Last months have again been really fast, and it doesn’t look like AI is losing speed.

I’m now dedicating 80% of my time to building AI Agents. And I have a 100% conviction they are going to be the future of work.

Now, my thesis is the following: every company will hire agents to work for them, but at some points agents will be like humans, there’ll be good agents at some tasks and not so good ones.

The unlock for the following years is building the good ones. And those are going to come from forward deployed ai consultant/engineers developing agents that behave exactly how comopanies need them to.

The value: the proprietary know how and strategy companies have.

Inside this piece:

• Why the biggest AI opportunity is now an execution gap, not a model gap

• Why “forward-deployed” may become one of the defining delivery models of the agent era

• Why OpenAI and Anthropic are both pulling consulting and implementation into the center of the market

• What a real agent deployment playbook looks like in practice

• Why the biggest AI opportunity may sit inside the “99%” of companies outside Silicon Valley

💭 Remember!

As a AI Opportunity + PMF subscriber you’ll also get access to all the other Claude resources we put together, such as:

• 1️⃣ The Claude Code Founder Guide

• 2️⃣ want to keep your job? read this.

• 3️⃣ claude just made outbound fully automatable.

• 4️⃣ 🚨 The Ultimate AI Fundraising Copilot (Built With Claude)

• 5️⃣ look what anthropic just released

• 6️⃣ The Ultimate Guide to Building Skills for Claude 🔥

• 7️⃣ The Prompting Skills Guide

• 8️⃣ The Garry Tan (YC) Playbook for Building with Claude

• 9️⃣ Claude. md Full guide

• 🔟 Understanding Claude Dispatch (Complete Guide)

• 1️⃣1️⃣ Claude For Finance: The CompIete Guide

• 1️⃣2️⃣ How I Rebuilt My SEO Strategy Inside Claude (The Playbook)

• 1️⃣3️⃣ The McKinsey Slide Playbook for Claude

We’re already 70,000+ founders and operators inside!

and just for this week you can get AI + PMF at 30% Discount!

Upgrade now to AI + PMF with 30% OFF

1. The biggest AI bottleneck is no longer intelligence. It is implementation

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Andrej Karpathy says he barely writes code anymore.

Since December, he says his workflow flipped from mostly coding himself to mostly delegating to agents.

That is a much bigger shift than most people realize.

The important part is not that AI helps engineers write code faster.
It is that the interface to building software is changing.

The job is moving from:

• writing functions

• debugging line by line

• manually pushing work forward

to something much stranger:

• assigning work to multiple agents

• managing parallel execution

• designing better instructions

• building systems that keep going without you

Karpathy describes it almost like a new kind of operating mode. Not coding in the old sense, but “expressing your will” to a set of agents for 16 hours a day.

That sounds extreme.

But he may be early, not wrong.

Because underneath all the jokes about “AI psychosis” is a serious idea:

The next generation of software work will not be about doing the work yourself. It will be about setting up loops of intelligence that can work on your behalf.

And that has implications far beyond software.

I broke down his latest interview for you, inside you will find:

1. What changed

2. Why the real skill is leverage

3. From copilots to autonomous loops

4. The first big opportunity: rebuilding software for agents

5. The second big opportunity: auto-research

6. Why this still does not fully work

7. The third big opportunity: the collapse of traditional UX

8. Digital will move first. Physical comes later.

9. Education is about to be rebuilt too

10. What this means for founders

11. My view

Deel just launched The Pitch, a global startup tournament, for the best founder in the world. The prices:

• 10 startups will each receive a $1M SAFE
• 100 regional winners will each receive $50K SAFEs
• $15M+ total capital deployed

And we’re officially partnering with them to push this to the Market Fit audience 🔥

This isn’t a gimmick. It’s a real funding vehicle.

Here’s how it works:
1️⃣ Submit a 5-minute application
2️⃣ Top startups get invited to regional in-person finals
3️⃣ Regional winners get funded
4️⃣ Global finalists compete for $1M

Apply Here

What changed

According to Karpathy, something broke open around December…

From here on, this is reserved for premium subscribers.

If you want to really understand where AI is going and how to stay ahead of it, you can subscribe here:

Subscribe now

Read more

Y Combinator just published its requests for Spring 2026 and, among all the things you can find in there, there’s one idea that’s very easy to skim past if you’re not paying attention: AI-native agencies.

At first glance it sounds familiar: every agency already claims to use AI, everything is “AI-powered,” so it’s tempting to dismiss this as just another variation of the same trend.

But that’s not really what YC is pointing at.

If you read it more carefully, what’s emerging is a deeper shift:

the next wave of meaningful AI companies might not start by selling software, but by actually doing the work.

That connects directly to what we saw in the Sequoia piece I wrote (read it here) and that YC is confirming: the next trillion company might be an services company, not software.

So I decided to follow my research into this and see why:

1. YC’s AI Native Agencies Thesis

2. Why to start an AI Native Agency

3. The Difference Between AI in Agencies vs AI-Native Agencies

4. Which Agency Categories Will Be Disrupted First by AI (And Why These Ones Win)

5. Top 15 AI Startups Moving Toward the Agency Model

Apply to The Pitch by Deel now

1. YC’s AI Native Agencies Thesis

For the past decade, the dominant playbook was clear and almost unquestioned: build software and sell it to agencies or service providers.

Agencies were seen as:

• inefficient

• messy

• hard to scale

YC is now suggesting something that moves in the opposite direction.

What if, instead of building tools for agencies, you build the agency itself but redesigned from the ground up?

At the same time, the real shift is in the economics.

Historically, agencies scaled linearly:

more clients → more people → thinner margins → more complexity → inconsistent quality.

With AI, that relationship breaks. You can:

• increase output per person

• reduce the marginal cost of delivery

• standardize parts of quality that used to be unpredictable

But the most important part is this: you can still sell the same outcomes at high prices while your internal cost structure changes dramatically.

That’s where the real advantage comes from:

not doing things better, but doing them on a completely different economic base.

From here on, this is for paid subscribers only.

If you want to keep reading, you can subscribe here:

Subscribe now

2. Why to start an AI Native Agency

Read more

Most people glanced at it, recognized a few names, maybe saved a couple to check later and moved on.

That’s a mistake.

Because that slide is not just a list. It’s a blueprint of where value in AI is being built today and where it will be created next.

And if you don’t know how to read it, you’re just looking at logos.

If you do know how to read it, you start seeing:

• where capital is concentrating

• where new players can still emerge

• and where the next category-defining companies will come from

What you’re about to get (and why it matters)

We didn’t just go through the list.

We rebuilt it from scratch.

We ignored Nvidia’s categories. And we asked a more useful question:

What role does each of these companies play in the AI economy?

That one shift changes everything. Because instead of a flat list, you get a map of the system. And once you see the system, you start seeing:

• where value accumulates

• where bottlenecks form

• and where opportunities actually are

What you will get:

1. What AI-Native Companies Really Are and How to Spot the Real Ones

2. Why Nvidia’s map is misleading (if you read it wrong)

3. How to Reclassify the 103 AI Startups to See Where Value Actually Sits

4. The Definitive Reclassification of All 103 AI-Native Startups

5. The insight Nvidia didn’t spell out

6. The two areas you cannot afford to ignore

Most people stop at the list.
The real insight starts here.

From this point on, this is for subscribers only.

If you want to understand where value is actually being created and how to read signals like this properly you can subscribe here:

Subscribe now

Read more

A few days ago, Sequoia partner Julien Bek made a very big claim:

The next $1 trillion company will be a software company masquerading as a services firm.

At first glance, that sounds like one of those bold VC one-liners people repost because it sounds smart.

But the more I looked into it, the more I thought:

wait, this might actually be one of the most important ideas in AI right now.

Because if this is true, then a huge number of founders are building the wrong thing.

They are building copilots.
They are building tools.
They are building software that helps someone do the job better.

But the real opportunity may be much bigger:

build the company that does the job itself.

Not software for accountants.
The company that closes the books.

Not software for lawyers.
The company that reviews the contracts.

Not software for insurance teams.
The company that handles the claims.

That is the shift Julien is pointing to.

In his piece, he argues that if you sell the tool, you are stuck competing with better models every few months. But if you sell the work, every improvement in AI makes your product faster, cheaper, and stronger.

And there is another reason this idea matters so much:

for every $1 spent on software, around $6 is spent on services.

So the real prize may not be the software budget at all.

It may be the labor budget.

What makes this even more interesting is that this is not a totally new idea.

For years, investors have tried to find ways to turn service businesses into something more scalable and more software-like.

That is part of why strategies like rollups have become so attractive: the belief that fragmented service industries can be consolidated, improved, and made far more efficient.

What AI changes is the magnitude of that opportunity.

Before, software could help professionals do the job better.

Now, it is getting close to doing meaningful parts of the job itself.

That is the real reason Julien’s thesis feels so important right now.

Not because nobody has seen the opportunity before.

But because this may be the first moment when the technology is finally good enough to make it work at scale.

So I decided to go deeper.

I looked into the companies Julien and Sequoia point to as early examples of this shift, what they are actually doing, how they are doing it, and why some of these categories may produce massive companies over the next few years.

In this deep dive, you’ll learn:

1. Who is already doing this: 5 founders building right now (mentioned by Sequoia)

2. How these companies are structuring the model so customers trust them

3. Where the biggest opportunities still are right now

This is the newsletter that surfaces what’s really happening in the AI market before it hits the front page. Every week you’ll get two things, fast:

1. What’s happening now (and where the money’s headed).

2. Who’s already building it (so you can invest, partner, or steal their playbook).

Each issue is short enough to read with your morning coffee but dense enough to move your roadmap.

However, most of the content from this newsletter will be only accessible to the paid subscribers.

If you are serious about building or investing in AI, you can subscribe here:

Subscribe now

Read more

Some people get lucky once.

Leopold Aschenbrenner looks like someone who engineered luck.

In a very short window, he went from being a young OpenAI insider with a reputation for sharp thinking to publishing a 165-page essay laying out a decade-long AI thesis (“Situational Awareness: The Decade Ahead”), and then building an investment vehicle that expressed that thesis in markets.

The numbers are hard to ignore: by the end of 2025, his fund’s public 13F long U.S. equity book alone was in the multi-billion range (this is not total AUM, but the SEC-reported public equities snapshot).

So I did what I always do when someone prints that kind of outcome:

I reverse-engineered the playbook.

In this analysis you’ll learn:

1. The one simple idea that explains his whole portfolio (so it stops feeling random and starts feeling obvious).

2. How the bets evolved over time as AI went from “hype” to “massive capex” to “real constraints” (power, data centers, networking).

3. A clean breakdown of the portfolio into buckets, so you understand what each part is doing

4. A framework you can reuse, so you can look at any “AI stock” and quickly tell:

The goal is not to idolize a manager.

It’s to extract a framework: how to turn a macro AI thesis into a coherent, compounding portfolio of bottlenecks and why that approach can outperform when everyone else is chasing shiny apps.

Let’s get to work

Apply to The Pitch by Deel now

This is the newsletter that surfaces what’s really happening in the AI market before it hits the front page.

Read more

Hey everybody, Guillermo here.

Mistral just hit an inflection point that most people in AI still don’t understand.

According to the Financial Times, Mistral’s revenue run-rate is now “north of $400M”, up from ~$20M a year ago (a 20x jump).

And Arthur Mensch says they’re aiming to surpass $1B ARR by the end of this year, while pushing to 100+ large enterprise customers.

This is not “Europe’s OpenAI” hype.

This is a very specific GTM machine working because they picked a less crowded market and built the product, pricing, and distribution to own it.

Also, we’re sharing Mistral’s €105M Seed Memo in this issue.

📢 CALLING ALL FOUNDERS 📢

I’ve partnered with Deel to find and fund the best founders in the world. Deel is launching The Pitch: a global startup pitch competition, presented by J.P. Morgan.

Up for grabs…

• 💰 Up to 100 regional winners receive $50,000 in investment

• 🌍 Up to 10 global champions win $1M in investment

• 🤝 Plus access to exclusive startup perks and a global founder community

If you’re building something bold, this is your moment.

Apply Now 👇

What we’re sharing in this article

1. The less crowded market Mistral is winning

2. Positioning: “Control” as the product

3. Product capabilities: what Mistral made easy (vs OpenAI and Anthropic)

4. Target customers: the real ICP and the buying committee

5. Pricing: the unit-economics wedge

6. Distribution: multi-cloud by design, plus a sovereign compute push

7. The copy playbook: 10 tactics you can steal

8. Bonus: the €105M Seed Memo

This is the newsletter that surfaces what’s really happening in the AI market before it hits the front page. Every week you’ll get two things—fast:

1. What’s happening now (and where the money’s headed).

2. Who’s already building it (so you can invest, partner, or steal their playbook).

Each issue is short enough to read with your morning coffee but dense enough to move your roadmap.

However, most of the content from this newsletter will be only accessible to the paid subscribers.

If you are serious about building or investing in AI, you can subscribe here:

Subscribe now

Read more

Last weeks have been super hectic and I’ve been working on many different mostly all AI related.

One of my angel investments Quiver AI just went out of stealth raising a $8M Seed round from A16Z and I’ve dedicated so much time to working on Claude, OpenClaw and many other AI tools.

All my study is built to answer the following question: “where is the biggest opportunity in AI right now?”

Well, Michael Truell (Cursor CEO) just published a simple thesis that gives a glimps into the future of AI Coding:

Subscribe now

And it’s the kind of shift that creates new winners fast.

What we’re going to cover

1. The 3 eras of AI dev (and why the first two don’t matter anymore)

2. Why cloud agents are the real inflection point

3. Why artifacts become the new UI (and the new moat)

4. The 5 wedges closest to revenue

5. A brutally simple investing checklist

The rest of this issue is for paid subscribers of AI Market Fit.

This is where I get specific: concrete product ideas, monetization angles, and the exact diligence checklist I’d use to evaluate “agent-first” startups in 2026.

Join OpenAI for Startups

Sponsor AI MARKET FIT & get your product in front of thousands of founders worldwide? Reach out to g@guillermoflor.com

Trending right now: How Base44 was bootstrapped and sold for $80M in 9 months

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GIVE SOME LOVE TO OUR LINKEDIN LAUNCH!

For years, the real craft of VC lived in private dinners, internal memos, partner meetings and backchannel WhatsApps.

Not because it was secret. Because access was limited.

The people who actually know how to build $1B+ companies don’t usually publish how they think.

Until now.

Today, we’re launching VC Market Fit 🚀

A daily platform where top investors behind Spotify, Klarna, Trade Republic, Mistral, Lovable, Mercor and more will breakdown:

1. The stories behind their craziest deals (insane) 🔥

2. How they actually spot winners

3. Built billion-dollar companies (this batch is cracked) 🔥

4. How they think about AI, distribution and moats

The kind of insight that used to require a warm intro, a $50M fund, or a seat at the table.

This is venture from the source. Straight from the partners.

Now it’s in your inbox. Free.

Subscribe now here to get them in your inbox every day

VC MARKET FIT

VC Market Fit is a 90-day investor writing residency that ships founder-grade insights, not fluff. Each week, we publish sharp, actionable essays that help founders raise smarter, hire better, and avoid expensive mistakes, while giving top investors a pl

By Guillermo Flor

Not only this: We are launching with an ABSURD first batch 🔥

These are investors who have helped build and invest in companies like Spotify, Trade Republic, Mistral AI, Lovable, Mercor, Klarna and many more billion $ companies.

This has always been inaccessible.
Now we’re publishing their stories.

Batch 01: The first writers

Creandum (team)

One of Europe’s most iconic venture firms. A full team coming in strong.

Staffan Helgesson (Creandum)

General Partner & Founder at Creandum

If you want a snapshot of Staffan’s taste, look at the companies he led investments in:

Spotify (2008 – first venture round), Klarna, iZettle, Bolt, Trade Republic, Epidemic Sound, Lovable…

From early Spotify to multiple European unicorns, his track record spans two decades of category-defining companies.

He’s a generational European investor, and we’re incredibly excited to have him onboard.

Peter Specht (Creandum)

General Partner at Creandum (London)

Peter spent several years at Bain before moving into venture, sharpening the strategic lens he now applies to early-stage investing. At Creandum, he has become one of the firm’s driving forces across AI and B2B software.

His track record reflects that focus:

Factorial (Spain’s fastest growing unicorn), cargo.one, Jack&Jill, Leapsome, Black Forest Labs & more

Peter understands where AI infrastructure meets real enterprise distribution.
From vertical SaaS to frontier AI labs, he consistently backs companies building the backbone of the next software era.

We’re very excited to have him onboard.

Carl Fritjofsson (Creandum)

General Partner at Creandum. Founder turned VC.

Before venture, Carl co-founded Wrapp, a mobile social gifting startup backed by top-tier investors, later acquired by Meniga. He’s built, raised, scaled and exited.

Now he invests. And he invests big.

Carl helped build Creandum’s U.S. presence and operates globally across AI, infra, consumer and fintech. He’s proudly supported companies like:

Spotify, Klarna, Trade Republic, Depop ($1.6B exit), Cornershop ($3.5B exit to Uber), YuLife, H Company & more

Founder energy and global ambition.

Carl backs companies that bend reality, from fintech giants to frontier AI labs.

Can’t wait to hear his story.

Simon Schmincke (Creandum)

General Partner at Creandum. Founder turned operator turned VC.

Before venture, Simon co-founded HelloFresh, which went public in 2017 and later reached a €20B+ market cap at its peak.

He was also Global Co-Founder of Foodpanda, one of the earliest global food delivery platforms, later consolidated into Delivery Hero, a company that grew into a multi-billion-euro public group. He has built category leaders from zero to global scale.

At Creandum, Simon has backed some of Europe’s strongest fintech and B2B names, including: Tide, Factorial, cargo.one, Navan, doinstruct & more.

Simon understands hypergrowth from the inside (IPOs, global rollouts, operational chaos at scale) and now applies that founder DNA to backing Europe’s next generation of category leaders.

We’re incredibly excited to have him onboard. 🚀

Fredrik Cassel (Creandum)

General Partner at Creandum since 2003.

One of the longest-standing partners at Creandum and one of the early architects behind its most iconic wins. Fredrik was a Board Member at Spotify (2008–2011) during its early breakout years.

He later served on the boards of: Depop (exited to Etsy for $1.6B), Kahoot!, Xeneta, Lindus Health, Lovable & more

Two decades of boards. Multiple category leaders. IPOs and billion-dollar exits.

Fredrik is pure institutional memory of European tech, and still backing the next wave.

Individual members

Andrew Tan (TLDR & Humans)

Founder of TLDR (1.5M+ readers). Founding Partner at Humans. Exited Founder.

Andrew is a pure AI-native founder turned investor.

Stanford & Berkeley CS + Statistics (Summa Cum Laude), ex-Sequoia Capital and then he built.

He founded Nova, scaled it, and exited to Handshake. At Handshake, he co-founded their GenAI team from 0→1. In parallel, he built TLDR into one of the largest tech media platforms globally.

Companies he’s backed include: Mercor, Charta Health, Cluely, Superpower, TierZero AI, Starcloud & more

Andrew operates at the center of distribution + product + capital in the AI era.

Michael Stothard (firstminute capital)

Investor at firstminute capital. Cambridge. Journalist turned VC.

Before venture, Michael spent almost a decade at the Financial Times as a foreign correspondent. He later became one of the founding editors of Sifted, which today reaches 10M+ readers across Europe’s tech ecosystem.

He understands startups from the inside and from the outside. At firstminute, he’s backed breakout companies including: Mistral AI, Nabla, Granola, Typology, Vocca AI & more

Backer of some of the most exciting AI companies in the region. We’re incredibly excited to have him onboard. 🚀

Gaultier Brun (199 Ventures)

Tech Investor at 199 Ventures.

Operator-minded early-stage VC focused on AI, fintech and vertical software.

At 199 Ventures, Gaultier backs ambitious European founders at the earliest stages, often before institutional capital shows up.

Portfolio highlights include: Wellapp, DermaScan, Lucis, Stairling, Spiko. He’s also an Angel investor in Nothing.

Small checks. High conviction. Backer of AI healthcare, fintech infra and next-gen vertical SaaS.

Jay Patel (Picus Capital)

Investor at Picus Capital (AUM $1B+). Ex-McKinsey.

Jay invests in early-stage companies challenging the status quo across fintech, climate, SaaS and emerging markets.

He’s been involved in investments such as WeRize, Measure, and Invact and as part of Picus, the firm has invested in category leaders including: Personio(~$8.5B), Enpal(~€2.2B), Hawk AI, Taxfix, Forto & more

Global platform. Early conviction. Billion-dollar portfolio.

Saran Davies (Moonfire Ventures)

Associate at Moonfire. Oxford PhD.

Associate at Moonfire Ventures. PhD from University of Oxford, where she researched antibiotic resistance and bacterial survival mechanisms.

She understands the science. She understands the data. She understands how frontier research becomes real companies.

At Moonfire, she backs category-defining startups at pre-seed and seed, including: LiveFlow, Humaans, Lightdash, GOALS & more

Scientific rigor. AI-native investing. We’re very excited to have her onboard. 🚀

Jack Cantillon (Green Room)

Partner & Co-founder at Green Room.

Green Room is built around a simple idea: find exceptional people early and help them become category-defining founders.

Alongside Green Room, he’s an active angel investor backing companies at pre-seed and seed, including: Kular (YC, EF), Pycoworks (Fly, EF), Dubsof (20VC), QStory…

Founder-first. Early conviction.

Subscribe now here to get them in your inbox every day

VC MARKET FIT

VC Market Fit is a 90-day investor writing residency that ships founder-grade insights, not fluff. Each week, we publish sharp, actionable essays that help founders raise smarter, hire better, and avoid expensive mistakes, while giving top investors a pl

By Guillermo Flor

In 2006, Craigslist looked like a badly designed website.

In hindsight, it was a map of future billion-dollar companies 🔥

Every link was a compressed market:

• Housing → Zillow

• Jobs → Indeed

• Services → Upwork

• Travel → Airbnb

• Community → Reddit

And many more…

These weren’t random startups, they were verticalized workflows hiding inside a general marketplace.

Now zoom forward to 2026:

The next Craigslist isn’t a website, it’s the “Capabilities” menu of PwC and almost nobody is looking at it correctly.

PwC does $56.9B in revenue.
364,000 people.
82% of the Fortune Global 500 as clients.

Their services navigation isn’t marketing copy. It’s a catalog of:

• Pre-approved enterprise budgets
• Risk-priced deliverables
• Repeatable, compliance-shaped workflows
• Institutional trust already embedded

In 2006, every Craigslist category became a $1B startup.

In 2026, every PwC capability is a potential $10B AI-native company.

Because AI doesn’t just automate tasks but it compiles institutional labor into software.

Here’s the unlock most founders are missing:

The hard part of building enterprise AI isn’t intelligence.

It’s defining the deliverable, pricing the risk, and getting procurement comfortable.

Services firms already did that.

They spent decades:

• Standardizing workflows
• Packaging outcomes
• Encoding liability
• Creating repeatable, sellable units of work

AI now collapses the cost of producing those deliverables. The result?

Professional services become unbundlable the same way Craigslist was.

Only this time the surface area isn’t classifieds. It’s global enterprise infrastructure.

In this issue, we break down 🔥

1. Why Craigslist was a startup map

2. Why PwC’s menu is the next one

3. A full AI unbundling blueprint across audit, tax, cyber, deals, ops, workforce, ESG, finance, managed services, legal, agencies, BPO, and more

4. Specific company archetypes that can become category leaders

If you’re a founder: This is a company-idea machine.

If you’re a VC: This is a sourcing lens.

If you’re an operator: This is a roadmap to productize your own workflow.

Right now, most AI founders are competing in crowded categories.

Copilots, wrappers, chat tools, incremental automation…

Meanwhile, sitting in plain sight we have:

$50B–$100B services firms
With standardized deliverables
Embedded budgets
Enterprise trust
And workflows that AI can now compile into software.

This is not a feature opportunity. It’s a market replatforming.

If you’re serious about building something generational, this is where you look.

If you’re ready to build at that level, below is the blueprint 🔥

Craigslist → PwC: the next unbundling wave is AI (and it’s bigger)

1) How Craigslist became a startup factory

Read more

I invested in Youshift some months ago after just one meeting with Lucía.

The sheer amount of talent and drive these founders have is amazing, as well as their market knowledge and experience (all three of them are Harvard grads).

PS: If you want to reach out to Lucía you can do so here.

Enjoy!

Subscribe now

I grew up in Spain in a family of doctors. I was fascinated by medicine, but I knew I didn’t want the life that came with it. So I studied Biomedical Science, convinced I could stay close to healthcare while choosing a different path.

In my third year of college, at Harvard, I met my two co-founders: Jota Chamorro and Adolfo Roquero. Both engineers. Both from families of physicians. We didn’t set out to start a company. We set out to understand why one of the most fundamental systems in healthcare, workforce management, is still broken.

If you know this industry, I hope you read what follows with care.

If you don’t, I hope you read it anyway.

What’s happening to the healthcare workforce isn’t isolated to one hospital system or one country. It is structural, and it will affect all of us, globally, if we don’t build better infrastructure.

Where we are heading

Healthcare is facing the worst labor shortage.

By 2033, the US alone is projected to face a shortage of more than 100,000 physicians and 200,000 nurses, nearly 10% of the clinical workforce. At the same time, the population over 65 will grow by more than 40%, inpatient demand is projected to rise by at least 10%, and 49% of physicians report burnout.

Meanwhile, labor expenses increased by $42.5 billion in just two years, and now represent nearly 60% of hospital operating costs.

So we have a system where the main input (clinicians) is shrinking, the main demand driver (aging population) is expanding, and the main cost center (labor) is under unprecedented pressure.

When hospitals cannot staff shifts, they buy relief.

Last year alone, US health systems spent over $10 billion on temporary clinicians, often paying 30–50% more per shift. But the deeper issue is not just cost, it’s incentives.

While hospitals are trying to build stable, long-term clinical teams, temporary staffing markets are structured around filling recurring short-term gaps. Higher short-term pay naturally pulls clinicians toward temporary assignments instead of permanent roles, and contractual restrictions can make it harder for those clinicians to transition into full-time positions at the hospitals where they work. In a labor market that is already supply-constrained, this dynamic fragments the workforce further.

And yet, the system that coordinates all of this — who works, when, at what cost, under which constraints — is still managed through legacy software, spreadsheets, manual workarounds, and large administrative teams. In fact, workforce management has become so complex that many hospitals outsource entire departments to specialized companies.

The impact of solving this problem

Solving the global healthcare labor shortage (at least 10 million workers by 2030) would be one of the highest-leverage “infrastructure” upgrades society can make: McKinsey Health Institute estimates it could eliminate about 7% of the world’s total disease burden by enabling more timely access to essential services (from maternal care to chronic disease management), averting ~189 million years of life lost to early death and disability, and lifting global life expectancy by roughly 1.5 years on average (with much larger gains in the hardest-hit regions, especially Africa, which holds ~52% of the shortage and 70%+ of the opportunity to reduce disease burden, where life expectancy could rise by ~7 years).

Economically, closing the gap could add around $1.1 trillion to global GDP in 2030, partly through the direct creation of healthcare jobs (~$300B) but mostly through second-order effects: a healthier population and workforce that boosts productivity and indirectly creates jobs beyond healthcare, meaning this is not just filling vacancies, but unlocking better health, faster care, and broad-based growth at global scale.

The first step: solving staffing

Today, about 60% of U.S. emergency rooms are staffed by physician groups. Their profitability depends almost entirely on how efficiently they manage labor. And yet, even at this level of scale and specialization, they lack a true operating system built for it.

Either we redesign how healthcare labor is managed, with precision, intelligence, and scale, or the infrastructure behind care will continue to erode under pressure.

When we stepped back and examined the system more closely, it became clear that workforce management in healthcare fails in two fundamental ways.

First, there is no true optimization.

Staffing a hospital is a constrained, multi-objective allocation problem. Every schedule must balance clinical coverage, legal requirements, compensation structures, continuity of care, individual productivity, personal preferences, and burnout risk, all while maximizing patients treated and minimizing unnecessary cost.

In this complexity, the market remains underserved and legacy actors fail to solve the problem. Forecasted patient arrivals are rarely modeled, alongside individual clinician productivity and the complex compensation models that exist.

Second, workforce management is still fundamentally human-coordinated.

Currently, it is managed by full-time schedulers, administrative coordinators, department heads, or medical directors.

As a result, decisions become reactive and local. Trade-offs are resolved manually. Standards vary across sites. What should be a unified, system-level allocation problem is managed through individual judgment and coordination.

The infrastructure to support them does not exist. We believe it must.

Bulding Youshift

We decided to target heads on the healthcare labor shortage starting with solving staffing.

We are building an AI-native operating system to manage hospital workforce management at scale, starting with scheduling.

By optimizing internal labor first, and automating the process end-to-end, we establish the infrastructure layer. From there, payroll, compliance, hiring, and eventually external coordination become natural extensions.

Healthcare is complex. Procurement is slow. Compliance is strict. It would be easy to accept that this is simply the nature of healthcare, but structural pressure forces redesign.

Complexity is not a reason to avoid rebuilding infrastructure. It is the reason it hasn’t been rebuilt, and the reason it must be.

We believe workforce management is becoming the defining infrastructure challenge in healthcare. And we are building the system designed for it.

Hope this was valuable! Let me know if you’re investing in healthtech!

Cheers,

Guillermo

FAQs: The Future of Healthcare Workforce Management (Youshift)

1) What is the healthcare workforce shortage, and how bad is it?

Healthcare systems are running into a structural labor crunch: fewer clinicians available while demand rises. The article cites projections including 100,000+ physician and 200,000+ nurse shortages in the U.S. by 2033, alongside rising burnout and a rapidly aging population.

2) Why is healthcare staffing becoming a global infrastructure problem?

Because staffing isn’t just an HR task—it’s the operating constraint of care delivery. When hospitals can’t staff shifts, patient access, quality, and outcomes suffer, and the whole system becomes more expensive and reactive.

3) What are the biggest drivers of the healthcare labor crisis?

The piece highlights three reinforcing forces: shrinking clinician supply, expanding demand from aging populations, and labor costs becoming the dominant cost center (nearly ~60% of hospital operating costs, as stated in the article).

4) Why are hospitals spending so much on temporary clinicians (locums, travel nurses, etc.)?

When coverage gaps appear, hospitals buy short-term relief. The article notes $10B+ spent in the U.S. on temporary clinicians in one year, often at 30–50% higher per-shift costs.

5) Do temporary staffing markets make the shortage worse?

They can. Higher short-term pay pulls clinicians toward temporary work, while contractual restrictions can make it harder to convert those clinicians into full-time roles—fragmenting an already supply-constrained workforce (as described in the article).

6) What is “workforce management” in healthcare?

It’s the system that determines who works, when, at what cost, under what legal and contractual constraints—including scheduling, credentialing/compliance checks, payroll rules, and coverage requirements.

7) Why is hospital scheduling so hard to fix with standard software?

Because it’s a constrained, multi-objective optimization problem. A workable schedule must balance coverage, legal requirements, compensation models, continuity of care, preferences, productivity, and burnout risk—often across multiple sites and roles.

8) Why do hospitals still use spreadsheets and manual workarounds for scheduling?

The article argues that legacy tools weren’t built to handle today’s complexity—so humans “patch” the system with admin teams, manual coordination, and inconsistent standards that become reactive over time.

9) What does it mean that workforce management is “still human-coordinated”?

Key staffing decisions are often made by schedulers, department heads, and medical directors juggling trade-offs manually. That creates local, reactive decision-making instead of a unified system-level allocation approach.

10) How does poor staffing impact patient care and outcomes?

When staffing breaks, access breaks: delays, reduced throughput, inconsistent coverage, and clinician fatigue. The article also cites broader potential upside: solving the global shortage could reduce disease burden by enabling more timely access to essential services.

11) What is Youshift (sometimes written “Youshif”)?

In this article, Youshift is described as an AI-native operating system for hospital workforce management, starting with scheduling—aiming to optimize internal labor and automate staffing end-to-end.

12) What problem is Youshift solving first?

The first wedge is staffing and scheduling—because it is the daily operational bottleneck that drives labor costs, clinician experience, and patient access.

13) What does “AI-native operating system” mean in this context?

It means the core workflow is designed around automation and optimization from the ground up (not “AI added on” to old tools). In the article’s framing: model demand, account for constraints, and coordinate staffing decisions system-wide.

14) How does AI help reduce hospital labor costs without reducing care quality?

By optimizing internal labor (better coverage with existing teams), reducing overstaffing/understaffing, minimizing expensive last-minute coverage, and decreasing the need for premium-priced temporary shifts—while respecting compliance and continuity constraints.

15) Can better scheduling reduce clinician burnout?

That’s one of the central bets: schedules that respect preferences, fairness, rest rules, and workload distribution can reduce the constant “fire drill” dynamic that contributes to burnout (49% physician burnout is cited in the article).

16) Why start with emergency rooms and physician groups?

The article notes that ~60% of U.S. ERs are staffed by physician groups, and their profitability hinges on labor efficiency—making scheduling and workforce optimization especially high-leverage there.

17) What comes after scheduling in a workforce management platform?

The article frames scheduling as the foundation—then extending naturally into payroll, compliance, hiring, and eventually external coordination as a connected infrastructure layer.

18) What makes healthcare procurement and compliance harder than other industries?

Healthcare has strict compliance requirements, complex credentialing and labor rules, and slow procurement cycles. The article’s view is that these constraints are exactly why infrastructure needs a rebuild—not a reason to avoid it.

19) What ROI should hospitals look for from staffing optimization?

Operators typically look for reduced premium labor spend (e.g., temp clinicians), lower admin overhead, fewer last-minute coverage gaps, better clinician retention, and improved patient throughput. This aligns with the article’s emphasis on labor as the major cost center.

20) Who founded Youshift?

The piece introduces Lucía Vives (founder) and her two co-founders Jota Chamorro and Adolfo Roquero, who met at Harvard and set out to tackle workforce management as infrastructure.

• Anthropic’s founder/CEO on why entry-level jobs in Law, Finance, and Consulting get automated within 5 years

• A Sequoia partner’s take on AI killing SaaS (as we know it)

• Public SaaS stocks sliding and what that says about moats vs AI

• What comes after “AGI-level” capability and how to hedge (a real-world angle)

This is the newsletter that surfaces what’s really happening in the AI market before it hits the front page. Every week you’ll get two things—fast:

1. What’s happening now (and where the money’s headed).

2. Who’s already building it (so you can invest, partner, or steal their playbook).

Each issue is short enough to read with your morning coffee but dense enough to move your roadmap.

However, most of the content from this newsletter will be only accessible to the paid subscribers.

If you are serious about building or investing in AI, you can subscribe here:

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1. Why BCG is so valuable for ElevenLabs

2. The BCG Playbook

3. How you can copy the Playbook (step by step) and distribute ElevenLabs & other AI products

The 5 key lessons from BCG × ElevenLabs:

• Consulting as GTM
• Services force real commitment
• Embedding directly into workflows
• Owning the interface, not the model
• Scaling experts with agents, not headcount

Don’t forget to subscribe to stay on top of the opportunities AI is creating right now.

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With the premium access you’ll get also access weekly to:

1. What’s happening now (and where the money’s headed).

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That’s more than 3× its $3.3B valuation (Jan 2025).

The bigger signal: they reportedly did $330M+ ARR in 2025 and want to double in 2026.

So I thought it was about time I started researching and studying what is the future of Voice AI and which other companies are building there.

This newsletter includes

1. The future of Voice AI (what actually changes next, and where value concentrates)

2. Voice AI startups founded in 2025 that got funded (the new cohort and what they’re betting on)

3. ElevenLabs preseed pitch deck: The 15-slide doc that unlocked their first $2M, set the narrative, and lined up the right investors fast.

1. The Future of Voice AI

Read more

This meme is becoming more recurring that I ever expected it to be.

I always thought it was just a matter o AI being so polivalent that startups without a MOAT, would be obviously crashed by the big models.

However, a recurring warning in the developer community is that OpenAI (and CEO Sam Altman) are running a familiar “platform playbook” associated with Microsoft and Facebook:

1. Open the platform just enough to attract builders,

2. learn what the ecosystem wants,

3. then consolidate the best-value product surface into the platform owner’s product, leaving third‑party developers commoditized, rate-limited, or displaced.

Jason Calacanis, frames this as the “Zuckerberg School of Business” (his phrase) and explicitly ties it to “Bill Gates and Microsoft” and Facebook’s platform history—urging developers not to build on OpenAI’s API.

In this newsletter I broke down:

1. The “Microsoft/Facebook platform playbook”: the mechanisms that made Microsoft and Facebook “dangerous dependencies” for developers,

2. Microsoft’s developer ecosystem playbook in the Windows and Office era

3. Facebook’s developer platform playbook

4. The OpenAI Platform Playbook

5. Practical caution for founders: how to benefit without becoming roadkill

1. The “Microsoft/Facebook platform playbook”

Read more

2 days ago, Sam Altman shared his vision for the future of AI at OpenAI’ Townhall.

Below are 12 moments from that conversation that actually matter if you’re building: reordered, compressed, and interpreted for people who care about product, distribution, and durability.

1. AI doesn’t reduce software, it radically expands it (Jevons Paradox)

A common mistake is to think AI reduces the need for more software.
Historically, the opposite happens.

When the cost of production drops, usage explodes.

AI lowers the cost of building software. That doesn’t eliminate demand, but it fragments it.

Expect:

• more niche tools

• more internal software

• more personalized systems

2. GTM didn’t get easier. Attention is still the bottleneck

Read more

I think it would be good that’s accessible for everyone.

This whole newsletter is around what industries AI is going to disrupt and where are the biggest opprtunities for us to capitalize.

We’ve talked about consulting, robotics, healthtech, neolabs, llms, rollups, legal tech and many more.

Now, AI is disrupting all the knowledge industries from legal to medicine.

It’s just a matter of time when AI can do all these jobs 100 times better than humans.

That will create a shift towards a new economy: the business of taste.

For a long time, this clip of a Rick Rubin interview has been a meme sent around as a joke.

The joke basically implied that Rick Rubin had no skills whatsoever and he’s basically just bullshiting his way around.

However, it looks like actually there’s a lot he knows that we don’t.

He describes the best artists as the ones that pick up faster what people want or need.

“The best comedians see the best jokes.” “You have the best joke because you see it best”

“You don’t believe what the structure around you asumes to be the case”

“We are taking information”

“If more than one person recommends something to me, I check it out”

What does this all mean for us in the business world?

Best founders are simply the ones that pick up faster than anyone what the market needs and are able to build it and give it to them faster than anyone.

AI, allows anybody to create good products with a fraction of the cost it used to. This is just going to keep decreasing.

That will basically allow anyone to be in any industry (at least knowledge industry).

The winners will be the people that pick up what the market needs first, and that have the best taste.

That, will be the business of taste.

1. Let me know what you think about this

2. If you enjoyed it share it on linkedin or x

3. Subscribe

   Subscribe now

Cheers,

Guillermo

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Trending right now: The Sequoia AI Ascent Deck

World’s most influential people share their thoughts and wants about the future.

I broke down the more relevant ones regarding AI:

1. The Day After AGI, Dario Amodei

2. Satya Nadella & Larry Fink on AI: AI is a platform shift

3. Alex Karp: AI exposes institutions that don’t actually work

4. Elon Musk: AI future is robots

5. Conclusions: what we can make of these talks in practice.

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Trending right now: The Sequoia AI Ascent Deck

1) Talk #1: “The Day After AGI” (Dario Amodei + Demis Hassabis)

The speed of AI progress depends on whether the self-improvement loop closes

Everything in their conversation revolved around a single question:

Can AI meaningfully help build better AI, fast enough, without humans doing most of the work?

Dario’s view is straightforward:

• models already write large amounts of production code

• the next step is end-to-end software work

• once that happens, AI accelerates the creation of the next model

• progress compounds

He’s not making a philosophical AGI claim.
He’s describing an engineering feedback loop.

Demis agrees the direction is real but highlights where the loop breaks:

• coding and math are easy because output is verifiable

• science, biology, and theory creation are not

• physical experiments, chips, and robotics introduce friction

So the disagreement isn’t about intelligence.
It’s about how much reality slows things down.

Why this matters

If the loop mostly closes, timelines collapse.
If it only partially closes, progress is still dramatic but more linear.

Either way, society is not ready for the speed they’re discussing.

Plain-language version

If machines start helping us build better machines, everything speeds up.
If they still need humans and the physical world, things slow down.

2) Satya Nadella & Larry Fink

AI is not a feature. It’s a platform shift that rewrites how work, companies, and economies are structured

Satya is placing AI on the same historical arc as the PC, the internet, and the cloud.

That framing matters, because platform shifts don’t add efficiency — they reorganize society.

What does “platform shift” actually mean?

A platform shift has three characteristics:

1. It digitizes something fundamental

2. It lowers the marginal cost to near zero

3. It creates entirely new workflows and power structures

The PC digitized individual productivity.
The internet digitized information access.
The cloud digitized infrastructure.

AI digitizes reasoning itself.

That’s why Satya keeps returning to the same idea:

Once something is digitized, software can reshape it endlessly.

Why this is bigger than “AI tools at work”

Most people interpret AI as:

• faster emails

• better search

• nicer dashboards

That’s missing the point.

Satya’s example about software development tells the real story:

• first: code completion

• then: conversational programming

• then: agents

• then: end-to-end project execution

This isn’t “better tooling.”
It’s changing the level of abstraction at which humans work.

Just like:

• assembly → C → Python

• documents → websites → apps

AI collapses the distance between intent and execution.

You don’t “build software” anymore.
You describe outcomes and software emerges.

The invisible consequence: organizations flatten

Satya slipped this in almost casually, but it’s one of the most important implications.

When AI can:

• instantly synthesize context

• pull information across silos

• brief anyone on anything

Then information no longer needs to:

• travel up hierarchies

• pass through layers

• be curated by middle management

The classic organizational pyramid starts to break.

The consequence of the AI platform shift is that reasoning becomes cheap, abundant, and embedded everywhere, which quietly rewires how work, companies, and economies function.

When reasoning is software, the distance between intent and execution collapses: fewer people are needed to produce the same output, hierarchies flatten because information no longer needs to flow upward, and advantage shifts from “who knows more” to “who can turn knowledge into action fastest.”

For individuals, this means skills compound faster but ladders thin out; for companies, it means workflows matter more than roles; and for countries, it means productivity, energy, and diffusion determine competitiveness.

It doesn’t feel like a revolution at first, but once it spreads, everything built for a slower, human-only reasoning world starts to break.

A concrete example: insurance underwriting (but this applies everywhere)

Before the platform shift

An insurance company looks like this:

• junior analysts gather data

• mid-level underwriters review cases

• seniors approve edge cases

• decisions take days or weeks

• a lot of value lives in people’s heads and emails

Reasoning is:

• slow

• expensive

• hierarchical

• bottlenecked by humans

This structure exists because reasoning is scarce.

After the AI platform shift

Now imagine:

• AI ingests all documents instantly

• evaluates risk across thousands of variables

• flags anomalies

• explains why a decision was made

• escalates only truly ambiguous cases

Suddenly:

• 1 senior underwriter oversees what 10 teams used to

• juniors are no longer required for data prep

• decisions happen in minutes

• the organization flattens

Nothing “magical” happened.
Reasoning just became software.

Profit comes from collapsing cost structures

If you remove:

• manual review

• duplicated effort

• waiting time

• error correction

You get:

• massive margin expansion

• higher throughput

• lower risk

This is why AI winners look boring on the surface.
They quietly print money.

3) Alex Karp

Conclusion: AI exposes institutions that don’t actually work

AI doesn’t reward “ideas,” it rewards institutions that actually work under real conditions and it brutally exposes the ones that only work on PowerPoint.

His whole Ukraine example is basically a stress test: in the real world you lose connectivity, your data is messy, people have different clearance levels, you can’t reveal sensitive info, enemies jam signals, the battlefield changes daily, and you still need decisions that are fast, auditable, and aligned with strategy and ethics.

Most organizations (governments and big companies) aren’t built for that.

They think they are, but half their “systems” don’t exist in a deployable way. So his punchline isn’t “buy an LLM.” It’s: if you don’t build a structured layer that turns messy reality into reliable action (with accountability), AI will just amplify your chaos.

That’s why he keeps emphasizing ontology/structure and why Palantir wins in low-trust environments: not because they have smarter models, but because they can make the organization operational when reality is adversarial.

3) Elon Musk:

Elon’s talk is basically him telling you: most people are debating the wrong layer of the stack.

They argue about models and “who wins the AI race,” but his mental model is physics-first: energy, manufacturing, robots, space.

1) His core thesis: AI progress is going to be bottlenecked by electricity, not algorithms

The most important thing he said (and it’s not the “smarter than humans” line) is:
we’re going to produce more AI chips than we can power.

That’s a very specific worldview: compute supply is compounding fast, but grid expansion is slow (single-digit % per year). In his head, the limiting reagent of intelligence is watts.

2) He sees “cost of AI” collapsing so fast that distribution becomes inevitable

He’s not framing AI as a premium product category. He’s framing it like:

• closed models lead

• open models follow ~1 year behind

• prices drop constantly

So his assumption is: AI will be everywhere because the marginal cost keeps approaching zero. That’s why he’s confident the benefits “will be broad”: companies will chase every customer and costs will keep falling.

What’s hidden in that assumption: when intelligence becomes cheap, it stops being a moat. The moat shifts to what you can do with it (robots, factories, distribution, energy).

3) His “abundance” argument is not philosophical — it’s a simple equation

He reduces the economy to:
(number of humanoid robots) × (productivity per robot)

Then he predicts:

• there will be more robots than humans

• eventually you “run out of things to ask for” because needs are saturated

What to take seriously: he’s saying the endpoint isn’t “AI tools help workers.” It’s labor becomes optional for basic production. That’s a very different future than “productivity gains.”

4) The most underrated claim: “space will be the cheapest place to run AI”

This is the most non-obvious part of his talk and it’s pure engineering logic:

• In orbit: solar is constant (no night/weather)

• Cooling is easier (radiate heat into space)

• You don’t fight land constraints or local grid bottlenecks

• The sun is effectively the ultimate energy source

So he’s suggesting a future where:
AI data centers move off-planet because power + cooling dominate cost.

Even if his 2–3 year timing is optimistic, the strategic idea is real:
if energy is the bottleneck, you look for environments where energy is abundant and cooling is cheap.

5) He’s unusually confident on timelines (and that’s the part you should treat carefully)

He claims:

• AI smarter than any human by end of this year / next year

• smarter than all humanity combined by ~2030–2031

Whether you believe it or not, it reveals how he’s operating:
he’s planning as if capability jumps are imminent, and he’s aligning Tesla/SpaceX around that assumption.

What you should learn from that (even if you disagree): the serious builders are not waiting for certainty. They’re building infrastructure that only makes sense if intelligence becomes abundant.

6) His view on safety is “control the physical layer,” not “pause AI”

He gives the obligatory Terminator joke, but he doesn’t argue for slowing down. He argues for:

• making robots safe and reliable before public deployment

• scaling manufacturing and energy so abundance happens

His safety posture is: build it, but engineer it properly.
Not “stop it.”

What we can make of this

First: AI is no longer a product advantage, it’s a production function.

Satya’s point, when you compress it, is that reasoning has become infrastructure. That means AI stops being “a feature” and starts being something every workflow assumes exists, the same way every workflow assumes databases or cloud exist today. For founders, this kills shallow differentiation. If your startup’s edge is “we use AI,” you’re already late. The advantage moves to who redesigns work itself: fewer handoffs, fewer approvals, tighter feedback loops between intent and action. For VCs, this means the best companies won’t look flashy. They’ll look boring, fast, and structurally different in how work gets done.

Second: AI brutally exposes whether an organization actually works.

Karp’s entire talk is a warning disguised as a battlefield story. AI doesn’t magically fix bad systems; it stress-tests them. If your data is fragmented, your permissions are unclear, your processes depend on heroics, or your decisions can’t be audited, AI will make that obvious very fast. That’s why so many AI pilots stall. The company didn’t fail to “adopt AI.” It failed to face reality. As an investor, this shifts diligence away from demos and toward institutional readiness. As a founder, it means the unglamorous work—structure, ownership, accountability—is now a competitive moat.

Third: intelligence is becoming cheap; energy and execution are not.

Elon’s contribution cuts through the noise. The bottleneck isn’t models. It’s electricity, manufacturing, cooling, and scale. Intelligence wants to run all the time. Whoever can power it cheapest and deploy it fastest wins. This reframes the AI race away from prompt engineering and toward infrastructure, energy, and industrial capacity. For founders, it means enormous opportunity outside “pure software”: energy, robotics, hardware, supply chains, industrial AI. For VCs, it means some of the biggest outcomes of this cycle won’t look like SaaS at all.

Fourth: abstraction levels are rising, and careers are thinning.

Satya is right that coding doesn’t disappear; it moves up a level. Karp is right that aptitude matters more than credentials. Elon is right that abundance removes necessity. Put together, the message is uncomfortable but clear: fewer people will be needed to produce the same output, but the people who remain will matter more. For founders, this means small teams can now attack problems that once required giants. For investors, it means team quality and adaptability matter more than headcount, pedigree, or story.

Fifth: diffusion, not invention, decides winners.

All three, in different ways, say the same thing: the value isn’t created by the people who invent the technology, but by the people who diffuse it into reality. Countries, companies, and sectors that actually use AI to change outcomes will outperform those who debate it, regulate it prematurely, or fetishize safety without deployment. For founders, this is permission to move faster than incumbents. For VCs, it’s a reminder that adoption curves beat technical elegance every time.

Finally: this is not a bubble problem, it’s a leadership problem.

If this were just about tech valuations, it would be a bubble. But what these talks show is something deeper: AI forces decisions. About work. About energy. About institutions. About truth. Companies and governments that avoid those decisions will fall behind quietly at first, then suddenly. The winners won’t be the loudest believers or skeptics. They’ll be the ones who redesign reality while everyone else argues about it.

Hope this was valuable!

If it was, please share with other investors & founders.

Share

Cheers,

Guillermo

Frequently Asked Questions (FAQ)

What were the main AI takeaways from World Economic Forum in Davos?

The dominant theme was that AI has moved from experimentation to infrastructure. Leaders no longer discussed whether AI will matter, but how fast it will reshape companies, governments, and economies. The most important insights focused on speed of progress, institutional readiness, energy constraints, and execution—not on model demos.

What did Dario Amodei mean by “The Day After AGI”?

Dario Amodei described a practical engineering scenario rather than a philosophical AGI moment:
AI systems increasingly help build better AI systems. If this feedback loop becomes mostly autonomous, progress compounds rapidly. The key uncertainty is how much the physical world—chips, experiments, and real-world validation—slows the loop down.

Why did Demis Hassabis disagree with full AI self-improvement?

Demis Hassabis agreed that AI self-improvement is real, but argued that it breaks down outside of domains with easy verification. Coding and math scale well; biology, science, and real-world experimentation introduce friction. The disagreement is about speed, not capability.

Why does Satya Nadella say AI is a “platform shift”?

Satya Nadella argues AI is comparable to the PC, the internet, or the cloud. Platform shifts don’t just add efficiency—they restructure how work happens. AI digitizes reasoning itself, collapsing the distance between intent and execution and raising the abstraction level of most jobs.

What does “AI as a platform shift” mean in practical terms?

It means:

• Reasoning becomes cheap and embedded everywhere

• Workflows matter more than roles

• Organizations flatten because information no longer needs to move up hierarchies

• Advantage shifts from “who knows” to “who executes fastest”

AI doesn’t just improve tasks—it rewrites how companies are structured.

How will AI change organizational hierarchies?

When AI can instantly synthesize context, retrieve information, and explain decisions, middle layers designed to move information upward become less necessary. Fewer people can oversee more output. This doesn’t eliminate leadership—but it compresses organizational pyramids.

Why did Larry Fink emphasize AI’s economic impact?

Larry Fink focused on AI as a productivity and capital-allocation force. When reasoning costs approach zero, margins expand, throughput increases, and capital flows toward firms that redesign cost structures rather than merely adopting tools.

What does Alex Karp mean when he says AI exposes institutions that don’t work?

Alex Karp argues that AI doesn’t fix broken systems—it stress-tests them. If an organization relies on informal processes, unclear ownership, fragmented data, or heroic effort, AI will amplify the chaos rather than solve it.

Why does Palantir succeed in low-trust or adversarial environments?

Because it focuses less on “smarter models” and more on structure: ontology, permissions, auditability, and accountability. In messy real-world conditions (war, national security, critical infrastructure), that operational layer matters more than raw model intelligence.

What is Elon Musk’s core thesis on AI?

Elon Musk believes most AI debates focus on the wrong layer. The real bottleneck isn’t algorithms—it’s energy, manufacturing, and physical deployment. Intelligence is scaling faster than our ability to power it.

Why does Elon Musk think electricity will limit AI progress?

AI chips are compounding faster than grid capacity. Power generation and transmission grow slowly, while compute demand explodes. In Musk’s view, watts—not models—will cap near-term AI scaling.

Why does Elon Musk believe AI will become cheap and ubiquitous?

He expects a pattern where:

• Closed models lead

• Open models follow within ~1 year

• Prices continuously fall

As marginal costs drop, AI stops being a moat. Competitive advantage shifts to what you do with intelligence, not access to it.

Why does Elon Musk think robots are the future of AI?

He frames the economy as:
(number of humanoid robots) × (productivity per robot)

In this model, abundance comes from physical execution, not digital tools. AI’s ultimate impact is not better software—but making labor optional for basic production.

Why did Elon Musk suggest space as a future location for AI data centers?

From an engineering perspective:

• Solar power is constant in orbit

• Cooling is easier via radiation

• There are fewer land and grid constraints

If energy and cooling dominate AI costs, space becomes strategically interesting—even if timelines are optimistic.

Is AI safety discussed as slowing down development?

No. The dominant view among builders is engineering safety at the physical and institutional layer, not pausing progress. The emphasis is on control, reliability, and deployment discipline rather than halting capability gains.

What does all this mean for startup founders?

• “We use AI” is no longer differentiation

• The advantage lies in redesigning workflows, not adding tools

• Small teams can now tackle problems that once required large organizations

• Structural clarity, speed, and execution matter more than vision decks

What does this mean for VCs and investors?

• The best AI companies may look boring, not flashy

• Diligence should focus on institutional readiness, not demos

• Some of the biggest outcomes will be in energy, hardware, robotics, and infrastructure—not SaaS alone

• Adoption speed beats technical elegance

Is this AI cycle a bubble or something bigger?

It’s not a valuation problem—it’s a leadership problem. AI forces decisions about work, energy, institutions, and accountability. Those who avoid those decisions fall behind quietly, then suddenly.

What is the single unifying message from Davos on AI?

AI is no longer optional, experimental, or theoretical.
It is becoming infrastructure.

And infrastructure doesn’t ask for permission—it reshapes everything built on top of it.

Hey everyone, my name is Guillermo Flor and I’m an entrepreneur and Angel Investor.

Sequoia’s AI Ascent is one of those rare events where the people closest to the frontier stop doing vibes and start doing maps.

It’s not “AI is big” content.

It’s: what’s actually changing, where value will accrue, and what builders should do next.

Today, I wanted to share a deck especially worth studying because it compresses the whole transition into 20 slides:

• A clean definition of what AI really is: Create → Reason → Interact

• Why this wave is the next platform shift after internet, apps, mobile

• The “white space” chart showing where AI is about to re-write entire categories

• Proof that AI is already shipping into real workflows (support, legal, engineering) not just demos

• The speed of adoption: AI curves that look nothing like classic SaaS and early scale signals across consumer + enterprise

• The big open question: AI’s $200B question

• What’s coming next: moving from copilots to agents, then planning/reasoning, then reliability

If you’re building in AI (or investing in it), this is the kind of artifact you keep open in another tab while you plan your product and your roadmap.

PS: You can access my whole breakdown of AI Ascent here.

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Founded in 2003 with a mission to prevent terror attacks through better data analysis, Palantir from day one paired powerful data integration software with intensive on-site engineering support.

This newsletter-style guide breaks down:

1. The Origin Story: A Software Solution to Intelligence Failures

2. Palantir Gotham: Early Development and Deployment

3. The Hybrid Model: Software + Consulting from Day One

4. Early Client Acquisition and Onboarding Strategies

5. Metropolis and Foundry: Pivoting to the Enterprise

6. Internal Workflows and Culture in Early Days

7. Lessons for Founders, Investors, and Consultants

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The Origin Story: A Software Solution to Intelligence Failures

Palantir’s story begins in the aftermath of 9/11. Peter Thiel (fresh off PayPal’s success) and a team of co-founders (Alex Karp, Stephen Cohen, Joe Lonsdale, and Nathan Gettings) believed that better tools could “connect the dots” and prevent future attacks.

With $30 million from Thiel and around $2 million from the CIA’s In-Q-Tel venture arm, Palantir was born as a company “designed to support U.S. intelligence services in data analysis, preventing possible threats”.

Early on, Palantir drew on anti-fraud ideas from PayPal – using software to augment human analysts rather than replace them, thus preserving civil liberties while catching bad actors

From 2005 to 2008, the CIA was Palantir’s sole patron and alpha customer.

This close partnership allowed Palantir’s engineers to iterate their product (later named Palantir Gotham) with direct CIA feedback, essentially “co-developing” the platform in a real operational context.

CIA analysts tested Palantir’s ability to ingest disparate data (from phone records to watchlists) and find hidden links – something existing tools like Analyst’s Notebook struggled with.

By 2008, Palantir was ready to officially launch Palantir Gotham, its flagship intelligence platform.

CIA’s stamp of approval gave Palantir credibility (Langley’s imprimatur), paving the way for other U.S. agencies to give this upstart a try.

Palantir Gotham: Early Development and Deployment

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