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Sam Altman (CEO de OpenAI): «Ya no necesitas escribir prompts.» En solo 38 minutos explica cómo usar ChatGPT a un nivel que la mayoría de la gente ni se imagina. Es una charla que dio a estudiantes de Stanford. Un amigo me pasó la grabación anoche. Después de verla me di cuenta de que estaba aprovechando apenas el 15 % de lo que esta herramienta realmente puede hacer. Mírala completa y después lee la guía que dejo abajo sobre cómo crear un sistema que se promptea a sí mismo.

The CEOs building the most powerful technology in human history are asking to be regulated. Demis Hassabis called for AI FINRA. Sam Altman proposed international governance to the G7. Dario Amodei co-signed. The same week, Mira Murati shipped her first model: 975 billion parameters, open-weight. -- Liquid AI's post-transformer architecture runs on everything from smartphones to F-35s. Deployed on 1M+ devices. -- The White House is pegging America's open release ceiling to China's best model, giving Beijing control over what the US can ship. -- Malaysia's PM launching an AI clone of himself to govern in 135 languages. -- Chinese open-weight models trail the US by 7 months.

Bryce Dallas Howard has shared a video of the late Sam Neill singing “Blackbird” with Jeff Goldblum and Laura Dern. “Sam had an immense passion for life. Generous with his wine, his stories, his time. One of the most unassumingly attractive and magnetic people I've ever met.”

Sam Altman just got sued by Apple. And that's not even his biggest problem this year!! His own co-founder, Elon Musk, spent two years trying to prove in court that Altman betrayed him. 😯 Now Apple says OpenAI stole its secrets too. Two billion-dollar wars. One CEO in the middle. 🤨 Back in 2015, Musk and Altman built OpenAI as a nonprofit, a lab to build AI safely before Google could lock it down. Musk put in $38 million. Altman turned OpenAI into a for-profit company. Apple's claim: OpenAI stole its secrets to build it. OpenAI's hardware boss, Tang Tan, a former Apple VP, allegedly told job candidates still at Apple to bring "actual parts," like batteries and logic boards, for "show and tell." Is his empire standing on stolen ground? 🤔 Tell us what you think, and subscribe @TruthTrumpPost for more.

A few thoughts on the very near future First of all, what had previously been little more than a rumor has now been confirmed: GPT-5.6 had already been fully trained for two months and was available to selected users in early access. The obvious question is why it was not rolled out earlier. I do not think this was because OpenAI feared that the model might be overshadowed by Fable 5 or Mythos 5. Instead, OpenAI likely began working with government and regulatory authorities at a very early stage to ensure that the model could be released at all. Even after it had been previewed and announced, it still took some time before it could be rolled out publicly. That said, OpenAI clearly handled the rollout far better than Anthropic, which apparently did not have the same level of cooperation with government and regulatory authorities. Conversely, however, this also clearly means that future delays and increasingly strict model reviews will probably force us to wait longer for official releases. The next widely discussed rumor is that, within a few weeks, most likely no more than six, we will see either a preview or even the release of GPT-6. (Andrew Curran @AndrewCurran_ is one of the most reliable sources here on X, so I think that's very realistic.) The model has undergone entirely new pretraining, and the pace of releases is accelerating. The numbers are clear: Frontier labs are releasing more and better models at an increasingly rapid pace. Whereas we once had to wait months, quarters, or even half a year for major new releases, they are now arriving almost weekly. The latest frontier models may be more efficient in terms of intelligence per token, but they are also being deployed with much larger reasoning budgets. In practice, models such as Fable 5 and GPT-5.6 often consume considerably more tokens during complex or agentic tasks. This is not necessarily a sign of declining efficiency. Rather, it suggests that improvements in efficiency are being reinvested into deeper reasoning, longer trajectories and more capable agentic behavior. The result is that total compute consumption per task can continue to rise even as the underlying models become more efficient. Fable 5 and GPT 5.6 demonstrate just how intensive token usage has become. Although Sam Altman explicitly stated that GPT-5.6 is 54% more token-efficient (via CNBC), the fact remains that compute demand continues to increase, requiring more powerful and efficient computing infrastructure. Inference chips will probably become even more important as well. In summary, my initial conclusion from the latest releases is that compute demand will not merely continue to grow, but will probably exceed the available supply. This naturally means that energy demand will also increase, and, based on my initial assessment, probably more sharply than previously expected. This is likely to remain the largest bottleneck in the very near future. And this is important to me: there are bottlenecks. Not the training of the models, but besides compute, above all energy. This needs to be taken seriously! The US power grid, for example, is a major bottleneck, and the obvious question is how the necessary expansion can be achieved. Capital expenditure on data centers in the United States continues to rise sharply. This year, it exceeds 800 billion. It is not yet clear what the situation will look like in 2027, but I can hardly imagine investment declining or less CapEx being required. The reason lies precisely in the developments already mentioned: Demand is growing, particularly demand for energy. China clearly has an advantage here, a genuine moat, and I believe the West must be extremely careful not to fall behind because of the energy advantage China already possesses in practice. This could also help explain why, according to a recent Reuters report, China is considering restricting Western access to its frontier models. It may have concluded that it will win the long-term race. Unless there is a genuine breakthrough, whether in small modular nuclear reactors or fusion energy, I expect major problems to emerge over the coming years, for example by 2030. So far, I do not see any viable solutions. We can therefore clearly establish two points: Models are becoming larger, better, and increasingly useful for all users. There is no end to this development in sight. At the same time, the bottleneck appears to be growing increasingly severe, and this is already visible in practice. Regulation, energy demand, and compute demand could mean that, in the very near future, the release cadence will not accelerate as quickly as hoped or desired. This creates a clear contradiction. Thank you for coming to my TED Talk.