Last year, Sequoia AI Ascent felt like the clearest map of the AI market.

The big ideas were obvious but important:

• AI was not just another software cycle.

• The application layer could capture massive value.

• Agents were becoming the next platform shift.

• And the best founders had to move from building tools to building systems that do real work.

One year later, Sequoia opened AI Ascent 2026 with Andrej Karpathy.

That choice matters.

Karpathy is not just another AI commentator. He helped cofound OpenAI, led important AI work at Tesla, helped make concepts like Software 2.0 mainstream, and last year gave the world the phrase “vibe coding.”

But this talk was not just about vibe coding.

It was about something much bigger:

Software is changing again.

Karpathy’s core point is that we are moving into Software 3.0.

• Software 1.0 was code written by humans.

• Software 2.0 was neural networks trained with data.

• Software 3.0 is software programmed through prompts, context, agents, tools, memory, and verification.

That sounds abstract, but the implications are very practical.

This is the full breakdown of Karpathy’s opening conversation at Sequoia AI Ascent 2026.

We’ll cover:

1. What Software 3.0 actually means

2. Why Karpathy says he has never felt more behind as a programmer

3. Why some AI apps are temporary wrappers around model limitations

4. Why verifiability explains where AI will automate first

5. The difference between vibe coding and agentic engineering

6. Why the best founders should build for agent-native workflows

7. And why understanding, taste, and judgment become more valuable as intelligence gets cheaper

8. Vibe coding raised the floor. Agentic engineering raises the ceiling

9. Hiring also has to change

10. Human taste, judgment, and understanding become more valuable

11. The agent-native world is coming

12. The final lesson: intelligence gets cheap, but understanding stays scarce

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1. Karpathy felt behind because the agentic shift became real

Open with the surprising line:

Andrej Karpathy, one of the people who helped build modern AI, said he has never felt more behind as a programmer.

Not because he forgot how to code.

Because the tools crossed a threshold.

His point: around late 2025, coding agents stopped being “helpful autocomplete” and started producing large chunks of useful code that often worked without correction.

This matters because it changed the emotional experience of programming:

• before: the model helped with snippets

• now: the model can push entire workflows forward

• before: you corrected the model constantly

• now: you increasingly supervise it

• before: AI felt like a tool

• now: it feels closer to a junior team

2. Software 3.0 means prompts are becoming programs

Karpathy’s framework:

Software 1.0: humans write explicit code
Software 2.0: humans train neural networks with data
Software 3.0: humans program models through context

The important part is not “prompt engineering.”

The important part is that the context window becomes the new programming surface.

You are no longer only writing deterministic instructions for a computer. You are giving context to an intelligent interpreter that can read, reason, call tools, inspect environments, debug errors, and adapt.

This changes what building means.

In Software 1.0, you write a shell script.

In Software 3.0, you give the agent a piece of text and say:

“Install this. Inspect my environment. Fix what breaks. Make it work.”

3. The OpenClaw installer shows why agent-native software will look different

Karpathy’s example: installing OpenClaw.

In the old world, you would write a complex bash script that tries to handle every environment, platform, dependency, and edge case.

In the new world, the installation is just a block of instructions for an agent.

The agent reads the instructions, looks at your computer, understands what is missing, runs commands, sees errors, and adapts.

That is much more powerful than a rigid script.

The bigger point:
Most software today is still built for humans clicking buttons and reading docs.

But agents need something else:

• clear instructions

• structured context

• machine-readable documentation

• direct permissions

• APIs and workflows designed for agent execution

• fewer human-only menus and settings pages

Founder opportunity:
The next wave of infrastructure will be agent-native. Not user-native.

4. MenuGen shows the biggest trap: building apps that should not exist

This is the best story in the talk.

Karpathy built MenuGen: an app that lets you take a picture of a restaurant menu and generate images of the dishes.

The traditional app flow was:

• upload photo

• OCR the menu

• extract the dishes

• generate dish images

• rebuild the menu

• display everything in a UI

Then he saw the Software 3.0 version:

Take a photo of the menu. Give it to Gemini. Ask it to overlay images of the food directly onto the menu.

No app.
No OCR pipeline.
No UI.
No complex workflow.

Just model input and model output.

Key insight:
A lot of AI apps are temporary wrappers around model limitations.

As models get better, entire product categories collapse into a prompt.

This is the most important founder lesson in the talk:

Don’t just ask what AI can help you build faster. Ask what AI makes unnecessary.

5. The next obvious companies will come from things that were previously impossible

Karpathy says the exciting part is not just speeding up existing workflows.

It is creating things that could not exist before.

Example: an LLM-generated knowledge base.

Before AI, there was no normal software program that could take a messy pile of documents, understand them, restructure them, rewrite them, and turn them into a useful wiki.

Now that becomes possible.

This is the category to watch:

information transformation.

Not just search.
Not just chat.
Not just summarization.

But systems that can take unstructured information and recompile it into new forms:

• company wikis

• investor memos

• research maps

• product specs

• operating manuals

• market maps

• internal training systems

• strategic briefs

• personal knowledge bases

6. Verifiability explains where AI will automate first

Karpathy’s framework on verifiability is one of the most important parts of the talk.

Traditional software automates what you can specify.

AI automates what you can verify.

That is why AI is so strong in domains like:

• code

• math

• tests

• security

• data workflows

• certain research tasks

• structured business processes

These domains are easier because the model can be trained with clear feedback.

The model can try something, get a reward, and improve.

But this also explains why AI feels so weird.

It can refactor a huge codebase and still fail at a simple common-sense question.

That is what Karpathy calls jagged intelligence.

Key insight:
AI capability is not evenly distributed. It spikes in places where labs have data, rewards, and verification loops.

7. The founder playbook: find valuable verifiable environments

The big founder question is:

Where can you create a valuable verification loop that the foundation labs are not focused on yet?

The labs are already going hard after coding, math, reasoning, and general agents.

But there are many high-value business domains where outputs can be verified and models can improve:

• finance operations

• tax workflows

• compliance checks

• insurance claims

• procurement

• contract review

• medical admin

• customer support QA

• cybersecurity

• internal reporting

• sales operations

• logistics

• accounting

• enterprise data cleaning

The opportunity is not “build another wrapper.”

The opportunity is:

1. Find a valuable workflow

2. Break it into tasks

3. Define what good output means

4. Create verification loops

5. Collect edge cases

6. Fine-tune or orchestrate models around that domain

7. Build the system that gets better every time it runs

Key insight:
The next strong AI companies may look less like SaaS products and more like domain-specific reinforcement learning environments.

8. Vibe coding raised the floor. Agentic engineering raises the ceiling

Karpathy makes a clear distinction.

Vibe coding is when anyone can build software by describing what they want.

It raises the floor.

A non-technical person can now create apps, websites, tools, automations, prototypes, and internal systems.

But agentic engineering is different.

Agentic engineering is about using agents while preserving professional quality.

That means:

• no security holes

• no messy architecture

• no broken abstractions

• no fragile systems

• no random code nobody understands

• no “it works somehow” production software

The human is still responsible for quality.

Key insight:
The best engineers will not be the ones who write every line of code. They will be the ones who can direct agents without letting quality collapse.

9. Hiring also has to change

Karpathy makes a very sharp point:

Most companies are still hiring engineers with old-world tests.

Small coding puzzles made sense when the job was writing code line by line.

But if the job is now agentic engineering, the test should change.

A better test might be:

Build a large project with agents. Make it secure. Make it work. Then let other agents try to break it.

That tests the real skill:

• problem decomposition

• taste

• architecture

• security thinking

• agent management

• quality control

• debugging

• product judgment

Founder implication:
The best AI-native engineers may not look best in traditional coding interviews.

They will look best when asked to ship large, real systems with agentic tools.

10. Human taste, judgment, and understanding become more valuable

Karpathy’s strongest point near the end:

You can outsource thinking.
You cannot outsource understanding.

Agents can write code, generate drafts, call tools, and execute tasks.

But humans still need to decide:

• what is worth building

• what good looks like

• what trade-offs matter

• what the system should optimize for

• what should be simple

• what should be secure

• what should not exist at all

This is where taste matters.

Karpathy gives the example of an agent making a strange product decision: matching Stripe payments to Google accounts through email addresses instead of persistent user IDs.

The code may work.

But the design is wrong.

11. The agent-native world is coming

Karpathy’s future world is simple:

Everything gets rewritten for agents.

Today, most software assumes a human user:

• read this doc

• click this button

• go to settings

• configure DNS

• copy this API key

• paste this value

• deploy here

That is painful for agents.

The agent-native version is different:

• agents get clear instructions

• tools expose machine-readable interfaces

• permissions are explicit

• workflows are decomposed into sensors and actuators

• docs are written for agents first

• agents talk to other agents

This is where the world is going:

“I’ll have my agent talk to your agent.”

12. The final lesson: intelligence gets cheap, but understanding stays scarce

End with the education point.

When AI gets better, the temptation is to learn less.

Karpathy argues the opposite.

Understanding becomes the bottleneck.

You still need enough depth to direct the system. You need to know what to ask, what to inspect, what to reject, and what matters.

AI can help you think.

But it cannot replace the need to understand.

Hope this was valuable!

Cheers,

Guillermo

Here are the facts:

AI is no longer just a software market.

It is becoming an industrial market.

Every AI agent consumes tokens.
Every token consumes compute.
Every unit of compute needs chips, memory, networking, data centers, power, cooling and capital.

That is why compute is becoming the new oil.

But the opportunity is not just Nvidia.

The opportunity is the entire compute supply chain.

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Most of the headlines we see are about the obvious part of the AI boom: another model launch, another giant funding round, another AI startup raising at a crazy valuation.

But there may be a safer and potentially huge opportunity underneath all of that.

The real question is not only who builds the best AI app.

It is who owns, finances, powers, connects, rents or optimizes the compute those AI apps need to run.

So in this newsletter, I’ll break down the compute market in a simple way: the main layers, the companies exposed to each layer, and where the biggest investment opportunities may be.

Let me know what you think!

👋 Just a quick note before you continue!

Building great AI systems and agents is quickly becoming one of the most important advantages a company can have. So we’ve been building non-stop and this will become a much bigger part of AIMF and PMF going forward.

From now on, as a PMF + AI subscriber, you’ll get access not just to the best resources on fundraising and growth (160+ Playbooks, 65+ Success Stories, 30+ Pitch Deck Collections, 25+ Investor Resources), but also:

• 🤖 3+ New AI agent Templates Every Week

• 📚 A Growing Library of 20+ AI Agents

Here, few examples:

• The McKinsey Slide Playbook for Claude

• Claude x Clay Playbook

• The Ultimate Guide to Building Skills for Claude

• Claude For Finance: The CompIete Guide

• The Claude Playbook for Reddit Prospecting

• Claude for AI SDR: The Complete Guide

• Linkedin X Claude Agent Playbook

• The Claude Code Founder Guide

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

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The market map

Read more

The agentic stack has six greenfield lanes. Here’s where to build, where to invest, and where the next category leaders get founded in the next 18 months.

McKinsey published a report this month on what they call the “agentic organization.” The headline number: more than 80% of enterprises investing in AI aren’t seeing bottom-line impact. Their frame is a five-pillar model for executives running transformation programs.

Read as a transformation report, it’s fine. Read as a market signal, it’s something else: a map of where the money is about to move.

Eighty percent of enterprises are stuck. They have ChatGPT licenses and a few pilots and no idea how to get from there to the organization McKinsey describes. That gap is the opportunity. Every problem McKinsey names is a startup waiting to exist or an investment waiting to be made.

Here’s the map.

Six layers in the agentic stack:

• what’s already won,

• what’s still open, and

• where the next $10B company gets founded in the next 18 months.

• At the end, five questions to separate real agentic companies from wrappers.

Let’s get to work

👋 Just a quick note before you continue!

Building great AI systems and agents is quickly becoming one of the most important advantages a company can have. So we’ve been building non-stop and this will become a much bigger part of AIMF and PMF going forward.

From now on, as a PMF + AI subscriber, you’ll get access not just to the best resources on fundraising and growth (160+ Playbooks, 65+ Success Stories, 30+ Pitch Deck Collections, 25+ Investor Resources), but also:

• 🤖 3+ New AI agent Templates Every Week

• 📚 A Growing Library of 20+ AI Agents

Here, few examples:

• The McKinsey Slide Playbook for Claude

• Claude x Clay Playbook

• The Ultimate Guide to Building Skills for Claude

• Claude For Finance: The CompIete Guide

• The Claude Playbook for Reddit Prospecting

• Claude for AI SDR: The Complete Guide

• Linkedin X Claude Agent Playbook

• The Claude Code Founder Guide

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

Get 7 day free trial

Read more

The quote went viral. The takes wrote themselves. The ladder is disappearing. The bottom rungs are dissolving. Run.

I want to offer a different read.

Subscribe now

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

Access now

• 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

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What’s actually happening

Amodei is right that the work junior analysts, associates, and consultants do today.

Drafting from templates, running comps, synthesizing 200 pages into 20 bullet points is exactly what language models are built to do. He’s not wrong about the capability curve. He’s not wrong that enterprise buyers are deploying Claude to do this work right now.

But “the work is being automated” and “the role is disappearing” are two different claims.

The work is being automated. The role is being rewritten.

The new job description

Here’s the version of the story that nobody went viral with: a junior lawyer who knows how to direct Claude through discovery, draft memos, cross-check citations, and pressure-test arguments doesn’t do less work than the 2019 version of that role. They do ten times more. They do it at a quality that used to require three years of experience. And they do it while the partner is still on the first call of the morning.

That person isn’t being replaced. They’re being promoted on day one, whether their title reflects it or not.

The juniors who disappear are the ones who treat AI like a threat to hide from, or a novelty to dabble with. The juniors who thrive are the ones who treat it like the most important tool they’ll ever learn to use — more important than Excel was for finance in the 90s, more important than Bloomberg terminals, more important than legal research databases.

What the bar looks like now

If you’re early in your career in one of these fields, the honest expectation has shifted. It’s not enough to be smart, willing, and good at following instructions. That was the old deal. The new deal is closer to:

Know your craft well enough to spot when the model is wrong. Know your tools well enough to get ten times the output without ten times the errors. Know your clients and colleagues well enough that your judgment — not your typing speed — is what people are paying for.

This is a higher bar than the one that existed five years ago. It’s also a more interesting job. The parts of junior work that were genuinely soul-crushing — the document review marathons, the deck-formatting death marches, the comps-building drudgery — those parts are going away. What’s left is the thinking.

What seniors should actually be doing

If you manage or mentor junior people, the Amodei quote isn’t a reason to stop hiring them. It’s a reason to hire differently and train harder.

The junior who can run an AI-augmented workflow at partner-review quality is worth more than three juniors who can’t. That’s a real shift in how teams should be structured, but it doesn’t mean teams shrink to zero. It means the ratio changes, and the training gets more deliberate.

The firms that quietly figure this out in the next eighteen months will pull away from the ones still arguing about whether AI belongs in the workflow at all.

The part worth sitting with

Amodei’s prediction reads as a warning because it sounds like mass unemployment. Read it again with fresh eyes and it’s something else: a forecast that the floor of what counts as competent professional work is about to rise dramatically.

That’s not a career-ending event. It’s a career-defining one, for people who take it seriously now rather than in 2029.

Five years is a long time. Long enough to get very good at something. Long enough to become the person on your team who everyone else quietly asks for help. Long enough to build the kind of leverage Amodei is describing — not to be displaced by it.

The question isn’t whether your job survives. It’s whether you do the work now to become the version of yourself that the new job requires.

Starting, as always, this week.

Cheers

Guillermo

OH PS: My friends from Newcomer just announced their annual AI Summit in London: Cerebral Valley. I went last year and it was amazing. Check it out!

So I thought it could be very valuable to break it down for you.

💭 Remember!

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

Access now

• 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

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Inside you will find:

1. Why Jensen thinks Nvidia’s real moat is much bigger than GPUs

2. Why he believes AI will increase software usage, not destroy it

3. Why supply chain coordination is one of Nvidia’s biggest strategic advantages

4. Why he still thinks GPUs beat TPUs and custom ASICs

5. Why he sees China as an ecosystem battle, not just an export control debate

1. Nvidia’s real business: turning electrons into tokens

The best line in the interview was also the simplest:

The input is electrons. The output is tokens. In the middle is Nvidia.

That is probably the cleanest way to understand the company.

Nvidia is not just trying to build the best chip.

It is trying to build the best system for transforming energy into useful AI output.

That includes:

• silicon

• packaging

• networking

• interconnects

• software

• libraries

• optimization

• algorithms

• and the ecosystem around all of it

This is why Jensen pushes back so hard on the idea that Nvidia could get “commoditized” just because parts of the stack are outsourced or because software is becoming easier to generate.

His point is simple:

the hard thing is not writing code

the hard thing is making the whole stack work together at extreme scale.

That is where Nvidia lives.

And that is why he does not think the company is becoming a commodity.

2. The moat is not the chip. The moat is the system.

A lot of people still talk about Nvidia as if its advantage is mostly having the fastest GPU.

That is too narrow.

Jensen’s argument is that Nvidia’s moat has four layers.

A. CUDA

CUDA is still the center of gravity.

Not because customers cannot write their own kernels.

They can.

But because CUDA gives developers the richest base layer to build on:

• the deepest tooling

• the most mature libraries

• the broadest compatibility

• the largest install base

That matters more than raw benchmark slides.

If you are building an AI company, you want your software to run everywhere. Nvidia gives you that.

B. Install base

Jensen repeatedly comes back to this.

Nvidia is everywhere:

• all major clouds

• enterprise data centers

• startups

• research labs

• robotics

• edge systems

That means software built on Nvidia has immediate distribution.

That is an underrated advantage.

C. Full-stack co-design

Nvidia is not just making chips in isolation.

It can change:

• the processor

• the fabric

• the network

• the system design

• the libraries

• the kernel layer

• and sometimes even the algorithmic implementation

That is a different game from building a narrow accelerator.

D. Supply-chain orchestration

This may be the least appreciated part of Nvidia’s moat.

Jensen makes it clear that Nvidia’s strength is not just invention.

It is coordination.

The company can align upstream suppliers, downstream buyers, packaging partners, memory providers, foundries, OEMs, clouds, and application builders around a future that it sees earlier than most of the market.

That is not a normal chip-company capability.

That is industrial power.

So the real takeaway is this:

Nvidia’s moat is not one thing. It is compute architecture + software + distribution + supply chain + ecosystem.

That is why it is so hard to dislodge.

3. Jensen’s contrarian view: AI may make software companies bigger

This was one of the most important parts of the conversation.

The current market narrative says AI will commoditize software.

Jensen’s view is almost the opposite.

He thinks AI will cause tool usage to explode.

Why?

Because the number of software users is about to stop being limited by the number of humans.

In the old world:

• one engineer used one set of tools

• one analyst used one stack

• one designer used one interface

In the new world:

• each person may have multiple agents

• each agent may use multiple tools

• each workflow may generate far more tool interactions than before

That means the number of software “users” could increase massively, even if many of those users are agents rather than people.

He gave examples like:

• EDA tools

• design compilers

• floor planners

• engineering systems

His point is that companies are not going to use fewer tools.

They may end up using far more of them, because agents will become tool users too.

This is a very important idea.

If Jensen is right, AI does not simply compress software spend.

It changes the unit of demand.

From human seat count to human + agent output.

That could be one of the biggest shifts in software over the next few years.

4. Why Nvidia keeps scaling while others get stuck

Another core message from the interview:

Nvidia’s advantage is not just that demand is high.

It is that the company has learned how to prepare the system around that demand.

Jensen describes a world where Nvidia is constantly:

• forecasting demand years ahead

• identifying future bottlenecks

• informing suppliers before the bottleneck hits

• getting them to invest early

• helping shape the ecosystem so capacity is there when needed

That is how you get from “everyone is bottlenecked” to “we are still shipping at scale.”

He mentions this directly across:

• CoWoS

• HBM

• photonics

• logic capacity

• packaging

• testing workflows

• and even basic labor constraints like electricians and plumbers

His broader point is:

most bottlenecks are not permanent

they are usually 2 to 3 year problems if the demand signal is strong enough and someone coordinates the response.

That is a very bullish view on compute scaling.

But he does highlight one bottleneck he takes more seriously than chips:

5. Energy is the real bottleneck

Jensen sounds relatively calm about semis bottlenecks.

He does not sound calm about energy.

That came through clearly.

You cannot build:

• AI factories

• reindustrialization

• robotics

• EV production

• advanced manufacturing

• new data centers

without energy.

His message is that America can solve packaging, memory, and foundry bottlenecks faster than it can solve energy bottlenecks.

That matters because it shifts the real strategic question.

The future of AI infrastructure may depend less on who can tape out the next chip, and more on who can secure the power to run it.

That is a much bigger systems question.

6. Why he still thinks GPUs beat TPUs and ASICs

This was the most technical part of the interview, but the core argument was clear.

Jensen does not deny that TPUs and ASICs can be strong for specific workloads.

What he denies is that the future of AI is stable enough for narrow specialization to dominate.

His reasoning:

AI is not just one repeated matrix multiply forever.

The field keeps changing through:

• new attention mechanisms

• new architectures

• new routing methods

• new parallelization patterns

• new model designs

• new inference patterns

• new system-level tradeoffs

In that world, programmability wins.

That is the heart of the Nvidia case.

Jensen’s claim is that most of the big leaps in AI performance do not come from pure semiconductor scaling.

They come from co-design across the stack.

That is how Nvidia got the kind of gains he references from Hopper to Blackwell.

Not from Moore’s Law alone.

But from:

• numerics

• system architecture

• software

• model design

• distribution of workloads

• network and fabric improvements

• and deep optimization at every level

That is why he keeps returning to CUDA.

Not because CUDA is some magical piece of software.

But because it is the programmable layer that lets Nvidia adapt as AI changes.

That is the strategic point.

ASICs can be good at a frozen problem. Nvidia is betting AI will not stay frozen long enough.

7. Nvidia’s strategy is to do only what nobody else will do

Jensen repeats one idea again and again:

do as much as needed, as little as possible

This is one of the cleanest explanations of Nvidia’s strategy I’ve heard.

It explains why Nvidia:

• builds core platform layers

• supports neoclouds instead of becoming one

• invests in ecosystem players

• helps labs scale

• but avoids absorbing every adjacent business

His test seems to be:

If Nvidia does not do this, will it happen anyway?

If the answer is no, Nvidia steps in.

If the answer is yes, Nvidia prefers to partner.

That is why it built CUDA and NVLink.

That is why it helps enable CoreWeave, Crusoe, and others.

That is why it invests in model companies.

But it is also why it does not want to become the entire stack.

This is not empire building for its own sake.

It is selective control around the hardest irreplaceable layers.

That discipline is part of why Nvidia has stayed so coherent while expanding so aggressively.

8. China: Jensen’s real fear is ecosystem loss

The China section was the most heated, but Jensen’s underlying point was consistent.

He thinks many people frame the issue too narrowly.

The standard debate is:

Should America sell advanced compute into China or not?

Jensen’s framing is different:

What happens if America forces the world’s second largest market to build around a non-American stack?

That is the part he keeps coming back to.

His concern is not only short-term revenue.

It is long-term ecosystem drift.

He believes that if Nvidia is pushed out:

• Chinese developers optimize elsewhere

• open-source models may optimize elsewhere

• the global software ecosystem may tilt elsewhere

• and America weakens one of the most important layers of its own AI stack

You can disagree with him on policy.

But the strategic logic is important.

He is not defending China sales only as transactions.

He is defending them as a way of keeping the global AI ecosystem anchored to the American compute stack.

That is a much bigger argument than “we want to sell more chips.”

It is really an argument about platform control.

9. The hidden worldview behind the interview

Step back from the specific topics and you can see Jensen’s broader worldview.

He believes:

• AI is an industrial revolution, not just a software cycle

• the winners will be the ones that build systems, not features

• software usage will rise because agents will become users

• supply chains are strategic weapons

• energy matters more than most people realize

• programmability matters more than narrow optimization

ecosystems matter more than products

• and America should lead by scaling, not by retreating

This is why Nvidia feels less like a chip company every quarter.

And more like the operating layer for the AI economy.

Hope this was valuable!

Cheers,

Guillermo

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hey everybody,

I’ve been diving really deep into AI for the last 4 months, to the point I’m now dedicating 70-80% of my time to coding.

AI is just blurring the lines between what a developer and a non developer can build, but not only that, it’s creating tools that can boost personal productivity to limits we still need to see.

the way people use ai is going to be super specific, and will substitute many jobs that are low value adding but will create so many new jobs.

but, where is the big opportunity now? i guess that’s the greatest question to solve.

here’s what i think:

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The Four Categories That Will Capture Most of the Value

Read more

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

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

Read more

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 🔥

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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

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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

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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

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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!

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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.

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The 5 key lessons from BCG × ElevenLabs:

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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

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