Antoine de Possesse

Passionate about #startup #openinnovation #entrepreneurship #VC #digital @Orange #HECParis #Babson Views are mine alone.

[Anthropic @AnthropicAI]

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor.

It’s happening faster than we thought, and the implications deserve greater attention. https://t.co/OVVPJO7VQx

[Mira Murati @miramurati]

Today we're sharing our work on interaction models. A new class of model trained from scratch to handle real-time interaction natively, instead of gluing it onto a turn-based one.
https://t.co/MoS5s4cm60

[Eric Schmidt @ericschmidt]

This is a major advancement in robotic reasoning and control. See the video !

[Y Combinator @ycombinator]

Startup School is coming to Paris! 🇫🇷

Hear from founders like @lxbrun of @amilabs, @oliveur of @datadoghq, @kiwicopple of @supabase, @james406 of @posthog, and more.

And join the best builders and hackers from across France and Europe for a day of talks and sessions with YC partners.

[NoLimit @NoLimitGains]

🚨 OpenAI is reportedly building a phone designed to replace the iPhone.

And it’s further along than anyone realized.

Analyst Ming-Chi Kuo, the same man who predicted every major Apple product cycle for 20 years, just dropped this.

Important details:

1: OpenAI is partnering with Qualcomm AND MediaTek to develop custom smartphone processors, not one chip partner, but two competing giants simultaneously

2: Luxshare has been named the exclusive system co-design and manufacturing partner, the same company that assembles Apple products

3: Mass production is targeted for 2028, the hardware roadmap is already in motion

4: The phone will run OpenAI’s own OS, replacing traditional apps entirely with AI agents that complete tasks autonomously, without you ever opening a single app

5: The processor is being designed around on-device AI performance, with complex tasks offloaded to OpenAI’s cloud infrastructure for seamless integration

6: OpenAI’s core thesis: users don’t want apps, they want results. The phone will continuously understand context, habits, and preferences in real time

This isn’t a gadget. It’s a direct attempt to replace the operating system layer that Apple and Google have owned for 20 years.

I’m doing more research, and what I’m about to post will blow your mind.

You’ll wish you followed me sooner, trust me.

[Y Combinator @ycombinator]

AI has stopped being a feature and started being the foundation.

We're excited about a new wave of startups rebuilding software, services, and silicon— and pushing AI into the physical world.

https://t.co/QCIz6DnQnN

[Brivael Le Pogam @brivael]

Quand j'étais en études il y a une quinzaine d'années, je passais mes soirées à débattre avec mes potes de promo d'une thèse qui leur paraissait absurde : il faut tout apprendre.

Pas par curiosité gentille de touche-à-tout. Par calcul stratégique froid.

Leur contre-argument était toujours le même : "tu vas te disperser, t'arriveras à rien, le monde appartient aux experts qui creusent profond". L'injonction culturelle de l'époque c'était la spécialisation. Les écoles te formataient pour ça, le marché du travail te récompensait pour ça, les parents te répétaient ça.

Je pensais l'inverse, et 15 ans plus tard je le pense encore plus fort.

La logique est purement économique. Un bon économiste est en compétition avec tous les autres bons économistes. Le marché de son expertise est saturé, les prix s'écrasent, sa rente disparaît. C'est la loi de l'offre et de la demande appliquée au capital humain.

Maintenant prends quelqu'un qui est bon en éco ET en entrepreneuriat ET en archi logicielle. La taille de son marché concurrentiel ne s'additionne pas, elle se multiplie à l'envers, elle s'effondre. Il n'est plus en compétition avec personne, parce que l'intersection des trois compétences est quasi vide. Il a construit ce que j'appelle un monopole de compétences.

C'est exactement la même mécanique que le positionnement d'une boîte. Tu peux être la 50e plateforme SaaS de gestion de projet et te battre dans un océan rouge, ou tu peux être la seule plateforme SaaS de gestion de projet pour vétérinaires équins en Argentine et avoir un monopole local. La différenciation crée la rente.

Le capital humain obéit à la même loi.

À l'ère de l'IA, cette logique ne devient pas juste vraie, elle devient critique.

Parce que l'IA fait quelque chose de très précis aux marchés de l'expertise : elle les commoditise. L'expertise verticale, profonde, technique, vérifiable, c'est exactement ce qu'un LLM fait le mieux. Tu peux louer un PhD synthétique pour 20$/mois qui te sortira en 30 secondes ce qu'un consultant senior te facturait 3000€ la journée. Le prix de l'expertise pure tend vers son coût marginal de production, qui tend vers zéro.

Ce qui ne se commoditise pas, c'est la capacité à connecter des domaines que personne ne pense à connecter.

Parce qu'un LLM n'a pas de goût. Il n'a pas d'intuition sur quels deux champs frotter ensemble pour faire émerger une idée nouvelle. Il n'a pas la trajectoire personnelle qui fait que toi, et toi seul, vois le pont entre la théorie mimétique de Girard et le design d'un produit social. C'est un acte de création, pas de récupération.

Quelqu'un qui maîtrise l'archi logicielle, la philo, l'éco, et qui en plus a du goût en musique, cette mayonnaise produit des choses que ni un dev pur, ni un philosophe pur, ni un économiste pur ne pourront jamais produire. Pas parce qu'il est meilleur qu'eux dans leur discipline, mais parce qu'il opère dans un espace que personne d'autre n'occupe.

C'est ça la fertilité intellectuelle. Pas la profondeur, l'intersection.

Conseil concret aux spécialistes qui ont fait l'erreur de se sur-spécialiser :

Écartez-vous de votre domaine. Pas un peu, beaucoup. Pas la discipline d'à côté, mais une discipline qui semble n'avoir aucun rapport. Si t'es dev, va lire de l'anthropologie. Si t'es financier, va apprendre à composer de la musique. Si t'es médecin, plonge dans l'urbanisme.

Et visez pas l'expertise. C'est le piège dans lequel tombent ceux qui essaient de s'élargir : ils transposent leur réflexe de spécialiste sur les nouveaux domaines, ils veulent atteindre 95% partout, ils s'épuisent et abandonnent.

Visez 80%. C'est suffisant pour comprendre la structure du domaine, parler à ses experts, importer ses concepts, voir ses analogies avec ce que vous savez déjà. Au-delà de 80%, le rendement marginal s'effondre. En dessous, vous êtes un dilettante qui ne capte pas.

80% dans 4 domaines vaut infiniment plus que 99% dans un seul.

On entre dans l'ère des polymaths. Des gens hyper agency, capables de tirer des fils entre des disciplines distantes pour créer des objets, des entreprises, des œuvres que personne d'autre ne peut créer. L'IA va leur servir de force multiplicatrice ahurissante, parce qu'elle leur permet d'aller chercher le 80% dans un nouveau domaine en quelques semaines au lieu d'années.

Les spécialistes purs vont être laminés. Les généralistes purs sans agency vont rester décoratifs. Les polymaths agency vont absolument tout rafler.

Ça va être incroyable à vivre.

[Naval @naval]

Introducing USVC - a single basket of high-growth venture capital, for everyone.

No accreditation required, SEC-registered, and a very low $500 minimum.

Includes OpenAI, Anthropic, xAI, Sierra, Crusoe, Legora, and Vercel. As USVC adds more companies, investors will own a piece of that too.

Liquidity typically comes when companies exit, but we’re aiming to let investors redeem up to 5% of the fund every quarter. This isn’t guaranteed, but if we can make it work, you won’t be locked up like in a traditional venture fund.

It runs on AngelList, which already supports $125 billion of investor capital.

And I’ve joined USVC as the Chairman of its Investment Committee.
—

Go back to the 1500s, you set sail for the new world to find tons of gold - that was adventure capital.

Early-stage technology is the modern version. It says we are going to create something new, and it’s risky. It’s daring.

But ordinary people can’t invest until it’s old, until it’s no longer interesting, until everybody has access to it. By the time a stock IPOs, most of the alpha is gone. The adventure is gone. Public market investors are literally last in line.

This problem has become farcical in the last decade. Startups are reaching trillion dollar valuations in the private markets while ordinary investors have their noses up to the glass, wondering when they’ll be let in.

Investing in private markets isn’t easy. You need feet on the ground. You need judgment built over years. Most people don’t have the patience to wait ten or twenty years for an investment to come to fruition.

But there is no more productive, harder-working way to deploy a dollar than in true venture capital.

USVC enables you to invest in venture capital in a broad, accessible, professionally-managed way, through a single basket of innovation, focused on high-growth startups, at all stages.

It is how you bet on the future of tech: the smartest young people in the world, working insane hours, leveraged to the max, with code, hardware, capital, media, and community. Your dollar doesn’t work harder anywhere.

There is an old line - in the future, either you are telling a computer what to do, or a computer is telling you what to do. You don’t want to be on the wrong side of that transaction.

USVC lets you buy the future, but you buy it now. Then you wait, and if you are right, you get paid.

Get access here:

https://t.co/pAj1sqUsG0

[Felipe Demartini @namcios]

Dois engenheiros da Anthropic acabaram de mudar a forma como devs pensam sobre IA.

Barry Zhang e Mahesh Murag subiram no palco do AI Engineer Code Summit e disseram uma frase que incomodou muita gente:

"Parem de construir agentes. Construam Skills."

Em 16 minutos eles provam que a indústria inteira está resolvendo o problema errado.

Aqui está o que a maioria não entendeu:

→ Skills são pastas. Literalmente pastas com arquivos markdown.
→ Elas ensinam ao Claude o SEU fluxo de trabalho, a SUA expertise, o SEU domínio.
→ Um único agente genérico + biblioteca de Skills específicas supera dezenas de agentes especializados.
→ Fortune 100s já estão deployando Skills em escala pra ensinar agentes sobre processos internos.
→ Times de produtividade com 10.000+ devs usam Skills pra padronizar como código é escrito.

A analogia que eles usaram é perfeita:

Quem você quer fazendo seu imposto de renda? O gênio com QI 300 que nunca viu legislação tributária, ou o contador experiente que faz isso há 20 anos?

Inteligência sem expertise é entretenimento.
Expertise empacotada é produtividade.

O que mudou: a Anthropic parou de tentar criar agentes diferentes pra cada domínio.

Perceberam que com Claude Code, o padrão é sempre o mesmo. Um modelo acoplado a um runtime com filesystem.

A diferença entre um agente medíocre e um extraordinário não é o modelo. É o conhecimento de domínio que você alimenta.

Skills resolvem isso com progressive disclosure. O agente só carrega o nome e descrição da skill. Quando relevante, puxa o SKILL.md. Quando precisa de mais, navega os arquivos de referência. Zero desperdício de contexto.

Isso não é uma feature. É uma mudança de paradigma.

Quem entender isso agora vai operar em outro nível daqui a 90 dias.

Quem ignorar vai continuar escrevendo prompts de mil palavras toda vez que abrir o chat. E ainda explicar de novo e de novo o que “realmente” quer.

[Machina @EXM7777]

https://t.co/XXdYeXQwAo

[Garry Tan @garrytan]

If you want your OpenClaw or Hermes Agent to be able to have perfect total recall of all 10,000+ markdown files, GBrain is here to help.

It's exactly my OpenClaw/Hermes Agent setup. MIT-licensed open source. Hope it helps you build your mini-AGI.

https://t.co/yFpFU4pn5b

[Claude @claudeai]

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale.

It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days.

Now in public beta on the Claude Platform.

[Physics & Astronomy Zone @zone_astronomy]

The highest quality video of the moon was just released… this is so beautiful.

[Lenny Rachitsky @lennysan]

My biggest takeaways from @simonw:

1. November 2025 was an inflection point for AI coding. GPT 5.1 and Claude Opus 4.5 crossed a threshold where coding agents went from “mostly works” to “almost always does what you want it to do.” Software engineers who tinkered over the holidays realized the technology had become genuinely reliable.

2. Mid-career engineers are the most vulnerable—not juniors, not seniors. AI amplifies experienced engineers by letting them leverage decades of pattern recognition. It also dramatically helps new engineers onboard. Cloudflare and Shopify each hired a thousand interns because AI cut ramp-up time from a month to a week. But mid-career engineers who haven’t accumulated deep expertise and have already captured the beginner boost are in the most precarious position.

3. AI exhaustion is real and underestimated. Simon runs four coding agents in parallel and is mentally wiped out by 11 a.m. He’s getting more time back, but his brain is exhausted from the intensity of directing multiple autonomous workers. Some engineers are losing sleep to keep agents running. This may just be a novelty issue, but the underlying dynamic—that managing AI amplifies cognitive load even as it reduces labor—is a real tension. Good companies will manage expectations rather than expecting 5x output indefinitely.

4. Code is cheap now. This simple idea has profound implications. The thing that used to take most of the time—writing code—now takes the least. The bottleneck has shifted to everything else: deciding what to build, proving ideas work, getting user feedback. Since prototyping is nearly free, Simon often builds three versions of every feature when he’s getting started.

5. The “dark factory” is the most radical experiment in AI-assisted development happening right now. A company called StrongDM established a policy: nobody writes code, nobody reads code. Instead, they run a swarm of AI-simulated end users 24/7—thousands of fake employees making requests like “give me access to Jira”—at $10,000 a day in token costs. They even had coding agents build simulated versions of Slack, Jira, and Okta from API documentation so they could test without rate limits.

6. "Red/green TDD" is the single highest-leverage agentic engineering pattern. Having coding agents write tests first, watch them fail, then write the implementation, then watch them pass produces materially better results. The five-word prompt “use red/green TDD” encodes this entire workflow because the agents recognize the jargon.

7. “Hoarding things you know how to do” is one of Simon's other favorite agentic engineering patterns. Simon maintains a GitHub repo of 193 small HTML/JavaScript tools and a separate research repo of coding-agent experiments. Each one captures a technique, a proof of concept, or a library he’s tested. When a new problem arrives, he can point Claude Code at past projects and say “combine these two approaches.”

8. The "lethal trifecta" makes AI agent security fundamentally unsolved. Whenever an AI agent has access to private data, exposure to untrusted content (like incoming emails), and the ability to send data externally (like replying to email), you have a lethal trifecta. Prompt injection—where malicious instructions in untrusted text override the agent’s intended behavior—cannot be reliably prevented. Simon has predicted a “Challenger disaster” for AI security every six months for three years. It hasn’t happened yet, but he’s pretty sure it will.

9. Start every project from a thin template, not a long instructions file. Coding agents are phenomenally good at matching existing patterns. A single test file with your preferred indentation and style is more effective than paragraphs of written instructions. Simon starts every project with a template containing one test (literally testing that 1 + 1 = 2) laid out in his preferred style. The agent picks it up and follows the convention across the entire codebase. This is cheaper and more reliable than maintaining elaborate prompt files.

10. The pelican-on-a-bicycle benchmark accidentally became a real AI benchmark. Simon created it as a joke to mock numeric benchmarks—get each LLM to generate an SVG of a pelican riding a bicycle, and compare the drawings. Unexpectedly, there’s a strong correlation between how good the drawing is and how good the model is at everything else. Nobody can explain why. It’s become a meme: Gemini 3.1’s launch video featured a pelican riding a bicycle. The AI labs are aware of it and quietly competing on it.

Don't miss our full conversation: https://t.co/ghZZeyvWBZ

[Brian Halligan @bhalligan]

NEW EPISODE: @jack & @roelofbotha unpack @blocks 40% staff cut and rebuilding the entire company as a mini-AGI.

This isn’t “use AI to make people more productive.” It’s making the company itself the intelligence.

If you’re a founder or operator wondering what work looks like in the next 5 years… this is the episode.

The evolution looks like:

• Manager mode = Pyramid 🔺 (command & control)
• Founder mode = Flat ➖(founders decide fast)
• Dorsey mode = Circle 🔵 w/ AI at the center, humans at the edge, and decisions flow from customer inputs → AI → humans steering it

I’ve tried killing org charts before. Brutally hard. But we never had these tools.

This is rewriting the CEO playbook for the AI era.

Buckle up.

00:00 Existential Dread & Hope
02:56 AI Replaces Hierarchy
07:22 Block’s New Three Roles
26:47 Flattening the Company, Fast
35:23 Getting the Board to Buy-In, Fast
36:50 Building a Great Board
41:29 Founder CEO Lessons
48:18 Second Acts & Conviction
56:22 Timeless CEO Traits

[Docteur Laurent Alexandre @dr_l_alexandre]

Le monde quitte ChatGPT à une vitesse incroyable

La dernière personne de mon entourage qui utilisait ChatGPT est passée à Claude hier

100 % des gens que je côtoie utilisent Claude

Conséquence : les actions @OpenAI sont invendables en ce moment selon @Bloomberg

[a16z @a16z]

Marc Andreessen says AI is the "silver bullet excuse" for companies laying people off, but most layoffs are actually due to higher interest rates and overstaffing during COVID:

"This entire labor displacement thing is 100% incorrect. It's completely wrong. It's classic zero-sum economics."

"It was the combination of the two—interest rates going to zero during COVID, and then the complete loss of discipline at all these companies when they went virtual and when employees just became an icon on a screen."

"What you have happening right now is that essentially every large company is overstaffed. We could debate how much—it's at least overstaffed by 25%. I think most large companies are overstaffed by 50%. A lot of them are overstaffed by 75%."

"And now they all have the silver bullet excuse—it's AI."

@pmarca with @HarryStebbings

[Elon Musk @elonmusk]

Europe will love Tesla self-driving!

Due to the extreme regulatory burden of the EU, which in general stifles innovation in Europe, Tesla owners there have been stuck with basic lane-following.

[Naval @naval]

AI coding agents can now deliver one-shot custom apps straight to your phone.

It’s the beginning of the end for the iPhone’s dominance.

[Shaun Maguire @shaunmmaguire]

https://t.co/KXLgdhFe1P