Juan Bernal

CEO @ Analytix | Executive Chair GSS Group. Author of: Deserving

[Juan Bernal @jpbernal]

https://t.co/W77FRtrom5

[Elad Gil @eladgil]

People at major AI labs (using internal models) 3-4 months ahead of startup silicon valley engineers

SV founders/eng 3-6 months ahead of NY

NY founders/eng 6-12 months ahead of rest of world

Most people have no idea how fast AI shifting as 1-2 years behind SOTA

"The future is here, just not equally distributed" - Robert Heinlein

[Marc Andreessen 🇺🇸 @pmarca]

“A man is what he thinks about all day.”—Emerson

[Juan Bernal @jpbernal]

Connect with Juan Bernal at the conference https://t.co/kALeH2UMBl

[Juan Bernal @jpbernal]

@zachrose51 https://t.co/1MgvJOMo2H

[Juan Bernal @jpbernal]

@Jason I’m in

[Juan Bernal @jpbernal]

@MarikHazan @ycombinator Feltsense

[Juan Bernal @jpbernal]

“Try not. Do or do not. There is no try.” - Yoda

[Refik Anadol @refikanadol]

They said photography wasn’t art.
They said cinema wasn’t art.
They said video games weren’t art.
Now they say AI arts/digital art isn’t art.

I’ve spent over a decade with my studio team turning millions of data points into living, breathing artwork experiences ethically — at MoMA, at the Guggenheim, at the Venice Biennale. Not because a machine told me what to create, but because I had a vision that no traditional tool could realize.

Denying all AI technologies as an artistic medium doesn’t protect art. It limits it. The artists who embrace new tools don’t replace the old masters — they join them.

Art is not defined by the brush. It’s defined by the intention, the emotion, and the courage to see the world differently.

[Juan Bernal @jpbernal]

Cualquiera que busque entender cual es el futuro con el que nos vamos a enfrentar debe tomarse 30 minutos para ver esto:

https://t.co/55xxYFtGL6

[Juan Bernal @jpbernal]

Buy Deserving. The Most Comfortable Lie We Tell Ourselves, and What Happens When You Stop Believing It on @Gumroad https://t.co/rXKHVbMWMS

[Juan Bernal @jpbernal]

Ya está entre nosotros un nuevo tipo de consumidor que va a transformar por completo la distribución. Al día de hoy, toda la capa de distribución, desde los sitios web, blogs, podcasts, hasta e-commerce, la hemos diseñado para humanos. Debemos ajustar esa capa para los consumidores más relevantes en el futuro. Los Agentes.

La pregunta es: Si un Agente hace un query a tu servicio como:

GET /api/v1/producto-servicio/47/resumen

Que le retornarías hoy? Que le deberías retornar antes de 12 meses?

[Juan Bernal @jpbernal]

🎯

[Aaron Levie @levie]

Most people have the wrong default assumption for what happens with the need for technical skills in a world of AI agents. The default view is that AI makes most of the skills obsolete.

When in fact, the leverage just went up massively on being good at your particular craft, starting first with software engineering.

It’s far better to have a deep understanding of the hardest part of what you’re trying to do in a task, because you will be able to successfully direct agents to do most of the other rote, undifferentiated work for you.

Writing code is just one component of the job of building software or automating something. There are vastly more steps required for building the right thing, getting it into a production state, and keeping it running. Now coding agents mean the ratio of time you can spend on the more strategic parts vs. less strategic goes up massively.

This pattern will soon be true of most other fields of knowledge work as well. It’s actually the best time ever to be an expert, assuming you’re adopting the tools.

[Juan Bernal @jpbernal]

For anyone that wants to understand what the next 18-24 months will look like 👇

[GREG ISENBERG @gregisenberg]

2026 is the GREATEST time to build a startup in 30 years

I’m 36. I’ve sold 3 startups, helped build companies that raised billions, and backed teams from seed to unicorn.

20 MEGA shifts that make this the BEST time to build in a GENERATION:

1. Hardware got smart. Download open-source AI models from HuggingFace to cheap robots and they're suddenly smart. Opens up tons of use-cases.

2. SaaS is imploding. AI can replicate $500K software for pennies. Enterprise software that took 30 engineers now requires 1 and a Claude Code subscription. Founders will go more niche and more custom and outprice incumbents.

3. Outcome-based pricing is eating subscriptions. With AI agents handling work automatically, founders can guarantee results instead of selling features. This creates a massive arbitrage opportunity to steal market share from rigid subscription models.

4. Vibe marketing is the new marketing. AI agents/tools like Lindy, Gemini and Claude Code Using agents to do personalized outreach, ads and content creation it’s getting good. This is like getting on social in 2005.

5. Social is FYP-ified. Distribution no longer requires massive followings, just content that hits. Founders can build audience from zero without ads and then convert them to owned media channels (text/email).

6. Interfaces are vanishing. Conversations are replacing dashboards across industries. This removes training barriers and means customers can use sophisticated products immediately.

7. Companies are obsessed with efficiency and cutting costs right now. Corporate budgets are getting reallocated to AI. Companies are cutting traditional software spend to make room for AI-powered alternatives. This creates fast-tracked approvals for startups delivering 10x efficiency.

8. 99% of MVPs won't need VC. Low-cost MVPs combined with creator partnerships and AI automation allow bootstrapped scaling. For most software businesses, outside funding is now unnecessary.

9. Global teams. You don’t need to hire in your own city anymore. Opens up tons of arbitrage opportunities and ways to create products unlike before.

10. Millions of creators want to get paid. If you have the right product, the right network of creators, you can hit scale insanely efficiently. Never before did this exist. Next gen founders are building startups community first, software second.

11. Prototyping is nearly instant. With Lovable, Rork etc, you can test ideas in days, not months. MVP speed is basically 1x/week. This creates room for multiple products from small companies (multipreneurship), helps get to PMF faster,

12. LLM APIs create building blocks weekly. I can’t even keep up with how many new APIs/tools coming out from LLMs weekly. Example: Nano Banana pro comes out, probably 1000 ideas built on top of that can be $5M/year businesses.

13. $1m+ revenue per employee. With the leverage of LLMs, community and agents, employees are way more efficient. It won’t be uncommon to generate $1m per employee. This will lead to a rise of "multipreneurship", small teams owning multiple products /businesses. Holding companies will be as common as startups.

14. Superniche is the new niche. Because costs to create software startups is 1/100th, you can service little niches (i call them superniches) and still have a life-changing business.

15. Mobile app ecosystem about to 10X. 2 reasons. First is, adding AI to apps make apps more useful. More useful apps, make more money. Second,

16. Compliance and boring workflows are suddenly buildable. Permits, audits, insurance, payroll edge cases, filings, RFPs. These were “too annoying” for startups before. Agents thrive on rules, checklists, and repetition. The least sexy problems now have the best unit economics.

17. Claude Code killed the “engineering bottleneck.” The constraint is no longer “can we build it,” it’s “do we understand the workflow deeply enough.” The winning founders are ex-operators who encode tribal knowledge into agents. Code is cheap. Taste + domain insight is scarce.

18. The long tail of software is now profitable. Niches that capped at $200k ARR can clear $5M with near-zero marginal cost.

19. Services are quietly becoming software. Manual agencies are one agent away from product margins.

20. if AI can replicate $500K software for $20/month, what’s your moat? distribution, customer service, brand, data etc. REALLY good time to be a world class designer/marketer.

(and even more.... but this is getting long already!)

We've entered the rarest of windows...

when multiple technological shifts collide at once, creating a brief period where small teams can build things that were previously impossible.

THE FUTURE OF BUILDING STARTUPS IS DIFFERENT.

I know this...

This unique moment won't last forever. Markets will adapt. Giants will respond. The window will close.

But right now, a founder with clear vision and bias for action can build more in six months than was previously possible in years.

(note: if you need an idea to get creative juices flowing, grab one at @ideabrowser)

The next generation of great companies is being created right now, many by founders you've never heard of.

Some by people who would never have had a shot in previous cycles.

That's the beauty of these rare windows. The playing field briefly levels, and the future belongs to those who see it clearly and move first.

It's a sacred time, don't bookmark/share this, build something in 2026, will ya?

Happy building, my friends. 2026 is yours.

Am I wrong?

[Juan Bernal @jpbernal]

La IA nos obliga a algo incómodo pero necesario: comunicarnos con precisión. Cuando no somos claros con el sistema, las respuestas no son lo que esperamos. Y ahí se revela algo más profundo: tampoco somos tan precisos con las personas que nos rodean. Esa falla afecta todas nuestras relaciones.

[Juan Bernal @jpbernal]

🎯 We are living this first hand

[Addy Osmani @addyosmani]

Every time we've made it easier to write software, we've ended up writing exponentially more of it.

When high-level languages replaced assembly, programmers didn't write less code - they wrote orders of magnitude more, tackling problems that would have been economically impossible before. When frameworks abstracted away the plumbing, we didn't reduce our output - we built more ambitious applications. When cloud platforms eliminated infrastructure management, we didn't scale back - we spun up services for use cases that never would have justified a server room.

@levie recently articulated why this pattern is about to repeat itself at a scale we haven't seen before, using Jevons Paradox as the frame. The argument resonates because it's playing out in real-time in our developer tools. The initial question everyone asks is "will this replace developers?" but just watch what actually happens. Teams that adopt these tools don't always shrink their engineering headcount - they expand their product surface area. The three-person startup that could only maintain one product now maintains four. The enterprise team that could only experiment with two approaches now tries seven.

The constraint being removed isn't competence but it's the activation energy required to start something new. Think about that internal tool you've been putting off because "it would take someone two weeks and we can't spare anyone"? Now it takes three hours. That refactoring you've been deferring because the risk/reward math didn't work? The math just changed.

This matters because software engineers are uniquely positioned to understand what's coming. We've seen this movie before, just in smaller domains. Every abstraction layer - from assembly to C to Python to frameworks to low-code - followed the same pattern. Each one was supposed to mean we'd need fewer developers. Each one instead enabled us to build more software.

Here's the part that deserves more attention imo: the barrier being lowered isn't just about writing code faster. It's about the types of problems that become economically viable to solve with software. Think about all the internal tools that don't exist at your company. Not because no one thought of them, but because the ROI calculation never cleared the bar. The custom dashboard that would make one team 10% more efficient but would take a week to build. The data pipeline that would unlock insights but requires specialized knowledge. The integration that would smooth a workflow but touches three different systems.

These aren't failing the cost-benefit analysis because the benefit is low - they're failing because the cost is high. Lower that cost by "10x", and suddenly you have an explosion of viable projects. This is exactly what's happening with AI-assisted development, and it's going to be more dramatic than previous transitions because we're making previously "impossible" work possible.

The second-order effects get really interesting when you consider that every new tool creates demand for more tools. When we made it easier to build web applications, we didn't just get more web applications - we got an entire ecosystem of monitoring tools, deployment platforms, debugging tools, and testing frameworks. Each of these spawned their own ecosystems. The compounding effect is nonlinear.

Now apply this logic to every domain where we're lowering the barrier to entry. Every new capability unlocked creates demand for supporting capabilities. Every workflow that becomes tractable creates demand for adjacent workflows. The surface area of what's economically viable expands in all directions.

For engineers specifically, this changes the calculus of what we choose to work on. Right now, we're trained to be incredibly selective about what we build because our time is the scarce resource. But when the cost of building drops dramatically, the limiting factor becomes imagination, "taste" and judgment, not implementation capacity. The skill shifts from "what can I build given my constraints?" to "what should we build given that constraints have in some ways been evaporated?"

The meta-point here is that we keep making the same prediction error. Every time we make something more efficient, we predict it will mean less of that thing. But efficiency improvements don't reduce demand - they reveal latent demand that was previously uneconomic to address. Coal. Computing. Cloud infrastructure. And now, knowledge work.

The pattern is so consistent that the burden of proof should shift. Instead of asking "will AI agents reduce the need for human knowledge workers?" we should be asking "what orders of magnitude increase in knowledge work output are we about to see?"

For software engineers it's the same transition we've navigated successfully several times already. The developers who thrived weren't the ones who resisted higher-level abstractions; they were the ones who used those abstractions to build more ambitious systems. The same logic applies now, just at a larger scale.

The real question is whether we're prepared for a world where the bottleneck shifts from "can we build this?" to "should we build this?" That's a fundamentally different problem space, and it requires fundamentally different skills.

We're about to find out what happens when the cost of knowledge work drops by an order of magnitude. History suggests we (perhaps) won't do less work - we'll discover we've been massively under-investing in knowledge work because it was too expensive to do all the things that were actually worth doing.

The paradox isn't that efficiency creates abundance. The paradox is that we keep being surprised by it.

[Juan Bernal @jpbernal]

@AutomateJohn Hey John. Not yet

[Juan Bernal @jpbernal]

Was about to post a classic “programmer” job opening today and stopped. Claude / Cursor already write code better and faster than any human I know, and they’re improving exponentially. What we actually need now are humans who master system architecture, spec design, validation, integration, and risk management of autonomous coding agents. What should this new title be?

[Juan Bernal @jpbernal]

@tomeromrix @base44 Send pls

[Juan Bernal @jpbernal]

@aditiitwt Continuous innovation is the only thing that has worked for us