Leopoldo Torres Azcona

Estudio IA, poder organizacional y el «futuro» del trabajo • Doctorante en Estudios Científicos Sociales (ITESO) • Fundador de ARISTTX y LET Consulting Partner

[Leopoldo Torres Azcona @leotorresazc]

Y se viene el tuta tuta tuta

[Yahoo Finance @YahooFinance]

🚨 Anthropic could make its IPO filing public as soon as Friday, according to three people familiar with the plans.

The listing could top SpaceX’s June debut as the biggest IPO ever, making it a big test of how much investors still want AI.

Read more: https://t.co/1lVm8VnGJx

[Leopoldo Torres Azcona @leotorresazc]

Algún día el GDP significaba algo, hoy lo único que hace es invisibliza.

[IMF @IMFNews]

The global economy remains resilient, but that resilience is becoming more fragile and more uneven. Growth is projected at 3.1% in 2026 and 3.4% in 2027, while inflation is expected to reach 4.7%, driven largely by higher energy and food prices. Read the IMF's latest analysis: https://t.co/yoil3EB47M

[Leopoldo Torres Azcona @leotorresazc]

Microsoft va por el ecosistema y no el modelo.

[Satya Nadella @satyanadella]

In my annual letter, I share how we’re building a frontier ecosystem to turn technological progress into real-world outcomes and economic opportunity for people, organizations, and communities everywhere. https://t.co/Zcrb96sCC3

[Leopoldo Torres Azcona @leotorresazc]

Buena respuesta de Darío

[Dario Amodei @DarioAmodei]

Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic.

That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it.

I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits:

1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed.

2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it.

3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do.

4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this.

5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future.

6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent.

7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it.

8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company.

9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund.

All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking.

For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea.

Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option.

I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent.

But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole.

To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years.

I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better.

Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so.

Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way.

Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.

[OpenAI @OpenAI]

OpenAI models are coming to Google Cloud.

We're bringing our frontier models and Codex agentic workflows to Google Cloud's Model Garden, so enterprises can use our technology where they already build.

https://t.co/gjuLVALjDm

[Leopoldo Torres Azcona @leotorresazc]

Thats American token spend??? One last thing, remember Chamath: the term is #SI, you were there.

[Chamath Palihapitiya @chamath]

1% of companies represent 90% of AI spending.

Of that 90%, 80% of this spend are from unprofitable VC backed companies.

So, roughly, $0.70-75 of every dollar of AI spending right now comes from AI companies spending VC investment dollars.

As a result, the revenue quality in AI is currently poor. This happened in previous cycles as well, like cloud when early users of AWS were mostly venture backed startups.

The thing, though, is that before AWS could scale and pull thru GCP and others as legitimate cash machines, there were a few clearing events to sort out these brittle revenues.

This is the extrapolation of Ken’s point. We will need to transition from speculative AI spending to consistent, reliable spend that shows up in public market earnings reports.

This hasn’t happened yet because most of the breathtaking revenue growth comes from LPs investing in VC funds who then send it to startups.

[Leopoldo Torres Azcona @leotorresazc]

Una buena noticia

[Sam Altman @sama]

I am very grateful Aleksander accepted our request to come take this on; the safety and security challenges in front of us as we confront superintelligence are immense, and he has an excellent set of skills to work on this.

[Leopoldo Torres Azcona @leotorresazc]

Jaja, nice gemini

[Sundar Pichai @sundarpichai]

Sound on!

[Leopoldo Torres Azcona @leotorresazc]

“Un país puede instalar poder de cómputo en su territorio pero, al mismo tiempo, depender de gobiernos foráneos para mantenimiento y licenciamiento. No podemos asegurar que vivamos en tiempos de paz.” Azeem Azhar

[La Catrina Norteña @catrina_nortena]

🚨 LA CIENTÍFICA DE MÉXICO @Claudiashein da una Cátedra de Ciencia en un minuto

Presenta la construcción de Coatlicue,
que será la Supercomputadora más poderosa de América Latina, con la capacidad de resolver en segundos cálculos que llevarían años en una computadora común; con ella se estudiará la IA, el cambio climático, y la medicina...

[Azeem Azhar @azeem]

Quite the chart.

[Carlota Perez @CarlotaPrzPerez]

Best discussion yet about #AI bubble or no bubble. @pkedrosky @azeem with @ezraklein. Well worth the hour it lasts! Either way, we should be prepared for a better response than in 2008.

[Leopoldo Torres Azcona @leotorresazc]

Me cae mal este tipo, pero neta, se la voló…

[Elon Musk @elonmusk]

Turns out the future does have flying cars

[Satya Nadella @satyanadella]

In @Nature this week, our new research shows how machine learning can improve weather forecasts two to six weeks out, a timeframe that's historically been very difficult to predict.

It's exciting to see the potential of this work to help us better prepare for extreme weather, manage natural resources, and make more informed decisions in agriculture.

There's a broader lesson here... the future may not just be about ever-more-capable models, but about building intelligent verification systems around them: systems that learn where models are systematically wrong, reason about cause and effect, and continuously calibrate predictions against reality.

Read more: https://t.co/WTXJM09474

[Leopoldo Torres Azcona @leotorresazc]

Poco a poco, lentamente… se viene

[Sundar Pichai @sundarpichai]

Some magical progress towards "magic state" in quantum!

Our @GoogleQuantumAI team has demonstrated "magic state cultivation" on physical hardware for the first time. The result is a 40x error reduction on our Willow processor vs. previous methods, achieving a state-of-the-art 99.99% logical fidelity.

It's another step towards useful, error-corrected quantum computers to help solve the hardest problems. The result is published in Science today, watch the video below to see how it works

[Leopoldo Torres Azcona @leotorresazc]

Modelos corriendo en MacMinis… en algunos años nos reiremos de esta imagen por el nivel de desarrollo exponencial hacia esta tecnología

[Mark Zuckerberg @finkd]

Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple.

[Leopoldo Torres Azcona @leotorresazc]

Este evento no se puede olvidar...

[Nayeli Roldán @nayaroldan]

Ufff 😖

[Leopoldo Torres Azcona @leotorresazc]

Si se la volaron

[Scale Labs @ScaleAILabs]

Congrats to @GoogleAI's Gemini 4 Argon for taking the top spot of our Humanity's Sixth Sense (HSS) benchmark with a score of 55.4%, edging out GPT-6-Astra at 53.6%. 🥇

HSS tests AI’s ability to reason beyond what’s visible, from hidden objects to predicting what happens next and understanding human behavior.

We took a closer look at performance across categories, and that’s where things get interesting. 👀🧵

[Leopoldo Torres Azcona @leotorresazc]

@rafauribe parte de lo que platicamos en el podcast.

[MIT Sloan School of Management @MITSloan]

What tasks should AI be trusted with, and when should humans remain involved? Designing those decision rights is one of the defining challenges of the AI era. Researchers at the MIT Center for Information Systems Research have developed a framework to help.

Learn more: https://t.co/NbTi0Vvd7Z

[Leopoldo Torres Azcona @leotorresazc]

Muy bien @GoogleDeepMind. Lo importante a destacar que este modelo de frontera viene con un precio adicional. Cada vez veremos más y más esta estrategia de las compañías sobre s diferenciación de modelos

[Sundar Pichai @sundarpichai]

Gemini 4 Argon is here.

It’s available globally today for developers and enterprises in the paid tier of the Gemini API and the Gemini platform, for Ultra subscribers in Google Antigravity, and Ultra and Pro subscribers in the Gemini app and Search.

Hope you all enjoy it!

[Leopoldo Torres Azcona @leotorresazc]

Me parece pertinente constatar que la adopción es un hecho. El «tipping-point» está sucediendo y con esto los modelos de negocios digitales (o no) tendrán que ajustar su propuesta de valor