Paris
Joined March 2009
Google just open-sourced Harness². (turn any harness into a self-improving system) a harness is everything wrapped around the model, like the system prompt, tools, memory, skills, and subagents. today a person designs it once, and the agent runs every new task with that same fixed setup. Harness² lets the agent refine that setup for each new task on its own, with the model weights left untouched. on every task, the agent proposes edits to its harness, runs a few candidate versions, and composes a better harness by comparing how those runs behaved. because it learns from the differences between runs, it needs no correct answer or grader for the task. that covers a single task. Harness² also carries what it learns from one task into the next. after each task, it saves the harness it settled on along with the lessons that held up across the runs. the next time it refines a harness, it starts from those notes instead of from scratch. this means every finished task makes the next round of edits more informed. Google tested this with Gemini 3.5 Flash and GLM-5 on legal, office, and professional work benchmarks. one round of edits was enough to beat their original harnesses, and giving the agent more rounds pushed scores higher. those extra rounds can go wide or deep. the agent either tries several candidate harnesses at once or keeps refining a single one step by step, using OpenCode or Codex to carry out the work. you can find the Harness² repo here: github.com/google-research/h… I wrote about a similar idea for agents already running in production, where the harness should repair itself after a failure instead of waiting for someone to debug the trace by hand. that piece walks through how @Cometml's Opik (open-source) closes this loop, going from a failing trace to a proposed fix to a regression test that stops the same failure from coming back. it's quoted below.
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Maintenant, il faut bien comprendre que lorsqu'on écrit c'est pour des #humains et pour des #machines, et que les machines sont plus utilisées par d'autres machines que par les humains. Il va falloir apprendre à vivre de manière #symbiotique #AI
AI agents are becoming the main user of company software, with humans approving its actions instead of entering data themselves. So it should be metered accordingly. ERP[.]AI has released a platform for business software that charges $0 per seat and bills the machines instead. Seats, apps, roles, permissions and manual record work in its interface cost nothing, while API-key access, imports, workflows, agents and AI draw down prepaid credits. Those credits come in one-time $5, $20 and $100 packs, and every new account gets $100 of starting credit that lasts 1 month. The meter is fine-grained, because $1 buys 4,000 record writes, 2,000 workflow runs or 2,000 agent executions, and AI is billed at the model’s exact cost plus an 8% agent fee. Most of that metered work comes from Proto, a desktop agent that sets up data, approvals, permissions and dashboards in ERP[.]AI from a plain description of how a business runs.
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@benavent retweeted
AI agents are becoming the main user of company software, with humans approving its actions instead of entering data themselves. So it should be metered accordingly. ERP[.]AI has released a platform for business software that charges $0 per seat and bills the machines instead. Seats, apps, roles, permissions and manual record work in its interface cost nothing, while API-key access, imports, workflows, agents and AI draw down prepaid credits. Those credits come in one-time $5, $20 and $100 packs, and every new account gets $100 of starting credit that lasts 1 month. The meter is fine-grained, because $1 buys 4,000 record writes, 2,000 workflow runs or 2,000 agent executions, and AI is billed at the model’s exact cost plus an 8% agent fee. Most of that metered work comes from Proto, a desktop agent that sets up data, approvals, permissions and dashboards in ERP[.]AI from a plain description of how a business runs.
🤝 Paid partnership
For 25 years, Salesforce, SAP and enterprise software were built around one assumption: humans operate the software. We’re changing that. Introducing ERP•AI and Proto: business software built for agents, and an agent that operates business software. Describe the system you need. ERP•AI builds it. Then Proto takes over. It manages records, runs workflows and executes actions inside the software you just created. This is Headless SaaS. 1: get ERP•AI 2: build your SaaS 3: post below Best one wins a MacBook. First 100 decent entries get $100. (a message from Steve Papa / founding team)
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Today @GoogleResearch is launching Deep Review — an agentic system designed to stress-test manuscripts, design docs, and complex proposals before submission or publication. After pilot partnerships analyzing over 15,000 research papers, we’re making Deep Review globally accessible to help researchers, engineers, and professionals bring structured critique to their daily workflows.
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Again twenty years later #VRM vs #CRM , that's an #utopy
There is a huge opportunity for specialized personal agents. Here is a great example: @OdessiaTravel just launched one for travel and experiences. You text it what you want, and it plans and books the trip, the table, or the tickets. It also keeps watching after you ask. You can set daily watches on tables, shows, and fares, and it pings you when something opens up. The thing is, like specialized and general models, I think we will see the same for personal agents. Love seeing more of these pop up recently.
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@benavent retweeted
There is a huge opportunity for specialized personal agents. Here is a great example: @OdessiaTravel just launched one for travel and experiences. You text it what you want, and it plans and books the trip, the table, or the tickets. It also keeps watching after you ask. You can set daily watches on tables, shows, and fares, and it pings you when something opens up. The thing is, like specialized and general models, I think we will see the same for personal agents. Love seeing more of these pop up recently.
🤝 Paid partnership
We’re months away from AGI and the leisure economy’s about to explode Today, we’re launching Odessia, an always-on concierge for travel and experiences Prices are spiking, places are getting crowded. Odessia is your cheat code to going places Text to get started for free
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🧵1/10 Given the recent reactions of mathematicians over rapid advances in AI, I think it is important to remember that technological change has historically been a very personal event for people. Over the history of technological progress, looms have fallen silent, switchboards have gone dark, and factory lines have shrunk bringing with them a human crisis of identity and what it means to be a master of one's craft.
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Extreme poverty ending 👇
China reduced extreme poverty fast, but Indonesia hasn’t been far behind. China is often the poster child for rapid reductions in poverty, and for good reason. In the early 1980s, over 90% of its population lived in extreme poverty, but by the early 2020s, that number had dropped to nearly zero. Some might assume that China is the only reason why global extreme poverty has declined. But that’s wrong: many countries have seen dramatic reductions in poverty, too. Indonesia is one clear example; it’s shown alongside China on the chart. In 1984, 86% of Indonesians lived on less than $3 per day. By 2025, this had fallen to less than 4%. While it didn’t quite match China’s decline, it has still been impressive. The number of people living in extreme poverty has fallen from 140 million to about 10 million. Note that the international poverty line is extremely low, defined as people living on less than $3 per day. But Indonesia has also made progress when measuring higher poverty lines. (written by @_HannahRitchie and @parriagadap)
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I've had so many insightful conversations over the years with @lessig, an expert on AI regulation and legislation, including more recent ones on the cyber incidents of this past summer. Happy to share our thoughts in this new @TheEconomist op-ed, where we outline concrete actions Congress could consider to protect the public from AI’s major risks: economist.com/by-invitation/…
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@benavent retweeted
Political scientists and economists: we're looking for researchers with deep expertise to come work with us for a unique 4-month fellowship at @AnthropicAI. You'll work closely with us to evaluate, improve, and apply Claude's capabilities in your field. It's going to be extremely fun and interesting work, and definitely worth taking a short leave from your university for if you need to! More info here: job-boards.greenhouse.io/ant…
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@benavent retweeted
Agent swarms send most new token volume to cheaper models, so agent token counts will outrun frontier-model revenue. In a swarm, a parent agent plans while child agents handle narrow jobs like searching files or running tests. Narrow jobs are usually easy to check, so developers can route them to the cheapest model that passes. And this massive token-explosion is also the major reason, flat-rate and per-seat pricing for AI software will give way to metering, because it relied on human prompting speed to cap usage. A person sends only so many prompts a day, so vendors could charge per user and still predict inference bills. But that equation will not hold with agent swarms working 24x7.
Your next users are machines Agents burn nearly 5x the tokens people do, up 14x in six months
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C'est fascinant. Apprendre à partir de jeux vidéo, affiner l'apprentissage dans le monde physique par du renforcement. C'est plus que de l'apprentissage, mais un véritable programme d'#éducation des machines des machines. #AI #humanoid
Can humanoids perform agile, autonomous, long-horizon parkour—based on what they see in the world? We present 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝘃𝗲 𝗛𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗣𝗮𝗿𝗸𝗼𝘂𝗿 (𝗣𝗛𝗣): a framework that chains dynamic human skills using onboard depth perception for long-horizon traversal. 1/6
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Seventh and last question on AI. Can AI really help us create as many jobs as it will replace? The AI industry offers two narratives at once. The first emphasizes that because of AI’s capabilities, there will be a lot of job loss, and soon. Anthropic’s co-founder and CEO Dario Amodei is particularly vocal on this: cnbc.com/2026/01/27/dario-am… The second claims that AI will also enable lots of new jobs, so we should not fear joblessness, higher unemployment, and humans being sidelined (see, for example, Jensen Huang, fortune.com/2026/09/01/nvidi… or Marc Andreessen, digitaltoday.co.kr/en/view/4…). A lot hinges on this question. Sizable job displacement is costly for workers and could destabilize our already ailing democracy. Displacements from robots and cheap Chinese imports in the 1990s and 2000s in the United States, which were smaller than the projected job losses from AI, contributed significantly to declining trust in government and institutions (see my book What Happened to Liberal Democracy, penguinrandomhouse.com/books…, on this). Our democracy may not be able to survive another massive job dislocation. I am worried about job loss, for two fundamental reasons. AI is currently being developed as an automation technology and in addition, I do not find the arguments about large numbers of jobs being created by AI particularly realistic. (I’ll come back below to how AI could create jobs if it weren’t developed as an automation technology). Proponents of the job creation side fall back on four general arguments, none of which is convincing in my assessment. First, new technologies have always created lots of jobs. True, but that does not guarantee that this time will be the same. And more importantly, this account neglects the richer details about different historical episodes. In a nutshell, automation technologies by themselves need not create jobs (though they can, depending on circumstances and how productive they are). They involve capital substituting for tasks previously performed by workers. The way they would create jobs is that the displaced workers would go and take non-automated jobs in other parts of the economy. This can happen, but it also depresses wages (or at the very least the labor share), and consequently, some of the workers may be discouraged from looking for work or may not have the skills that remaining non-automated tasks require. This, in any case, is what task-based models predict. See, for example: aeaweb.org/articles?id=10.12… and onlinelibrary.wiley.com/doi/…. The empirical evidence also casts doubt on plentiful job creation taking place automatically. For example, the introduction of robots led to many fewer jobs in local economies that depended on blue-collar jobs in robot-heavy industries (and this effect was quantitatively large; one more robot per thousand industrial workers reduced the employment population ratio in the local economy by about 2 percentage points and local wages by 0.42% between 1990 and 2007). See journals.uchicago.edu/doi/fu…. Second, AI will increase productivity and higher productivity will lead to more jobs. But this argument suffers from the same weakness as the previous one: higher productivity will trigger job creation only in remaining non-automated tasks (unless there are new tasks, which I discuss below). If wages are depressed and workers don’t have the skills for these non-automated tasks, even with higher productivity, there will not be AI-driven job creation. Third, AI will need a lot of supporting roles, such as people to train the AI models. This one is the weakest argument of all. If AI is indeed an automation technology, then the whole point is to save labor costs. By the same logic, the labor needed to train AI must be much less than the labor AI ultimately saves. For this reason, I see essentially no chance that there training and maintaining AI will create anywhere near as many jobs as AI replaces. Fourth, AI is already complementing workers. This argument is based on some evidence that many workers are using AI in their jobs – see, for example, thefp.com/p/ai-jobs-unemploy…. However, the mere fact that workers are using AI in their jobs doesn’t establish that new jobs will be plentiful. The theory here is again quite clear (see the detailed treatment of this issue here, nber.org/papers/w32872). If AI simply makes workers more productive in the tasks that they used to perform already, it will drive down task prices. If you are the only worker using AI, this will benefit you. If all workers like you start using AI, then the skills on which you depend become commodified. This is what happened, for example, to London black cab drivers, whose highly-valuable knowledge of London streets was commodified when GPS technology enabled minicabs, and then later Uber drivers, to perform the same tasks as effectively. To put it simply, it isn’t enough for AI to make workers more productive at the tasks that they are performing already. This brings me to how AI could create a lot of jobs. Again, the task-based framework provides the answer here: new technologies create jobs, and in fact well-paying jobs, if they enable new tasks (either in the same industry or in new industries). I have argued elsewhere that AI has this potential as a powerful information technology. See, for example, brookings.edu/articles/build…. But this isn’t currently the direction in which AI is being developed. Hence, on its current trajectory I remain quite concerned about future job losses (and wage declines) from AI. Of course, things may turn out differently. But if they do, it will be because AI becomes more pro-worker. I see this as unlikely, but it is useful to still consider the scenarios that would bring such an outcome. These are: (1) current AI technologies may become turn out pro-worker than I am anticipating; (2) in the future the industry will automatically and by itself change and focus on pro-worker applications; (3) based on AI-driven science and research, new sectors will be created where there will be need for new workers. I’m not optimistic about any of these possibilities, and therefore my conclusion is that AI is likely to lead to more jobs being lost than gained. (But I do think there are policies that would encourage pro-worker AI; see, for example, nature.com/articles/d41586-0…). That being said, I don’t think the job loss will happen very quickly, because as I discussed in an earlier post (third question), the diffusion of AI is relatively slow. But this also implies the productivity gains from AI will be slow in coming.
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@benavent retweeted
please stop asking me wen @osseneth to CreeptoPunks I would like to know myself...
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Post-training / RL and inference is taking over compute
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Elon Musk vient de donner une définition assez radicale de la souveraineté industrielle. Terafab, sa future usine géante de puces au Texas, sera construite et exploitée par ses propres entreprises. TSMC, numéro un mondial du secteur, discutait d'en prendre les commandes. Ce ne sera pas le cas. Intel reste associé au projet. Le raisonnement est simple : ses entreprises ne trouvaient pas assez de puces pour l'IA, la robotique et le spatial. Si le marché ne garantit pas la capacité, il la construit. Lanceurs, satellites, IA, voitures, robots, et maintenant semi-conducteurs. Maillon après maillon, il retire au marché ce dont il dépend pour le faire entrer dans son propre système. Quelques jours plus tôt, on apprenait qu'il cessait de vendre des vols Falcon 9 au-delà de 2028. L'Europe parle beaucoup de souveraineté et d'autonomie stratégique. Musk en applique une version brutale : ce dont je dépends, je le contrôle. Et il le fait en coopérant. Il discute avec TSMC, travaille avec Intel, mais garde l'exploitation. Coopérer sans céder la maîtrise industrielle : c'est ce que l'Europe doit apprendre, d'Airbus aux lanceurs en passant par les licences américaines.
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Today, we are launching a preview of our new model, Mistral Large 4 (ML4), aka le Chonk 🐈. ML4 is a 1T-parameter model with 49B active parameters, trained natively with multimodal capabilities. It is at the frontier of open models, and by far the strongest open-weight model from the US or Europe. The RL run behind this preview is still in flight and shows no sign of saturation -- we will release a final version before the end of the month along with the weights of the model. 🧵 1/n
Meet Mistral Large 4, aka Le Chonk. • 1T parameters, natively multimodal. 49B active. It is the best open weights model from US or Europe on aggregated benchmarks. • State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding. • Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure. • Available to all via API today. Working with cybersecurity partners privately. Open weights release end of October.
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This is why GPUs cost $6,000
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