La burbuja de la IA

[Cadavre Exquis]

https://x.com/karpathy/status/2056753169888334312

OpenAI Co-Founder Andrej Karpathy Joins Anthropic
Posted by BeauHD on Tuesday May 19, 2026 @04:00PM from the AI-hiring-wars dept.

OpenAI co-founder Andrej Karpathy has joined rival AI lab Anthropic. "The hire is a major coup for Anthropic in the high-stakes competition for elite AI talent -- and another sign the company is emerging as a magnet for some of the industry's most respected technical minds," reports Axios. From the report:
Karpathy will start this week on Anthropic's pre-training team, which is responsible for the massive training runs that give Claude its core knowledge and capabilities, according to Anthropic.

Karpathy will help launch a new team focused on using Claude itself to accelerate pretraining research -- an increasingly important frontier as AI companies race to automate parts of AI development. "I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D," Karpathy said in a post on X.

Karpathy is a rare AI figure with credibility across research, industry and education. He was a founding member of OpenAI before serving as Tesla's director of AI, where he led the computer vision team behind Autopilot. Karpathy coined the term "vibe coding" and recently described himself as being in a "state of AI psychosis" since December -- embracing "tokenmaxxing" and aggressively stress-testing frontier models.

Saludos.

[Benzino Napaloni]

Un estudio lo confirma: la inmensa mayoría de empresas que han despedido empleados y los han reemplazado por la IA no han mejorado sus beneficios https://share.google/AyFZ1RmyrQUAJANR7

Ya hay un nombre para esto: AI Washing.

https://www.linkedin.com/posts/aeejazkhan_a-software-engineer-at-atlassian-was-laid-share-7461016199250919424-zXj_/

A software engineer at Atlassian was laid off in March after 8 years with the company.

His response was a detailed 38-minute YouTube video that reveals how the company’s entire web traffic architecture works, now available for free for anyone to learn from or copy.

His name is Vasilios Syrakis. He worked in Sydney on Atlassian’s “digital plumbing” — the critical system handling web traffic with about 2,000 programs running across 13 regions worldwide.

Every time a user clicks into Atlassian’s products, Syrakis’s system decides which servers respond. Atlassian’s own engineering blog had featured his team’s work as recently as February 2025.

That same quarter, Atlassian reported record revenue of $1.79 billion. Its cloud business grew 29% year over year, with 350,000 customers including 80% of the Fortune 500.

Despite these strong numbers, the company laid off about 10% of its staff. Leadership described the cuts as necessary to “self-fund AI investment.”

In the six months before the layoffs, CEO Mike Cannon-Brookes sold 866,145 shares for roughly $134 million. Co-founder Scott Farquhar sold the exact same number on the same schedule.

The board also approved $2.5 billion in stock buybacks to support the share price. Even so, Atlassian shares have fallen 56% this year.

This pattern has fueled skepticism around “AI washing.” While companies cite AI as a reason for cuts, many layoffs stem from other financial priorities.

Sam Altman recently highlighted this issue. Of the 1.2 million American jobs cut in 2025, only 55,000 were directly attributed to AI.

Now without a paycheck to protect, the engineer who helped build Atlassian’s core infrastructure is openly teaching the world how it works, for free.

https://lesluthiers.org/verversion.php?ID=315

MM: “... y miles de personas pasarán hambre, y algunos gobernantes se enriquecerán a costa de su pueblo”
JM: Perdón, ¿una guerra cada siglo?, ¿nada más?
DR: ¿Miles de personas pasarán hambre?, ¿nada más que miles?
LP: ¿Sólo algunos gobernantes?

TODOS: Quédate niño,
quédate acá...

 


Es decir, de 1,2 millones de despedidos en EEUU el año pasado, sólo 55.000 han sido realmente despedidos por amortización por IA. De las empresas que dicen que despiden por IA el relato es falso (despiden porque hay que pagar la IA, que vale un cojón), o falsísimo (empresas que se están hundiendo pero para disimular dicen que despiden para adoptar la IA).

Esta burbuja va a dejar a la de las puntocom a la altura de una broma.

[muyuu]

https://blog.google/products/ads-commerce/google-marketing-live-search-ads/

Todo ese ladrillo que acaba de publicar Google es para venderte que te empiezan a poner anuncios en las respuestas de la IA. Por supuesto te sacan toda la información posible y comercian con ella, lo admitan o no, y ya pueden investigar y extraer tus archivos a escala como nunca antes.

[Saturio]

La presentación de Google 1/0 fue tremendamente deprimente.
En el vídeo de Youtube han tenido que bloquear comentarios. Hay otro vídeo resumen que tiene los comentarios abiertos (o los tení ayer por la noche).

Es un chorrear de nuevos servicios y servicios sobre servicios todos integradísimos en servicios tutelados por la IA.
Un montón de cosas que nadie ha pedido pero que parece que van a desplazar a las que tenemos y que con sus defectillos y su, ya rampante, enshittificación más o menos funcionaban.
Todo recomendaciones realizadas a través de los datos que ya tienen y los que queriendo o no vas a entregar a la IA.
Como decía algún comentario, enseñaremos a los nietos las fotos de juventud que la IA generará por nosotros (sin que se lo pidamos) sobre experiencias que no hemos tenido con gente con la que nunca hemos estado.
Parece que quieren que nos levantemos de la cama con una agenda repleta de cosas por hacer y comprar que ni siquiera nosotros hemos decidido porque la IA sabe más que nosotros lo que nos conviene.

Lo dicho, deprimente.

[muyuu]

Las compañías que recortan empleos para sustituirlos por IA perderán contra las compañías que no lo están haciendo. : https://libertas.software/en/knowledge-hub/19/the-companies-cutting-headcount-for-ai-will-lose-to-the-ones-who-didnt

There is a version of AI adoption that looks smart on a spreadsheet. Fewer people, lower payroll, same output. It is the version being quietly executed in boardrooms right now, dressed up in language about efficiency and transformation.

It is also the version that will cost those organisations dearly over the next five years.

This is not an argument against AI. It is an argument for using it correctly — and the distinction matters more than most leadership teams currently appreciate.

The Asset They Are Cutting Is the One They Cannot Rebuild

When an organisation downsizes in response to AI capability, the assumption is that the work being removed was the value. That the task itself — the report, the analysis, the email, the data entry — was what the role existed to do.

That assumption is wrong.

The real value sitting inside most teams is not the work they produce. It is the knowledge they carry. How the business actually operates. Where the edge cases live. Why certain decisions get made the way they do. What customers really mean when they complain about a specific issue. The context that never makes it into a process document because it does not need to — because the right person already knows.

That knowledge is institutional. It is built over time. It is extraordinarily difficult to reconstruct once it walks out the door. And right now, organisations are letting it go in exchange for short-term cost reductions, without fully accounting for what they are losing.

AI Does Not Replace Judgement. It Multiplies It.

The organisations that will come out ahead are not the ones who used AI to do the same work with fewer people. They are the ones who used AI to do significantly more work with the same people — or with people who are better positioned to apply their judgement at scale.

This is a fundamentally different operating model. Instead of replacing a team member's output, AI extends their reach. A marketing team that previously managed one campaign at a time can now manage five. An analyst who spent three days on a report can now produce one in a morning and spend the rest of the week on interpretation and strategy. A customer success manager who handled thirty accounts can now meaningfully engage with a hundred.

The human is not removed from the equation. The human is the equation. AI is what makes that equation run faster.

Business Knowledge Is a Competitive Advantage — But Only If You Keep It

There is a compounding effect to institutional knowledge that does not show up in headcount metrics. Experienced teams make better decisions. They catch problems earlier. They understand the business deeply enough to apply new tools — including AI tools — in ways that actually fit the organisation's context.

An AI system is only as useful as the judgement that guides it. A prompt written by someone who deeply understands the customer base, the product, and the operational constraints will produce something categorically more valuable than the same prompt written by a replacement hire working from a brief. Context is not a soft advantage. It is a hard one.

When organisations cut experienced team members in favour of AI-led efficiency, they often discover too late that the AI works considerably better when the people who truly understand the business are the ones directing it.

The Right Question to Be Asking

Rather than asking "where can AI replace people?" the more useful question is: "where can AI give our people back the time they are losing to tasks that do not require their judgement?"

Most organisations have a significant amount of high-skill time absorbed by low-skill work. Administration, formatting, scheduling, basic reporting, first-draft production. These are areas where AI can deliver genuine relief — not by removing roles, but by removing the friction that stops experienced people from operating at their best.

The teams that reclaim that time and redirect it toward the work only they can do — relationship management, strategic thinking, complex problem solving, nuanced decision making — will have a meaningful edge. Not because they have fewer costs. Because they have more capability.

A Sustainable Model Looks Different

Done well, AI adoption should result in teams that are more effective, more focused, and more capable of delivering at a level that was not previously achievable. It should make the knowledge inside an organisation more accessible, not more redundant.

The organisations that understand this will invest in training their teams to work alongside AI tools rather than replacing teams with them. They will treat business knowledge as infrastructure. They will build processes where AI handles the volume and humans handle the depth.

That is not a more cautious version of AI adoption. It is a more ambitious one. Because it is asking AI to do something harder than replacing human output — it is asking it to multiply human potential.

The companies currently cutting headcount to absorb AI costs are making a short-term trade with long-term consequences. The ones holding their teams together and investing in how those teams operate with AI are building something more durable.

The gap between those two approaches will become visible sooner than most expect.