, backing ambitious founders building category-defining businesses.

• Citation Capital closed its inaugural $1.2B fund to invest in innovative, high-growth companies.

• Acurio Ventures closed Acurio Secondaries I FCR at approximately €115M, targeting venture-backed secondary opportunities.

The AI Corner

The Claude Code system that replaces a 5-person team

Most Claude Code articles list a few hacks and stop there…

Read more

3 months ago · 45 likes · Ruben Dominguez

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Hottest Deals 💥

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• Cover Genius raised $100M in funding to scale its embedded insurance platform worldwide. (read more)

• Spectro Cloud raised $100M in Series D funding to expand its Kubernetes management platform. (read more)

• TerraFirma raised $100M in Series A funding to scale climate infrastructure technology. (read more)

• Singularity raised $80M in Series A funding at a $400M valuation to build AI infrastructure. (read more)

• SwissTo12 raised $70M in Series C funding to expand satellite communications technology. (read more)

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The Live Fundraising List

Every startup currently raising, with the deck attached. New cohort every month:

Read more

The good ones do the opposite. They send a packet nobody has questions about, four days early. Then they spend the whole hour on the two or three decisions that actually move the company.

That is the difference between a status update and a board meeting.

Below is the entire system. The email that carries it, the deck structure slide by slide, the metrics page that surfaces the one tension every board should see, and the agenda that puts formalities last. Plus two files you can use tonight: the deck and the model, already built.

Fill the model. Paste the numbers into the deck. Send the email. Done before dinner.

What’s Inside

▫️ The Board Deck Template — 12 slides, pre-structured, dark and investor-ready. Native charts you edit in PowerPoint. Every slide carries the right section and a prompt to fill.

▫️ The Board Metrics Model — an Excel model where you type five inputs and it computes gross margin, net burn, cash, runway, revenue per head, and burn multiple on its own. The runway row turns red as it falls.

▫️ The pre-read email, the agenda, and the five rules — copy-paste ready.

Start your free 7-day trial → Cancel anytime. First subscribers lock in 50% off for life.

Two files and a system beat a $300 advisory hour. This costs less than lunch.

The System

Everything below is built to be copied, filled, and sent:

1. The Pre-Read Email

Send this with both files attached, four to five days before you meet.

Get 50% off forever

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2000 rows. Crunchbase, a Twitter thread, someone’s shared Airtable. Sorted by fund size, because that felt like a metric.

Then 200 cold emails. Four replies.

The email was fine. The list was dead.

The Uncomfortable Math

Take any raw list of 2,000 investors.

A third sit in funds past their investment period, managing reserves and taking meetings out of politeness. Another chunk have written zero checks at your stage in 18 months. Some back a competitor, which turns the meeting into a diligence call wearing a smile.

About 2% survives. 40 names.

Those forty are the entire fundraise. The rest is a month of your life spent teaching strangers what your company does.

Qualification Is the Bottleneck

Anyone can pull 5,000 names in an afternoon. Collection was solved years ago.

The hard part is deciding which of those 5,000 can write your check, wants to write it, and is allowed to write it right now.

A sorted list gives you an order. A scored list gives you a decision. Rank every investor 1 to 10 across five weighted dimensions and the bottom 60% disappears without anxiety, because the score tells you why they are gone. Better: the reason someone scored a 9 belongs in line one of your message to them.

Skip the guides teaching you to scrape LinkedIn. Stale titles, restricted accounts, and the exact posture that makes investors stop replying. Filings update. Bios lie.

The Loop

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Monday morning: 40 names, and a first line for each one.

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1. The Kill List

Run these six filters before you score anything. They remove 60% to 80% of a typical file in about twenty minutes.

Copy-paste prompt:

Read more

The co-founder of Monzo and GoCardless. A group partner with more than 1,000 office hours behind him and a portfolio worth $5 billion.

He took a leave of absence to join Anthropic’s compute team.

His reason, in his own words: as we enter the early stages of recursive self-improvement, availability of compute becomes one of the most important issues to solve.

A consumer fintech founder. Working on servers.

That should tell you something.

The Nine Weeks

Here is the run:

Karpathy went first. Anthropic confirmed he joined the pretraining team, and that he would start a team focused on using Claude to accelerate pre-training research.

Claude, making Claude better.

The company that pitched investors on that idea in 2022 is now executing it. The leaked deck they used to raise is the best artifact in venture right now, and it reads very differently once you know how the story ends.

Then came the Nobel laureate. John Jumper, who shared the 2024 Nobel Prize in Chemistry for AlphaFold, left Google DeepMind after nearly nine years.

Then the mathematician. Jelani Nelson, chair of Berkeley’s EECS division, took leave to become a Member of Technical Staff. His Harvard algorithms lectures have 21 million views.

“Anthropic is collecting them like Pokémon.” One reply on X, and the most accurate thing anyone has written about 2026.

The Line Everyone Skipped

Two of these people went to compute.

Nordeen is an xAI founding member, and he announced the same day Anthropic struck a deal with SpaceX to rent capacity at xAI’s Colossus 1 data center in Memphis.

Blomfield has zero infrastructure background. He picked compute anyway.

The numbers behind that choice: up to 1 million Google TPUs, more than 1 gigawatt coming online in 2026 under a deal worth tens of billions, plus 220,000+ Nvidia GPUs through cloud partnerships.

Compute stopped being an engineering problem. It became a deal problem, a supply problem, a negotiation problem.

So they hired a founder.

Watch where a company sends its best people. That is where the pain lives. The same read applies to the funding rounds you see announced every week, and it is exactly the signal top investors screen for before they write a check.

Fable 5 Is Free for Six More Days

The model rollout has been messy. The extension is now the story.

Anthropic extended free Claude Fable 5 access through July 19. That is the third delay since the original June 22 cutoff, after pushes to July 7 and then July 12.

The terms:

▫️ Pro, Max, Team, and eligible Enterprise plans get Fable 5 for up to 50% of weekly usage limits at no extra cost

▫️ Claude Code weekly limits stay 50% higher through the same date

▫️ After that, access moves to prepaid credits at $10 per million input tokens and $50 per million output tokens

▫️ Available on web, mobile, desktop, Claude Code, Claude Cowork, Claude Design, and Claude for Microsoft 365

Six days of frontier capability at zero marginal cost, then the meter starts.

I wrote the full Fable 5 playbook for exactly this window. If you want the shortest path to output that survives the deadline, run the leverage kit first, then the chief-of-staff system inside Claude Code.

The AI Corner

Claude Fable 5 is free for 4 more days. Here is the full guide

Anthropic shipped its most intelligent model ever, a government switched it off, and now it is free for everyone on a paid plan…

Read more

23 days ago · 78 likes · 2 comments · Ruben Dominguez

The pressure behind those extensions has a name. Artificial Analysis found GPT-5.6 Sol delivers comparable performance at lower cost, with one developer spending $16 on Sol for work that cost $63 on Fable 5.

Anthropic is buying loyalty with time.

$87 Million to $47 Billion

The run rate, month by month.

Salesforce took about 20 years to reach $30 billion in annual revenue. Anthropic did it in under three years from a standing start.

The rest of it:

1. A $65B round at a $965B valuation, passing OpenAI for the first time

2. A confidential S-1 filed June 1, with a listing possible this fall

3. Q2 revenue expected at $10.9 billion, roughly double the prior quarter, with the first profitable quarter in sight

4. Claude Code crossed $1 billion in annualized revenue within six months of launch

Now read that against the 2022 pitch deck again. Then read it against the 200+ decks that raised billions. The pattern is the same every time: the deck sells a mechanism, the market pays for the mechanism working.

One asterisk. Anthropic reports a meaningful share of revenue on a gross basis, counting end-customer cloud spend routed through resellers rather than only its net take, so the headline inflates against peers reporting net.

Growth is genuine. Comparability is murkier than the headline suggests. That distinction is the whole job when you model a company properly, and it is the same trap that shows up in every SaaS financial model built on top-line optimism.

Want to run this analysis yourself, on any company, tonight? The 12-prompt stock analyst build and the 4-level Claude investing system do exactly that.

They Are Suing the Pentagon

Here is the part that could break everything above.

The Pentagon designated Anthropic a supply chain risk, a label usually reserved for firms tied to foreign adversaries.

The trigger was two conditions Anthropic held: Claude stays away from mass surveillance of US citizens, and away from fully autonomous weapons. The Pentagon wanted access for all lawful purposes.

Talks collapsed.

Anthropic became the first American company to receive that designation. It filed two federal lawsuits in March, arguing First Amendment retaliation and procedural violations.

The scoreboard so far is split. A San Francisco judge granted a preliminary injunction blocking enforcement of a ban on Claude. The D.C. Circuit denied a stay, leaving Anthropic outside DOD contracts while the case runs.

And then they hired Teresa Carlson, the AWS public sector legend, as their first-ever global head of public sector, as tensions with the U.S. government continued.

Suing the Pentagon with one hand. Hiring the person who sold to the Pentagon for 20 years with the other.

That is a strategy.

The export episode rhymes. Three days after Fable 5 launched on June 9, the Commerce Department issued a directive restricting both Fable 5 and Mythos 5 to US nationals only. The ban expired July 1.

Regulatory risk is now a line item in a near-trillion-dollar S-1. Every founder raising in AI should read that as a term sheet issue waiting to happen.

Four Reads

1. Mission still moves people at the top. SignalFire partner Heather Doshay told the Wall Street Journal that when she asks candidates to name their dream company, Anthropic comes up more often than anyone else. Karpathy, Jumper, and Nelson all took roles they could have priced higher elsewhere.

2. Compute is the constraint. Both compute hires landed alongside infrastructure deals. Follow the people, find the bottleneck.

3. Recursive self-improvement went mainstream. Karpathy’s charter is Claude accelerating Claude. Blomfield used the phrase in his resignation post. Two months ago that vocabulary lived on forums.

4. Policy is now product. A company approaching a trillion dollars is in court with its own government over how its product may be used.

For the operating side of this, three moves this week: loop engineering for coding agents, the Claude Cowork setup, and the 10 Cowork workflows that actually hold up.

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FAQs:

Q: Who has Anthropic hired in 2026?

A: Andrej Karpathy (OpenAI co-founder, pre-training), John Jumper (Nobel laureate, ex-Google DeepMind), Tom Blomfield (Monzo founder and YC group partner, compute), Ross Nordeen (xAI founding member, compute), Jelani Nelson (UC Berkeley EECS chair), Marcus Fontoura (former Azure Core CTO), Teresa Carlson (global head of public sector), and Eric Boyd (ex-Microsoft Azure AI, infrastructure).

Q: Why did Tom Blomfield leave Y Combinator for Anthropic?

A: He took a leave of absence to join Anthropic’s compute team alongside co-founder Tom Brown. He said that as the industry enters the early stages of recursive self-improvement, the availability of compute becomes one of the most important problems to solve.

Q: How long is Claude Fable 5 free?

A: Through July 19, 2026, on Pro, Max, Team, and eligible Enterprise plans, for up to 50% of weekly usage limits. After that, access requires prepaid usage credits at $10 per million input tokens and $50 per million output tokens.

Q: When is the Anthropic IPO?

A: Anthropic filed a confidential draft S-1 with the SEC on June 1, 2026, with reporting pointing to a possible listing in the fall of 2026. Share count, price, and date remain unset publicly.

Q: What is the Anthropic Pentagon lawsuit about?

A: The Department of Defense designated Anthropic a supply chain risk after contract talks broke down over two conditions: that Claude would stay out of mass surveillance of US citizens and out of fully autonomous weapons. Anthropic filed two lawsuits in March 2026 alleging First Amendment retaliation and procedural violations. A California judge granted a preliminary injunction, while the D.C. Circuit denied a stay.

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The AI Corner

The Claude Code system that replaces a 5-person team

Most Claude Code articles list a few hacks and stop there…

Read more

3 months ago · 45 likes · Ruben Dominguez

VC Jobs 💼

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There is a paradox in the venture capital industry today. Capital is still flowing, but the people writing the checks are suddenly questioning their own methods.

Venture capital used to be a cottage industry of outsiders and eccentrics. A small circle of people betting on things that sounded like science fiction, in half-empty offices.

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However, the same industry that helped finance the modern technology economy has begun asking itself difficult questions. Returns have become harder to predict and exit timelines are stretching. Startups are staying private longer, requiring far larger pools of capital than the venture model was originally built to handle.

We are seeing a system designed for speed and agility try to navigate a world of massive scale and slowing exits. The real question is not just where the next big winner is coming from. It is whether the model that gave us the modern world can survive its own growth.

To answer that, we need to look at how the most powerful financial force on the planet was built, and what the future holds.

Table of contents

1. Where Venture Capital Actually Came From

2. How Venture Became the Engine of the Tech Economy

3. The Fever Dream and the Morning After

4. The Civil War Inside the Asset Class

5. The Value-Add Arms Race

6. What the Future Holds for Venture Capital

7. Why Venture Capital Still Matters

1. Where Venture Capital Actually Came From

What is essential to this story is to understand that the venture capital industry did not begin as a polished financial sector. Back in the day, there was no appetite for risk.

If you were an engineer in the late 1940s with a breakthrough in radar or semiconductors you were essentially an island. You couldn’t just walk into a bank and ask for a loan, because banks were, and still are, in the business of certainty.

The problem was that some ideas with enormous potential were far too risky for traditional finance. Banks favored predictable borrowers with collateral and steady cash flows. The kinds of companies that eventually built the modern technology economy offered none of that.

What they offered instead was uncertainty and the possibility of extraordinary success.

And that gap in the market created a need for the venture capitalist. For the people who understood that the future is built on a mountain of failed experiments.

This is what we know now as the “venture model.” Most investments would fail or produce modest outcomes, while a small number would succeed on a scale large enough to repay the entire portfolio.

This dynamic created what investors later described as a power-law business, where a few companies generate the overwhelming share of returns.

Before legendary firms like Kleiner Perkins and Sequoia Capital existed, there was just “The Group.” This was a literal circle of wealthy friends in San Francisco who met for lunch to look at deals. If they liked a founder, they each chipped in about $25,000 on a handshake.

Silicon Valley became the natural center of this system. The region already combined universities, engineers, and defense-funded research. Venture capital added another ingredient: risk capital willing to back founders long before their companies generated revenue.

Over time the industry adopted the limited partnership (LP) structure, pairing institutional investors who supply the capital with venture firms that manage and deploy it across portfolios of startups.

Their participation transformed venture capital from a niche experiment into a recognized investment category.

So even as the industry scaled, its core logic remained unchanged. Venture capital was designed for patience, uncertainty, and the rare companies capable of reshaping entire industries.

2. How Venture Became the Engine of the Tech Economy

For decades the venture model remained relatively small. What eventually turned it into a central force in the global economy was not simply the growth of venture firms, but successive technology waves that required exactly the kind of capital venture was designed to provide: patient, risk-tolerant funding for ideas whose markets were still uncertain.

The Genesis: Semiconductors

The first major expansion came during the semiconductor era. Companies building integrated circuits and computing hardware required large upfront investment long before they produced revenue.

Venture capital filled that gap, funding firms that became the foundation of Silicon Valley’s early technology ecosystem.

Around the 1950s, eight brilliant scientists were working for William Shockley, the co-inventor of the transistor. Shockley was a genius but a notoriously difficult, paranoid manager, so they decided to walk out.

History calls them the Traitorous Eight. They had no plan until they met Arthur Rock, a young banker who convinced them to start their own company instead of just finding new jobs. He found a backer to put up $1.38 million, and that single act of rebellion created Fairchild Semiconductor. This was the “mother ship” that eventually gave us Intel and birthed the DNA of Silicon Valley.

The Boom: The Era of the Internet

The internet boom of the 1990s expanded the VC model even further. Software companies could suddenly reach global markets far faster than traditional businesses, dramatically increasing the potential scale of successful startups.

Even after the dot-com crash reset expectations, the infrastructure of the internet economy remained in place.

In the following decade, venture investors backed the next wave of companies built around mobile computing, cloud infrastructure, and software delivered over the internet. The SaaS model introduced recurring revenue and global distribution, making software companies especially attractive to growth-oriented investors.

By the 2010s the industry entered what became known as the unicorn era, as startups began reaching billion-dollar valuations while still private, supported by larger funding rounds and an expanding global investor base.

Today: Startups Go Global

At the same time, the economics of building startups were changing due to three main factors:

1. Cloud infrastructure reduced the cost of launching software products

2. Open-source tools lowered engineering barriers

3. Online distribution allowed young companies to reach customers without massive physical infrastructure

These factors together triggered an explosion of entrepreneurial activity beyond Silicon Valley. Venture capital followed that opportunity, and over time startup networks now exist on every continent emerged across Europe, Asia, and Latin America.

And thus, what began as a niche financing model gradually became the primary mechanism through which new technology companies were created, scaled, and brought to market.

3. The Fever Dream and the Morning After

For most of its history, venture capital expanded gradually alongside technology cycles. However, the most recent acceleration happened during the period between 2020 and 2021.

In less than two years, the industry experienced an extraordinary surge of capital that reshaped how startups were funded and how venture firms operated.

When Capital Became Abundant

The immediate trigger was the global monetary environment. Interest rates had already been low for years, but the pandemic response pushed them even lower. Central banks flooded financial markets with liquidity, sending investors in search of assets that offered growth.

Technology was the obvious winner. With the world locked down, cloud software and digital commerce became our entire reality. Suddenly, every fund on the planet wanted a piece of the next big thing.

Venture funding surged. Investment levels reached record highs, startups raised capital at unprecedented speeds, and valuations climbed rapidly.

The Arrival of Crossover Capital

One defining feature of the boom was the entry of hedge funds, private equity firms, and large asset managers into late-stage venture deals.

They brought checks so large they made traditional VCs look like they were playing with pocket change. Valuations skyrocketed. The “Unicorn” became a common sight rather than a rare beast, and companies stayed private for years, fueled by an endless supply of cheap cash.

The Correction That Followed

The music stopped in 2022. Inflation arrived, interest rates climbed, and the public markets took a sledgehammer to tech stocks. The “easy” money disappeared overnight.

Today, the industry is in a period of intense soul searching. Investors have now moved beyond “correction” talks. They are now wondering if the hyper-inflated model of the last few years was a glimpse of the future or just a giant, expensive mistake.

4. The Civil War Inside the Asset Class

There is indeed a debate going on among VCs today.

Some believe the model has drifted away from its original strengths. Others argue the industry is simply evolving to match the scale of modern technology.

Ultimately, the question that needs to be asked is this:

Can venture capital scale without losing its soul?

Perspective 1: Venture Capital Has Become Too Institutionalized

On one side are the purists. They argue that venture has become “Institutionalized.” When a fund like a16z grows to $15 billion, it stops being about high-conviction bets on weird ideas and starts looking like a financial factory.

To return that much capital, you need massive, predictable winners. This creates a subtle form of risk aversion where investors favor founders who “look the part” and business models that follow a known playbook.

It is pattern matching disguised as insight. They worry we have traded raw intuition for process-driven safety.

Perspective 2: Venture Capital Must Scale

On the other side are the pragmatists who say the venture capital industry simply had to grow up.

The problems we are solving today are just bigger. You cannot build a foundational AI model or a climate tech breakthrough with a small check and some advice over lunch.

Many emerging fields now require far more capital than earlier generations of startups. Artificial intelligence infrastructure, advanced semiconductor design, climate technology, and biotechnology involve longer development cycles and significant upfront investment.

The structure of technology companies has also changed. Successful startups increasingly remain private for longer periods, raising multiple rounds before considering public markets.

Venture firms have responded by expanding their role from early investors to long-term capital partners that support companies throughout their growth and are becoming “platforms” that provide recruiting, policy experts, and global networks.

In this view, the venture platform is a necessary tool for a more complex world.

The tension lies in the middle. We are all trying to figure out if you can run a billion-dollar financial machine while still maintaining the gut-level risk tolerance that built this industry in the first place.

5. The Value-Add Arms Race

For much of the industry’s history, venture firms operated as small partnerships focused primarily on selecting investments and advising founders. It was a “pick and advise” model.

But as capital became a commodity, the top firms realized that money alone wasn’t enough to win the best deals. If five different firms are offering the same valuation, why should a founder pick you?

The answer was the Service Layer. Venture firms began transforming from small partnerships into massive operating platforms. They stopped being just investors and started being company builders. They built dedicated teams for:

• Talent: Recruiting the first ten engineers or a seasoned COO.

• Go-To-Market: Helping a technical founder figure out how to actually sell to enterprises.

• Networking: Facilitating warm introductions to other investors or potential partners.

• Policy: Navigating the regulatory minefields of fintech, healthcare, and AI.

Winning the Term Sheet War

Most VCs aren’t doing all this because they are altruistic. This is about survival. It’s about competing.

Today, the most sought-after founders often have ten term sheets on their desk at any time. To win, a firm has to prove they can de-risk the company’s future.

We are seeing a power-law dynamic play out among the firms themselves. The “Alpha” firms, which are the ones with the biggest platforms and the best networks, are able to attract the strongest founders.

Those successes reinforce their reputation, making it even easier to win the next big deal. What started as a few guys in a room has become a global network of operators, lobbyists, and recruiters, all working to ensure their portfolio companies don’t just exist, but dominate.

6. What the Future Holds for Venture Capital

Venture capital has never evolved in a straight line. We’ve had periods of explosive growth followed by long winters of reflection.

Right now, it feels like the industry is splitting into two very different worlds, a “barbell” effect that will likely define the next decade.

A Barbell Industry

At one end of the barbell, you have the Mega-Platforms. These are the global institutions that operate across every stage of a company’s life. They have the billions to fund massive AI infrastructure and the massive operating teams we just discussed.

They are the “safest” bet for a founder who needs a global machine behind them.

At the other end, we are seeing the return of the Specialists. These are smaller, leaner funds that look more like the “cottage industry” of the 1960s.

They don’t try to be everything to everyone. Instead, they focus on a single niche. Maybe it’s bio-manufacturing, or seed-stage startups in Latin America, or a specific flavor of open-source software.

They win because they know their world better than any generalist ever could.

Liquidity and the AI Acceleration

Besides the structure of the industry, its mechanics are changing too.

As companies stay private longer, the “IPO or bust” mentality is starting to fade. We are seeing the rise of secondary markets, where employees and early investors can sell their shares without waiting ten years for an exit. This keeps the ecosystem fluid even when the public markets remain frozen.

Finally, we have to talk about AI. If artificial intelligence can automate 80% of what an engineering team does, the cost to start a company drops toward zero.

We might see an explosion of “micro-startups” that need very little capital to reach millions in revenue. Conversely, the companies building the models themselves will need more capital than anything we’ve seen before.

At the end of the day, venture capital isn’t going away, but it is shedding its old skin. The core purpose remains the same. The firms that win are the ones who are able to fund the uncertain future before the rest of the world catches on.

7. Why Venture Capital Still Matters

For all the noise about bloated funds and broken models, the fundamental reason venture capital exists hasn’t changed. Banks and public markets are built to reward the past. They crave spreadsheets, collateral, and the safety of what we already know.

But innovation is inherently fragile and often looks like a mistake until the moment it becomes inevitable. This industry is the only mechanism we have designed to absorb the risk that traditional finance will not touch.

We will probably see funds fail and cycles turn, but the core engine is permanent. As long as there is a founder with a wild idea and zero collateral, there will be a need for conviction capital.

Venture is the bridge between a fragile “maybe” and a global reality. It is how we fund the future before the rest of the world even knows it is coming.

But when we are talking about exceptional results, there is a story that goes far beyond any normal expectation.

In 2010, an $8.4 million seed fund was raised with no institutional backing, no legacy franchise, and no committee architecture. A single general partner ran it.

Before we get into how:

The best AI deals of the next decade are being funded right now.

The founders building the next Sacca-level returns are already taking checks. Most people will never see the deals…

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Lowercase Capital is widely believed to have returned roughly 200x to 250x to its investors. A limited partner who committed $100,000 could have seen approximately $25 million returned over time. In an asset class where a 3x net outcome over a decade qualifies as strong and 5x places a firm near the top tier, this sits far outside conventional VC returns.

This is the story of how Chris Sacca built what might be the most successful investment fund ever. Not a stroke of luck, but the result of a few simple, smart choices that most people thought were too risky or too small to care about.

By the time he was done, those few million dollars had grown into billions. The fund is widely believed to have returned roughly 200x to 250x to its investors.

A limited partner who committed $100,000 could have seen approximately $25 million returned over time. In an asset class where a 3x net outcome over a decade qualifies as strong and 5x places a firm near the top tier, this sits far outside conventional VC returns.

To see how he pulled it off, we have to look past the fame and at the underlying logic that allowed a tiny fund to win bigger than the largest banks in the world.

Table of Contents

1. Before Venture Capital There Was Leverage and Loss

2. Google Was The Training Ground

3. Why a Small Fund Could Do What Large Funds Could Not

4. Keeping Your Slice of the Pie

5. Three Companies, One Fund, and the Reality of Concentration

6. Standing Out By Being Different

7. Why This Was a Rare Moment in Time

1. Before Venture Capital There Was Leverage and Loss

Long before Chris Sacca became associated with extreme outcomes in venture capital, he encountered capital in its least forgiving form.

In the late 1990s, while still in law school, he traded technology stocks using brokerage loopholes that allowed heavy leverage. Small sums expanded rapidly as the dot-com market inflated, reinforcing the sense that pattern recognition was skill rather than circumstance.

In rising markets, leverage feels like intelligence multiplied.

Leverage Without Friction

The lesson of that period wasn’t about brilliance but about amplification. Leverage compresses time between decision and consequence while magnifying both conviction and exposure.

When prices move upward without interruption, risk feels manageable because it remains unrealized. Gains compound quickly while judgment adjusts slowly.

The Reversal That Recalibrates

So when the market inevitably collapsed, that amplification worked in reverse. Positions inverted and Sacca was left several million dollars in debt, a burden that required negotiated restructuring and years of repayment.

The lasting effect was calibration. Loss at that scale teaches you how fragile everything is. How thin the line is between ownership and obligation, and how quickly paper wealth dissolves.

By the time he moved into early-stage investing, he had already internalized how leverage distorts perception and how downside materializes without warning. That early confrontation with reversal was the base for his approach to risk from day 0.

2. Google Was The Training Ground

In 2003, Google was still evolving from search engine into the invisible backbone of the internet. While most people viewed a job there as a highlight for their CV, Sacca saw it as a chance to learn how giant systems actually run.

He did not just sit in meetings about software. He worked on the hard stuff such as initiatives that intersected with telecom policy, building data centers, and dealing with international laws.

It was evident that for Sacca, growth was not just a number on a screen. It was a physical reality that required moving massive amounts of money and equipment.

Learning the True Weight of Risk

When you work for a company that’s spending billions on hardware, you learn to see danger differently. You stop focusing only on how many users sign up. Instead, you look for the bottlenecks that can break a business. You start to see the difference between a product that gets attention and a system that becomes a permanent part of life.

This experience taught him to look for “indispensable” systems. In a world of apps that come and go, he learned to spot the ones that could turn into a utility.

How This Turned Into Better Investing

This background gave Sacca a massive advantage when he started investing in startups. When he saw early versions of Twitter or Uber, he did not just wonder if they were popular. He looked at the underlying structure. He asked if these companies could build a strong foundation through network effects and government rules.

Most investors wait for a company to become a success before they believe in it. Because Sacca had seen how global platforms are built from the inside, he could spot that potential early.

He was not guessing about the future. He was looking for the same patterns he had already seen at Google.

3. Why a Small Fund Could Do What Large Funds Could Not

In the world of investing, people usually think that having more money is always better. If a big bank has a billion dollars and you only have eight million, you might assume they will win every time.

But the math of venture capital often works the other way around. Because Sacca’s fund was small, it could do things that the giants were physically unable to do.

The Simple Math of Winning Big

Think about it like this: if you have a tiny fund and you put $100,000 into a brand-new company, you might own 2% of it. If that company eventually becomes worth $10 billion, your small slice is now worth $200 million.

For an $8 million fund, that is a legendary result. But for a billion-dollar fund, $200 million is just okay. To a giant firm, that small check feels like a waste of time.

Big funds are trapped by their own size. They have so much cash that they are forced to write huge checks into companies that are already well-known. By the time they arrive, the price is high and the potential for a massive payout has shrunk.

Sacca’s advantage was that he could play in the “early days” where the big players wouldn’t or couldn’t go.

Ownership Over Everything

Many investors just want to say they were involved with a cool startup. They want the logo on their website for the fame.

But Sacca did not care about fame at all. He cared about how much of the company he actually owned.

When you run a huge firm, you have to answer to committees and partners. If you want to put 10% of your money into one tiny, risky idea, your boss will probably say no.

But because Sacca was working alone with a small amount of cash, he could be as bold as he wanted. He could focus his money exactly where he had the most confidence and conviction.

Speed and Freedom

Big firms move slowly. They have meetings, votes, and long lists of rules to manage risk. This protects them from losing money, but it also stops them from moving fast. Sacca had no one to argue with. If he liked a founder, he could write a check and get back to work immediately.

The success of Lowercase Capital Fund was not just about picking the right companies, but about having a setup that allowed him to own enough of those companies to move the needle.

Most big funds are built to be safe and steady. Sacca’s fund was built to capture the full power of a win.

4. Keeping Your Slice of the Pie

Getting into a great company early is only half the battle. As a startup grows, it needs more and more money to keep going. Usually, every time a new investor puts money in, the original investors see their percentage of the company get smaller through what is known as “dilution”.

This is a common trap. Many people own a piece of a “unicorn” company but end up with very little cash because their share was watered down over time.

Sacca understood that he didn’t just need to be “in” companies like Twitter and Uber. He needed to make sure his slice of the pie stayed large enough to be life-changing.

Buying When Others Wanted Out

When Twitter started to show it could be a massive success, it wasn’t yet a sure thing to the rest of the world. Some early employees and investors wanted to sell their shares to get cash right away.

At the time, most big investment firms weren’t set up to handle these small, private sales. They preferred to wait for the official, multi-million dollar funding rounds.

And that’s when sacca saw an opening. He created special accounts specifically to buy these shares from people who wanted out.

It was a brilliant move. He was buying more of the company at a price that looks like a bargain today. And he did the same with Uber.

He didn’t just wait for the company to ask for more money. He went out and found shares to buy whenever he could.

Using Money as a Simple Tool

In the later stages of a startup’s journey, writing a huge check is often about status or “signaling” to the market that a company is a winner.

For Sacca, capital was just a tool to solve problems and lock in his position. He used his funds to help founders bridge a short-term need for cash or to clean up a messy list of early investors.

By helping the company solve these small issues, he was able to buy more ownership when there was very little competition.

He was also disciplined about where he put his extra cash. He didn’t spread it thin across every company he owned. Instead, he doubled down on the ones where he had the most confidence.

In the end, his success came from treating ownership as something to be defended and grown, not just something you get lucky enough to start with.

5. Three Companies, One Fund, and the Reality of Concentration

People often look at a famous investor’s website and see a long list of logos. They assume the secret to success is being right fifty times. But for Lowercase Capital Fund, the story is all about concentration.

Twitter, Uber, and Instagram generated an overwhelming share of value. Yes, other investments did produce returns in absolute terms, but they did not define the fund. The real magic happened because Sacca went very deep on just a few massive winners.

To put this in perspective, the fund turned its initial $8.4 million into a payout of several billion dollars for its investors. At its peak, the Twitter position alone was worth approximately $1 billion, while the Uber stake grew to be worth billions more.

Betting Everything on Conviction

If you have eight million dollars and spread it thin across fifty different startups, you are actually making it harder to win big.

Even if one of those companies becomes a huge success, you won’t own enough of it to change your life. To reach a return like 200x, you have to do the opposite of “playing it safe.”

You have to find the companies that can become essential parts of daily life and own as much of them as you can.

The Math of the Home Run

In the startup world, there is a common rule: one big winner usually pays for all the companies that fail. Others might refer to it as the “Power Law.”

In this case, that rule was taken to the extreme. Twitter alone would have made Sacca one of the best investors ever. Uber was an even bigger win. When you add Instagram to the mix, the rest of the portfolio almost disappears.

Most people think that spreading your money around is the best way to manage risk. But in venture capital, spreading your money too thin can actually be a mistake. It dilutes your upside.

By focusing his cash and his time on just a few “indispensable” systems, Sacca turned a strong performance into a result that the rest of the industry still can’t quite believe.

6. Standing Out By Being Different

In an industry where consensus is starting to become the norm, being different is a huge advantage. Most investors in Silicon Valley wear the same clothes, talk the same way, and live in the same neighborhoods.

But Sacca did the opposite. He became famous for wearing embroidered cowboy shirts and living in the mountains of Truckee instead of staying near the big, traditional firms.

The Power of Being Memorable

When a founder meets 50 investors in a week, those people all start to blend together. But they always remember the guy in the cowboy shirt.

And this was not just a fashion choice for Chris. It was a strategy to be remembered. By standing apart from the crowd, he made it easier for the best founders to find him and remember him when it was time to sign a deal.

Using the Internet as a Bridge

Chris was actually one of the first investors to be very active on Twitter. By sharing his thoughts publicly, he made founders feel like they already knew him before they ever met in person.

This created a level of comfort that most other investors didn’t have. In a business built on relationships, being the person everyone feels they already know is like having a head start.

Access in venture is often attributed to network density and also shaped by signal clarity. By standing outside the prevailing aesthetic and geographic norms, Sacca increased the likelihood that founders would remember, contact, and engage him. In a business where first conversations often precede formal process, memorability has structural consequences.

7. Why This Was a Rare Moment in Time

It is tempting to look at these results and think there is a simple “how-to” guide you can follow to do it again.

But the truth is that Sacca’s success was a mix of great choices and a very specific moment in history. The late 2000s were a unique time when the world was changing in ways we will likely never see again.

A Perfect Storm of Technology

When the first fund started, smartphones were just becoming popular. Social networks were turning from hobbies into necessities, and it was becoming cheaper than ever to start a software company.

Companies like Uber and Twitter were building on top of brand-new technology that had not yet reached its full potential.

Less Competition

Back then, there were far fewer people trying to invest in tiny startups. Big firms were still focused on older, more established companies.

This meant Sacca could get into world-changing businesses at very low prices. Today, the market is crowded, valuations are inflated, and every big VC fund or investment bank has a team looking for the “next big thing.”

The Final Word

Lowercase Capital Fund I was a “lightning in a bottle” event. It was the result of a small fund having the freedom to move fast, a single person with a clear vision, and a world that was ready for a digital revolution.

While you might not be able to copy the exact results today, the lesson remains the same: focus, ownership, and the courage to be different are still the best tools an investor can have.

If you want to learn more about Chris Sacca, his podcast episode with Tim Ferriss from 10 years ago is definitely worth listening to:

Over 90% of U.S. firms that adopted AI in the past three years reported zero change in headcount. That number comes from the Atlanta Fed. It has been sitting in the data while the discourse ran in a completely different direction.

David George at a16z published a piece making this case directly. The evidence he draws on has been accumulating for years, and the findings are worth going through carefully. The story being told about AI and jobs is pointing companies toward decisions that make them worse at using the technology they are already paying for.

The data does not support the apocalypse. It points to something most companies haven’t dealt with yet.

Hiring is the hardest part of the redesign this article is about.

Every point below comes back to one thing:

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Table of Contents

1. The Assumption Underneath All of It

2. What the Surveys Actually Found

3. The Pattern That Has Repeated Every Time

4. What Is Changing Inside Companies Right Now

5. The Decisions That Follow From This

1. The Assumption Underneath All of It

The entire AI job apocalypse argument runs on a single premise: there is a fixed amount of work in the economy. Once AI takes a portion of it, that portion is no longer available for people.

Economists call this the lump-of-labor fallacy. It has been tested against every major wave of labor-displacing technology for two hundred years. It fails every time, not occasionally, not in most cases, but consistently, across different countries, different industries, and different time periods.

The intuition feels airtight. A machine performs a task. The person who performed it is no longer needed for it. Multiply that across enough tasks and enough machines, and the arithmetic appears to hold. What it omits is how economies actually respond to efficiency gains. When technology makes labor more productive, costs fall. Output rises. Demand expands into things that didn’t previously exist. That demand generates work that also didn’t previously exist.

The total pool of work does not shrink. What changes is where the work is, and who is doing what inside it.

2. What the Surveys Actually Found

The predictions have been running for three years. There is now enough data to evaluate them directly.

The Atlanta Fed surveyed U.S. firms on AI’s employment impact. Over 90% reported no change in headcount after three years of AI adoption. A Census Bureau survey found that only 5% of AI-using companies reported any headcount change at all, and that 5% split almost evenly between firms that grew headcount and firms that reduced it. The Yale Budget Lab published consistent findings in April 2026. NBER working paper 34984 reached similar conclusions from a different methodology.

What the data shows is not mass displacement. It is task reallocation happening gradually inside companies, with most of the visible change occurring at the role level rather than the headcount level. The number of people is not changing. What those people are being asked to do is.

Founders, this is your hiring signal. The work is shifting toward roles that own the judgment AI cannot make yet. Filling those seats is where Jill helps.

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Stable headcount is not the same as no change. The surveys capture the number of employees. They do not capture what is happening to the nature of the work those employees are doing. A stable employment number with rapidly shifting role content is evidence that the change is operating at a different layer than the headlines describe, and that most companies haven’t updated how they think about the jobs they already have.

3. The Pattern That Has Repeated Every Time

The mechanism George points to at a16z is not unique to AI. It has shown up in every major shift in how productive work gets done, and the structure of each episode is similar enough to examine directly.

Agricultural employment in the United States fell from roughly a third of the workforce to under 2% over the twentieth century. The people who left farming did not become permanently unemployed. They moved into manufacturing, service industries, healthcare, and eventually technology. Each wave of agricultural efficiency created the economic conditions for the next wave of demand elsewhere. The work did not disappear. The location of the work changed.

Electrification reshaped manufacturing in ways that destroyed certain trades while creating new ones that had no prior name. Spreadsheets automated repetitive accounting tasks and produced, within a generation, an entirely new class of financial analysis work with no prior equivalent. The tools that reduced the need for manual data entry expanded the demand for people who could interpret and act on the data those tools now made available. The pattern in each case is the same: efficiency at the task level, expansion at the economy level.

The argument that AI automates cognitive work and is therefore a different kind of disruption is also the same argument that was made about each prior wave from inside that wave. The people who made it were not stupid. They were correctly observing what was happening at the task layer and incorrectly assuming it would stay there.

4. What Is Changing Inside Companies Right Now

The surveys show stable headcount. They also show that the nature of work is changing faster than job titles reflect. That gap between what the title says and what the job actually requires is where most of the real management problem lives right now.

A recruiter at a company using AI tools today is not doing the same job as a recruiter two years ago. Repetitive screening work has compressed. Strategic and relational work has expanded. The title is the same. The actual scope is not. The same pattern runs through legal departments, finance teams, and operations functions. Lawyers are being asked to review and validate AI output rather than produce first drafts. Finance teams are moving from assembling data toward interpreting it and supporting decisions that require judgment.

The task content of most roles has changed in the past two years. The official description of those roles, in most cases, has not.

Companies treating AI adoption as primarily a headcount reduction exercise are solving the wrong problem. The output available per person has expanded. The question of who owns the judgment calls the tools cannot yet make, and how to build a workflow around that, is the one most firms haven’t answered. Most firms have updated their tech stack. Almost none have updated the job description that goes with it.

If you are early in your career and feeling this shift, this is where an agent like Jack earns its place.

He scans every job daily and introduces you directly to hiring managers, so you land in front of the person deciding instead of the bottom of an application pile.

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5. The Decisions That Follow From This

Founders are asking how many people to hire. That is the wrong question. A team that has thought carefully about what AI handles and what people own will outperform a larger team that hasn’t. Roles need to be designed clearly enough that responsibility doesn’t fall through the gaps between what the tool does and what the human is supposed to catch. Get that architecture right. The headcount number follows from it.

Operators are sitting on a role definition problem most haven’t named yet. People are carrying larger scope than their current description captures, and that misalignment shows up in compensation, in hiring, in how performance gets evaluated, and in how the team understands its own work. Fixing it does not require eliminating positions. It requires rewriting them to reflect what AI adoption has actually changed about the day-to-day.

Investors watching headcount cuts as the primary signal are watching the wrong number. Companies that treat efficiency as a strategy tend to reduce the capabilities that are hardest to rebuild later. The more useful signal is which companies are generating more output per person while keeping the human judgment the tools cannot yet replicate. That gap is already visible in the teams that have treated role redesign as a real operational problem.

The companies that took the role redesign problem seriously will look, five years out, like they saw something that was always obvious in the data. The ones that didn’t will have spent those years running the same org chart through better software and wondering why the gap kept widening.

This one is US-focused and laser-targeted. No mixed sectors. No noise. Just 200+ people with a track record of writing checks into AI companies at pre-seed and seed.

The VC Corner

The VC Corner is where founders stay ahead: sharp news, fundraising strategies, battle-tested playbooks, and the tools to grow.

By Ruben Dominguez

Every entry gives you the name, job title, primary company, a short description, and a direct LinkedIn profile. That means you skip the research phase and go straight to outreach.

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You built the cash flow model in month 1. You have not opened it since. The numbers feel approximately right, until they do not, and by the time you realize the model drifted, you are 6 weeks from a problem you could have seen coming in January.

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From top insights a

nd reports to new funds, VC jobs, resources, and the hottest deals, here’s everything you need to stay ahead 👇

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In-Depth Insights 🔍

• The Great Descent 📉
  AI is driving down the cost of expert judgment much like the internet drove down the cost of accessing information. Fields built on scarce knowledge now face the same shift software brought to publishing and distribution. [Chamath Palihapitiya]

  

• Iran Charges Hormuz Fees By...? 💰
  Prediction markets currently assign a modest chance that Iran introduces transit fees before the end of August. Longer dated contracts price higher odds, signaling traders expect the risk to increase over time. []

  

• Cerebras Bet Against the GPU. It Just IPO’d at $56B.🎯
  The company built its architecture around moving data efficiently instead of maximizing raw compute throughput. That early design choice carried it from an unconventional startup to one of the largest US tech IPOs in recent years.

  

• The Free AI Content Engine That Beats a Content Team 📋
  A Notion based workspace combines persistent memory, creator workflows, and automatic knowledge capture into one system. The thesis is simple: consistent publishing builds familiarity long before buyers enter an active purchasing process.

  

• Why Most VCs Misunderstand Their Own Fund Economics 📊
  A quarterly waterfall model shows how cash timing shapes returns beyond headline fund performance. Management fees, distributions, and carry can produce very different outcomes despite identical investment results.

  

• Are AI Employees More Expensive Than Humans? 🤖
  Lower model pricing has not reduced spending because usage continues to grow much faster than costs decline. Teams are increasingly measuring cost per completed task instead of comparing software licenses with employee salaries. []

  

• ElevenLabs: $2M Pre-Seed to $11B in Three Years 🎙️
  The voice startup scaled from an early seed round to an $11 billion valuation in just over three years. Strong fundraising and rapid adoption positioned it among the fastest growing infrastructure companies in AI. [Niko Ludwig]

  

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Interesting Reports 📊

• Global Startup Funding Breaks $510B In H1 2026 📈
  Startup investment reached $510 billion in the first half of 2026, already exceeding the full year total from 2025. OpenAI and Anthropic accounted for $217 billion, representing 43% of all capital deployed. [Crunchbase News]

  

• Europe’s Repeat Founders Keep Building Bigger Companies 🏭
  Founders with prior exits continue to raise larger rounds and create higher valued startups across Europe and Israel. Networks from companies like Wise, Skype, and Klarna are producing a growing pipeline of new founders. [Dealroom.co]

  

• Global Markets Close June With Strong Equity Gains 📊
  Major stock indexes advanced through June, with the Nikkei 225 leading yearly performance and the S&P 500 posting double digit gains. Gold remained well above last year’s levels, while SpaceX’s $1.78 trillion listing became one of the month’s biggest VC backed milestones. [PitchBook]

  

Recently Launched Funds 💸

• PranaVentures launched a €100M seed fund to invest in early-stage startups across Europe.

• Stratos Ventures raised over $50M for its debut fund focused on backing early-stage technology startups.

• InvestEco closed Sustainable Food Fund IV at C$106M to invest in sustainable food and agri-tech companies.

• Tapestry VC launched $80M Fund III to continue investing in enterprise infrastructure startups.

• Harpoon Ventures closed Fund IV at $155M to back frontier and defense technology startups.

• Ruya Ventures closed its inaugural $50M pre-seed fund to support founders across the MENA region.

• Magnify Ventures closed Fund II at $46.6M to invest in deep tech and life sciences startups.

The AI Corner

The Claude Code system that replaces a 5-person team

Most Claude Code articles list a few hacks and stop there…

Read more

3 months ago · 45 likes · Ruben Dominguez

VC Jobs 💼

• Upfront Ventures (Los Angeles, CA): VC Associate (apply here)

• Primary Venture Partners (New York City, NY): VC Associate (apply here)

• Pear Ventures (San Francisco, CA): VC Investor (apply here)

• Social Capital (New York City, NY): VC Associate (apply here)

• Capital Factory (Austin, TX): VC Associate (apply here)

• Alumni Ventures (London, England): VC Principal (apply here)

• SuperSeed (London, England): VC Manager (apply here)

• Samaipata (Madrid, Spain): VC Manager (apply here)

• Healthworx (Washington, DC): Senior VC Associate (apply here)

• 1752VC (Los Angeles, CA): VC Fellowship (apply here)

Hottest Deals 💥

• Quantum Systems raised $1.2B in Series D at an ~$8B post-money valuation to scale its AI-powered autonomous aerial intelligence platform for defense and commercial applications. (read more)

• Together AI raised $800M in Series C at an $8.3B valuation to expand its AI cloud infrastructure and open-source foundation model ecosystem. (read more)

• Etched emerged from stealth with $800M in funding to commercialize transformer-specific AI chips built for large-scale inference. (read more)

• Addi raised $85M in Series D to expand its embedded finance and consumer lending platform across Latin America. (read more)

• Venice AI raised $65M in Series A to accelerate development of its privacy-first generative AI platform. (read more)

• Aligned raised $60M in Series B to expand its AI-powered enterprise workflow automation platform. (read more)

• Qolab raised $54.2M in Series B to advance superconducting quantum computing technology. (read more)

• Xellar Biosystems raised $50M in Series A & A+ to scale its next-generation cell engineering platform for biotech applications. (read more)

• CarbonSix raised $40M in Series A to expand its carbon removal technology and climate infrastructure platform. (read more)

• 10Beauty raised $23.5M to accelerate commercialization of its AI-powered robotic manicure system. (read more)

• LinqAlpha raised $22M in Series A to build AI-powered investment research and financial intelligence tools. (read more)

• StirlingX raised $20M in Series A to develop advanced electric propulsion systems for the space industry. (read more)

• Telum Therapeutics closed an €18M Series A to advance precision oncology therapies. (read more)

• Alva Industries raised €16M in equity funding to scale production of its ultra-light electric motor technology. (read more)

• Luxonis raised $14M in Series A to expand AI vision hardware and robotics solutions. (read more)

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