LITIO: Si bien tenemos grandes recursos, aún debemos potenciar nuestra producción de carbonato de litio para apostar al desafió de subir en la cadena de valor. Eso y mucho más lo analiza el especialista @eddiegigante en esta entrevista exclusiva visiondesarrollista.org/edua…

El pueblo y el Carnaval. 💙💛

Recuerdos de aquellos veranos mágicos de Maradona en la playa Marisol: “Siempre le pedía a mi abuela que le hiciera alitas de pollo” | Por Fernando Soriano infobae.com/sociedad/2025/11…

We have the strategic petroleum reserve—we need a strategic mineral reserve!

Goldman Sachs: “China has already dominated the robot supply chain: gearing up for mass production in the second half of 2026 — securing annual production capacity of 100,000–1,000,000 units” • According to a recent survey by Goldman Sachs of major Chinese robot component makers such as Sanhua (三花), the industry views the second half of 2026 as the time when large-scale mass production of “humanoid robots” will begin in earnest. This is presented as a key benchmark for gauging investment timing across the robot industry value chain. 1. A clear timeline: It expects the inflection point for large-scale mass production across the supply chain to be the second half of 2026, which will serve as an important temporal anchor point for investment in the robot industry. 2. Coexistence of confidence and risk: Component and module suppliers are adopting an “invest first, demand later” strategy through real-economy investments such as securing land, expanding factories, and building production lines. This both reflects strong confidence in a surge of orders from key OEMs such as Tesla and entails the risk of overcapacity in production facilities if demand falls short. 3. Reconfiguration of the value chain: Suppliers are shifting from simple component sales to a focus on “module/system integration.” In particular, companies that provide integrated actuator/sensor solutions and possess global production capacity are expected to move up the value chain and gain bargaining power. 4. Points to watch: Goldman Sachs presents two key events over the next 1–2 years to confirm the industry’s turning point. (1) Tesla Optimus, 3rd generation: scheduled to be unveiled in February–March 2026, (2) The 2026 shipment/order targets of global robot companies: expected to be announced in late 2025 to early 2026. These two will be the critical litmus test for whether the optimistic outlook translates into actual orders. In summary, Goldman Sachs’ report describes China’s humanoid robot supply chain as “everything is ready except for the very last step.”

Llegué a Viena con expectativas altas. Lógico para cualquier persona que estudia las ciudades y sus problemas habitacionales. La capital de Austria parece una utopía hecha realidad, un modelo a seguir. Ahí nadie quiere ser dueño de una vivienda. Les cuento por qué. 🧵

La DAIA, a través de su Secretaría de Lucha contra el Antisemitismo en el Deporte y su Centro de Estudios Sociales, se reunió ayer con autoridades del club San Fernando para evaluar la acción de un socio adulto de dicha entidad, que vistió una remera con la inscripción "SAVE GAZA" en el tercer tiempo luego de un partido de jockey entre ese club y Macabi. El encuentro fue altamente positivo ya que las autoridades de San Fernando dejaron en claro que no avalan ninguna clase de antisemitismo ni discriminación y que su Comisión Direcitva se encuentra estudiando las medidas a tomar con el socio. Se acordó producir una agenda de trabajo entre San Fernando y la DAIA consistente en brindar capacitaciones a los profesionales deportivos y jugadores, además de generar visitas al Museo del Holocausto. Se continuará con esta línea de trabajo a fin evitar el antisemitismo en el deporte.

🚨🇨🇳China unveils first-ever thorium-powered cargo ship This cargo vessel powered by a thorium molten salt reactor, capable of carrying 14,000 containers, South China Morning Post reports.

I would like to clarify a few things. First, the obvious one: we do not have or want government guarantees for OpenAI datacenters. We believe that governments should not pick winners or losers, and that taxpayers should not bail out companies that make bad business decisions or otherwise lose in the market. If one company fails, other companies will do good work. What we do think might make sense is governments building (and owning) their own AI infrastructure, but then the upside of that should flow to the government as well. We can imagine a world where governments decide to offtake a lot of computing power and get to decide how to use it, and it may make sense to provide lower cost of capital to do so. Building a strategic national reserve of computing power makes a lot of sense. But this should be for the government’s benefit, not the benefit of private companies. The one area where we have discussed loan guarantees is as part of supporting the buildout of semiconductor fabs in the US, where we and other companies have responded to the government’s call and where we would be happy to help (though we did not formally apply). The basic idea there has been ensuring that the sourcing of the chip supply chain is as American as possible in order to bring jobs and industrialization back to the US, and to enhance the strategic position of the US with an independent supply chain, for the benefit of all American companies. This is of course different from governments guaranteeing private-benefit datacenter buildouts. There are at least 3 “questions behind the question” here that are understandably causing concern. First, “How is OpenAI going to pay for all this infrastructure it is signing up for?” We expect to end this year above $20 billion in annualized revenue run rate and grow to hundreds of billion by 2030. We are looking at commitments of about $1.4 trillion over the next 8 years. Obviously this requires continued revenue growth, and each doubling is a lot of work! But we are feeling good about our prospects there; we are quite excited about our upcoming enterprise offering for example, and there are categories like new consumer devices and robotics that we also expect to be very significant. But there are also new categories we have a hard time putting specifics on like AI that can do scientific discovery, which we will touch on later. We are also looking at ways to more directly sell compute capacity to other companies (and people); we are pretty sure the world is going to need a lot of “AI cloud”, and we are excited to offer this. We may also raise more equity or debt capital in the future. But everything we currently see suggests that the world is going to need a great deal more computing power than what we are already planning for. Second, “Is OpenAI trying to become too big to fail, and should the government pick winners and losers?” Our answer on this is an unequivocal no. If we screw up and can’t fix it, we should fail, and other companies will continue on doing good work and servicing customers. That’s how capitalism works and the ecosystem and economy would be fine. We plan to be a wildly successful company, but if we get it wrong, that’s on us. Our CFO talked about government financing yesterday, and then later clarified her point underscoring that she could have phrased things more clearly. As mentioned above, we think that the US government should have a national strategy for its own AI infrastructure. Tyler Cowen asked me a few weeks ago about the federal government becoming the insurer of last resort for AI, in the sense of risks (like nuclear power) not about overbuild. I said “I do think the government ends up as the insurer of last resort, but I think I mean that in a different way than you mean that, and I don’t expect them to actually be writing the policies in the way that maybe they do for nuclear”. Again, this was in a totally different context than datacenter buildout, and not about bailing out a company. What we were talking about is something going catastrophically wrong—say, a rogue actor using an AI to coordinate a large-scale cyberattack that disrupts critical infrastructure—and how intentional misuse of AI could cause harm at a scale that only the government could deal with. I do not think the government should be writing insurance policies for AI companies. Third, “Why do you need to spend so much now, instead of growing more slowly?”. We are trying to build the infrastructure for a future economy powered by AI, and given everything we see on the horizon in our research program, this is the time to invest to be really scaling up our technology. Massive infrastructure projects take quite awhile to build, so we have to start now. Based on the trends we are seeing of how people are using AI and how much of it they would like to use, we believe the risk to OpenAI of not having enough computing power is more significant and more likely than the risk of having too much. Even today, we and others have to rate limit our products and not offer new features and models because we face such a severe compute constraint. In a world where AI can make important scientific breakthroughs but at the cost of tremendous amounts of computing power, we want to be ready to meet that moment. And we no longer think it’s in the distant future. Our mission requires us to do what we can to not wait many more years to apply AI to hard problems, like contributing to curing deadly diseases, and to bring the benefits of AGI to people as soon as possible. Also, we want a world of abundant and cheap AI. We expect massive demand for this technology, and for it to improve people’s lives in many ways. It is a great privilege to get to be in the arena, and to have the conviction to take a run at building infrastructure at such scale for something so important. This is the bet we are making, and given our vantage point, we feel good about it. But we of course could be wrong, and the market—not the government—will deal with it if we are.

The 2025 List adds 10 NEW minerals: • Copper • Uranium • Silver • Boron • Potash • Phosphate • Lead • Rhenium • Silicon • Metallurgical coal These additions reflect updated science, public input and interagency collaboration.

The oil cost curve There’s a famous (ish) chart in oil and gas called the cost curve, it’s a staple of petroleum economics and is fundamental to understanding global energy markets but seems to be lost on the wider technology community. Crucially for all commodities, including energy, price and cost are two very different things. You will see a lot of people compare the price of one thing (eg gas) to the cost of another (eg solar). This is fatally flawed and leads to all sorts of poor reasoning and wildly flawed assumptions about the world and the future. The oil cost curve is the best way to explain this. We are all familiar with the price of oil. It is quoted on Bloomberg, we all recognise WTI or Brent as the price of a barrel of oil. That is the price of oil if you (a buyer) want to buy a barrel of oil right now, today. It’s the spot price. The cost is a very different thing. The cost is amount of money it costs someone who has an oil resource to get that oil from below ground to the surface, and onto an oil tanker. The cost varies wildly around the world for a multitude of reasons and the oil cost curve illustrates how some regions have far lower costs than other regions. All whilst the price is the same shared commodity price you see on CNBC. For example, Saudi Arabia produces about 10m barrels a year at a cost of $25 / barrel, then then sell this oil for a price of $60 they make $35 on every barrel and when you make $35 x 10m every day, you quickly start banking $100’s of billions a year and you soon have trillions of dollars of liquid capital reserves. This is why the world has multiple petrostates, but not one coalstate, or lithiumstate. Likewise if you wanted to get Saudi crude oil off the world energy market you don’t need an energy source that is cheaper than WTI, you need an energy source that is cheaper than $25 / barrel and you need to be able to maintain that when half a dozen trillionaire petrostates are prepared to do whatever it takes to stop you. The oil cost curve is what all commodity curves look like at the scarcity side of the bath tub. It’s just that the entire global economy is still priced off the marginal barrel of oil, because oil is the largest value, commodity durable in the global economy and demand is quite inelastic (all countries always need it). Petroleum economics are important part of understanding global energy market dynamics, it dominates pricing, behaviours and international capital flows.

Su majestad 👑