Matthieu Bienvenu

Head of Business Development @ Nebius • I love life, my wife, and my fellow man (at least I try to...) • Searching for truth and beauty in this world

[Matthieu Bienvenu @mabienve]

Apart from helping China develop its own national champions, over which we would no longer have any visibility, I don't see what the US and Europe would gain from this law !

[Clash Report @clashreport]

NEW: The US is pushing legislation that would force the Netherlands to ban all remaining ASML lithography machine sales and servicing to China, with a 150-day compliance deadline backed by the threat of extraterritorial sanctions.

The MATCH Act targets older DUV machines still permitted under Dutch rules.

China was 33% of ASML's sales in 2025.

The bill has bipartisan support and is being attached to the must-pass defense budget.

The Dutch trade minister called forced cooperation "undesirable" and has lobbied Washington against the measure, but Dutch sources assess chances of blocking it as slim.

ASML is Europe's most valuable listed company and relies heavily on US suppliers, limiting its room to push back.

Source: NL Times

[Matthieu Bienvenu @mabienve]

@Kenpashi_82 Ah je l'ai eu aussi, mais pour une autre société: Classplus

[Matthieu Bienvenu @mabienve]

@brivael the chosen one !

[Matthieu Bienvenu @mabienve]

@MrBeast 3,041,307

[BFM Business @bfmbusiness]

Cloud IA : Nebius parie sur la France

"On veut renforcer cet écosystème IA en Europe, on veut mettre nos infrastructures au maximum à disposition des Européens"

💬Matthieu Bienvenu, Directeur du développement commercial international de Nebius
🎙️@simottel

[Matthieu Bienvenu @mabienve]

@romanchernin #buildingmode !! Such an honour to be part of the journey

[Matthieu Bienvenu @mabienve]

@nebiusai @reactor You did a mistake on the alias. Should be @reactorworld

[Matthieu Bienvenu @mabienve]

@bad_pote @moniaceleste que penses-tu de cette technologie de peinture thermo réactive qui peut aider à mieux absorber la chaleur l'hiver et mieux renvoyer la chaleur l'été.

[Matthieu Bienvenu @mabienve]

@elonmusk @brivael becoming Elon BFF

[dylan ツ @demian_ai]

Inference got a hundred times cheaper this year. The compute bill went up anyway.

If you understand why those two sentences are both true at the same time, you understand the most important thing happening in AI right now.

I work on inference for a living, at @nebiustf, where we run open-source managed inference at scale. Most of what follows is what I'm seeing from inside the bill.

12 months ago, the cost of 1M tokens of frontier-class reasoning was somewhere on the order of $60.
Today, an equivalent quality of output costs roughly $0.50.

Price /token of o1-level intelligence has dropped about a 128x in a year.
Price of GPT-4-level output has dropped roughly 100x since the original GPT-4 shipped.

By any normal reading of a technology cost curve, this should be deflationary. It should be saving customers money.

The opposite has happened. The total compute bill at every hyperscaler is going up, not down. Anthropic just signed multi-year capacity deals with both XAI and Amazon. Microsoft's Azure capex guide for 2026 starts with an eight. OpenAI is reportedly spending more on compute every quarter than it did in all of 2023. Nvidia paid roughly twenty billion dollars to acquire Groq, an inference-specialist company that did not exist as a serious commercial entity three years ago.
The cost curve and the demand curve crossed, and then the demand curve lapped the cost curve.

Here is what happened underneath.
A reasoning model burns roughly 10x the output tokens of a non-reasoning model on the same task, because it spends most of its tokens thinking out loud before answering. An agentic workflow chains roughly twenty times the requests of a single-shot completion, because it loops, calls tools, plans, retries, and synthesizes. A modern deep-research query (the kind a research analyst can fire off in fifteen seconds and then walk away from for ten minutes) costs more compute than 10 original GPT-4 queries combined. We made every individual token a hundred times cheaper, and then we built a generation of products that consume ten thousand times more tokens.

This is the Jevons paradox playing out at trillion-dollar scale, in compressed time, in front of everyone. Jevons noticed in 1865 that making coal-burning more efficient did not reduce coal consumption. It increased it, because efficiency unlocked uses that were previously uneconomic. Steam engines became more practical at smaller scales. Whole industries that could not afford coal at the old price suddenly could. Britain's coal consumption rose sharply, not despite the efficiency gains, but because of them.

The same thing is happening to AI compute right now and it is happening faster than any analogous historical cycle. Falling token prices did not contract demand. They unlocked agents, deep research, code-writing systems, multi-step reasoning, persistent memory, the entire next layer of AI products. Every product in that next layer consumes orders of magnitude more compute than the chat interfaces it is replacing.

The math at the aggregate level is brutal: 100x cheaper tokens times 10 000 more tokens equals a 100x larger total bill.

The implications stack quickly.
If you are running a hyperscaler, your 2026 capex guide is not a peak. It is a step on a curve. Inference is structurally always-on, twenty-four hours a day, in a way that training never was. Training is bursty. You spin up a cluster, run for weeks or months, and stop. Inference runs continuously, scales with usage, and the usage curve is exponential. Your power bill, your cooling bill, your transceiver count, your storage footprint, all of these were sized for a workload mix that no longer exists.

If you are running an AI software company built on top of someone else's closed API, you have a problem that did not exist a year ago. Your gross margins get worse as your customers get more value out of your product, because the more they use it, the more compute you pay for. The companies that win this are the ones that figured out vertical integration before the math caught them.

If you are watching this from a distance and trying to understand where the next bottlenecks form, the answer is everywhere downstream of "more inference compute, always-on, with massive memory state per session." The KV cache, the running memory state of a long conversation or an agent loop, is the silent monster of the inference era. It does not scale linearly with parameters. It scales linearly with context length and number of agent steps. A long agent session can hold tens of gigabytes of state per user, per session.

Multiply that by every concurrent user of every product, and you understand why $MU, $SNDK, $TOWCF, and the entire memory and packaging layer have re-rated the way they have.

The CPU-to-GPU ratio is evolving. Training is 1:8. Basic chat inference is 1:4. Agentic inference is 1:1, sometimes CPU-heavy. Google has split its TPU line in two, with a dedicated inference chip carrying tripled SRAM for KV cache. $INTC and $AMD just spent two earnings calls explaining that this shift is structural, not cyclical. The hardware map is redrawing in real time and the financial press is mostly still writing about training clusters.

The right framing of where we are right now is not that AI is hitting a wall. The framing a year ago that scaling was hitting a wall was the most expensive bad take of the cycle. The right framing is that AI got dramatically cheaper, dramatically more capable, and dramatically more useful, and the cost of running it at the new equilibrium of demand is much higher than the cost at the old equilibrium of demand, because the new equilibrium is enormous.

A meaningful share of what we actually do at Token Factory, day to day, is help customers stop their bills from running away from them. KV-cache management. Speculative decoding. Quantization. Routing. The kind of vertical integration that, eighteen months ago, every product team was happy to leave abstracted away behind a closed API. The reason this stack matters now is the same reason this whole essay matters: at the new equilibrium of inference demand, the cost of treating compute as a commodity is no longer survivable. The companies that figure out the layer beneath the API are the ones who keep their margins.

Cheaper tokens. More tokens.
Same coal as 1865.

[Matthieu Bienvenu @mabienve]

@DrScratch @Cthulhu4Prez @HairHellHell @MaudBregeon ?

[Matthieu Bienvenu @mabienve]

@Mileistesfr Attention:
- Pas de SMIC mais 80% des salariés sont couverts par des conventions coll. avec salaires min élevés
- Licenciement: employeur peut licencier mais chômage généreux
- Droit de grève fonctionnaires : beaucoup d'agents publics sous contrats privés et peuvent faire grève

[Matthieu Bienvenu @mabienve]

Hello @plbiojout ! Je n'arrive pas à échanger avec le chat et j'ai l'impression que le site ne se lance pas. Est-ce que je dois faire quelque chose de particulier ?

[Matthieu Bienvenu @mabienve]

I just launched my autonomous AI company "Full Co" on @NanoCorpHQ

Verification: nimb-lsYQ

[Léo Peria-Peigné @Leo_PeriaPeigne]

Le nombre de pro-russes attardés dans les commentaires, que quelqu'un donne une médaille à ce Carré!

[Matthieu Bienvenu @mabienve]

@Xavier75

[Matthieu Bienvenu @mabienve]

@CerfiaFR "Philippe de Gaulle" qui en plus d'être le fils du Général était aussi amiral dans la marine française !

[Christophe Daunique @CDaunique]

Je viens d'écouter cet édito d’Eugénie Bastié @EugenieBastie sur la génération Z et le rapport au travail.
Il fait écho à un article du Figaro que j’avais déjà commenté en janvier sur le même sujet. Beaucoup d’éléments décrits sont justes : quiet quitting, désengagement, recherche d’équilibre de vie, distance vis-à-vis du travail...

Cependant, je voudrais apporter une lecture complémentaire.
Il y a aujourd’hui une tentation très forte de lire ce phénomène uniquement sous un angle culturel ou psychologique. Les jeunes seraient devenus plus fragiles, moins endurants, moins attachés à la valeur travail...
C’est une lecture très répandue, notamment dans les générations plus anciennes, et c'est d'ailleurs typique de ce que j'ai appelé la pensée boomer.

Toutefois, cette lecture passe à côté d’un élément fondamental, les incitations économiques. Quand le travail ne paie plus, il ne faut pas s’étonner que les gens aient moins envie de travailler.

Or c’est exactement la situation dans laquelle nous sommes.
Le travail est aujourd’hui lourdement prélevé pour financer un modèle fiscalo-social qui repose largement sur une extraction intergénérationnelle massive au profit des seniors. Une part considérable de la production des actifs est captée pour financer des transferts vers des populations qui ne travaillent plus, notamment via les pensions de retraite.
Dans ces conditions, le rendement marginal de l’effort devient très faible. Travailler plus signifie souvent : plus de contraintes, plus de stress, plus de responsabilités… pour un gain net limité.
C'est fondamentalement un problème d'incitations.

Il faut aussi reconnaître que le monde du travail lui-même a profondément changé : mondialisation, concurrence accrue, pression permanente sur la productivité, automatisation, et désormais l’IA. Les entreprises sont elles-mêmes prises dans une logique de compétition permanente. Dans ce contexte, les salariés deviennent souvent des variables d’ajustement économiques.

Il ne faut donc pas s’étonner qu’en retour, les salariés adoptent un comportement plus transactionnel et moins loyal. Autrement dit, ils deviennent rationnellement des mercenaires. Pourquoi faire des efforts ou être loyal quand derrière les promesses ne sont pas tenues car à la merci de la première contrainte économique ?

Face à cela, que devrait faire la puissance publique ? Elle ne peut pas changer ni mondialisation, ni la technologie, ni l’économie mondiale telle qu’elle est. En revanche, elle peut agir sur un levier central qui est le modèle fiscalo-social.

Si l’on veut réellement que les jeunes générations aient envie de travailler davantage, il faut restaurer le rendement du travail. Cela suppose un débat que la classe politique refuse obstinément d’avoir, celui du rééquilibrage entre actifs et inactifs, notamment les plus âgés.

Tant que nous refuserons de remettre en cause cette extraction intergénérationnelle massive, il ne faudra pas s’étonner que la motivation au travail continue de s’éroder chez les plus jeunes. Les anciennes générations ne peuvent pas à la fois demander plus d’efforts aux jeunes et refuser toute remise en cause du système qui capte une part croissante du fruit de leur travail.

Il faudra bien à un moment refondre notre modèle fiscalo-social et baisser drastiquement les transferts sociaux en faveur des seniors, en commençant par les pensions de retraite.
Je ne suis pas naïf au point de croire que les mentalités des plus jeunes n'auront pas un impact, même après avoir refondu le modèle, mais cette action sera toujours plus utile qu'un énième sermon moral de seniors qui se lamentent stérilement en disant que tout était mieux avant, tout en faisant preuve d'un énorme égoïsme.

[Matthieu Bienvenu @mabienve]

@MoselleCD57 on a retrouvé Nestor Halambique !

[Matthieu Bienvenu @mabienve]

@grok @manly_mentor can you provide reliable source links ?