Pascal Poty

Born at 322.17 ppm CO2 Senior ExP @adn_wallonie @digitalwallonia Program Dir. AI - Pr. & Epicurean Enthusiastic, Author of the book l'Instant Mobile. E/acc

[Pascal Poty @Mobisphere]

Gartner said through 2030, AI data center owners would need to generate a cumulative $8.5 trillion in revenue to cover announced capital spending, financing costs, and a 12% ROIC. More here. #AI https://t.co/kFqxXqtf09 via @Gartner_Inc

[Pascal Poty @Mobisphere]

Unitree says its 'self-evolving robots' are the next big leap for humanoids — as Tesla Optimus production ramps up to hit 20,000 robots a week https://t.co/o1Q1VUXXYm #robolution

[Pascal Poty @Mobisphere]

Vendeurs de pelles et pioches et les autres… Note d’Apollo Global Management selon laquelle, en dehors du secteur technologique, l’usage de l’IA n’a produirais aucun gain financier mesurable pour les entreprises américaines
https://t.co/qO0BQCYr6A

[Pascal Poty @Mobisphere]

« On a eu d’excellents retours » : Carrefour lance les courses via ChatGPT, avec recettes, budget et livraison https://t.co/YYSnmxdusd

[Pascal Poty @Mobisphere]

‘Know Your Agent’ will be the new rule of the AI age https://t.co/1tKPKvfuL0 via @ft

[Pascal Poty @Mobisphere]

There is no moat for LLM giants https://t.co/PISgkQCiV7

[Boston Consulting Group @BCG]

Which countries are making the most progress with AI adoption?

BCG's AI Maturity Matrix 2026 assesses AI readiness across more than 75 economies and 33 indicators.

Chile, the Czech Republic, Ecuador, Greece, Japan, Lithuania, Nigeria, and South Korea have all moved up a readiness tier since 2024.

With AI ambition now nearly universal, execution speed is the differentiator.

Read more: https://t.co/1msxOl0Hm3

[Arthur Mensch @arthurmensch]

We've just released a comprehensive suite of state-of-the-art voice models https://t.co/Ue1S72c9HW. Voice will be the most important interface to AI systems, and we deploy voice models physically close to reasoning models

[Pascal Poty @Mobisphere]

AI Gets Physical: The Global Startups Readiness Index https://t.co/wnHnudl3cF #physicalAI

[01net @01net]

🚨 28 minutes : c'est tout ce qu'il a fallu à Claude pour reproduire la cyberattaque sur les frigos des supermarchés ! ➡️ https://t.co/OIq10EQCSC

[Luiza Jarovsky, PhD @LuizaJarovsky]

🚨 IMPORTANT: The EU's first AI standard has been published in the Official Journal of the European Union! Here’s what companies need to know:

The standard EN 18286:2026, “Artificial intelligence - Quality management system for EU AI Act regulatory purposes,” specifies requirements and provides guidance for defining, implementing, and maintaining a quality management system for organizations that provide high-risk AI systems.

The European standardization organizations CEN and CENELEC had already published the details of this harmonized standard earlier, but the European Commission still had to formally assess whether it met the AI Act’s intended objectives and legal requirements.

That assessment has now been completed, as this standard was published today in the Official Journal of the EU.

In practice, companies can apply this standard and benefit from a presumption of conformity with the EU AI Act’s quality management system requirements, as established in Article 17(1) points “a” through “m.”

As a reminder, this article will come into force on December 2, 2027 for high-risk AI systems under Annex III and on August 2, 2028 for high-risk AI systems under Annex I, according to Article 113(c).

This is the first European standard on AI to be published in the EU Official Journal. Additional standards covering the following areas are also expected to be published:

- risk management
- governance and quality of datasets
- record keeping
- transparency
- human oversight
- accuracy
- robustness
- cybersecurity
- quality management
- conformity assessment

This is great news for everyone. The EU AI Act's standards are voluntary, but they will help increase legal certainty, foster innovation, standardize legal requirements, and accelerate compliance.

However, the challenges in AI are only beginning, and every company developing or deploying AI should focus on strengthening its AI governance and safety practices.

I will keep you posted.

👉 READ my full article (and join my newsletter's 102,200+ subscribers) below.

[Pascal Poty @Mobisphere]

Verizon CTO: #6G needs a breakthrough for AI uplink demand https://t.co/QGFmYAXX7s

[Andrej Karpathy @karpathy]

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.

- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.

The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.

So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

[Gilles Babinet @babgi]

1/2 point de PIB de croissance en plus par an d'ici quelques années : une perspective enthousiasmante selon la présidente du FMI. Et mine de rien c'est effectivement considérable : 10,000 milliards de dollars sur dix ans.
https://t.co/EF7gznWc86

[Pascal Poty @Mobisphere]

AI models may soon self-replicate without access to data centers, Anthropic warns - https://t.co/y34rsJ20FO

[Stanford HAI @StanfordHAI]

A common assumption is that people use AI as a shortcut. Stanford HAI Faculty Affiliate @Diyi_Yang and her team analyzed a quarter-million anonymized chatbot conversations and found most users stay engaged, asking for explanations, working through friction, and refining their work: https://t.co/dASMfyyLfz

[Pascal Poty @Mobisphere]

The State of AI Report 2026, by @nathanbenaich https://t.co/s2LSZ8Smsf

[Pascal Poty @Mobisphere]

Les premiers chiffres qui incriminent l'IA dans le repli de l'emploi des jeunes selon HEC et la Banque de France
https://t.co/Ed1MdAqIpz

[Mistral AI @MistralAI]

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.