AI Productivity Lead @ Sanofi — scaling AI engineering + measuring real impact in regulated pharma. @AnthropicAI Ambassador • Paris AI meetups host.
Paris
Joined October 2010
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Most AI content on this feed is theory. Mine is from the other side.
I scale AI engineering across a global regulated pharma and measure what actually works.
Posting what it takes.
Field reports, unpolished takes, occasional hot takes.
I'd use @mattpocockuk's /grill-with-docs to recover the architecture decisions that never made it into the docs:
/grill-with-docs on our existing architecture, not a new feature: grill me only about decisions that aren't written down anywhere. Every answer lands in GLOSSARY.md or an ADR.
Stop when a new teammate could re-derive our design from the docs alone
Finding vulnerabilities got cheap. Reading the patch and checking nothing else broke did not. In our team the bottleneck moved from writing code to reviewing it, and review doesn't scale with a model.
Almost all the code in the 200-person org I work with is now AI-assisted. The surprise: writing it isn't where we got stuck. Writing the spec, and the check that proves the agent did what we meant, is. Nobody taught us that. We're learning it in production.
Unpopular take: AI doesn't replace specialists, it replaces average specialists.
The top 1% get a lever. The competent, certified, usually-right middle now competes with something that works at 3am and never gets bored.
That's the part nobody puts on a slide.
The spec is the new code.
Writing the code used to be the bottleneck. Now an agent writes it in minutes.
What is left is saying precisely what you want, and proving you got it.
Intent in, validation out. That is the engineer's job now.
Agents are becoming the experts.
The single-field specialist now competes with an agent that never sleeps.
The generalist directs ten of them.
The scarce skill is no longer knowing the answer. It is knowing which question to ask.
Thomas Roux retweeted
Exactly a year ago, I launched Beads, my agentic memory layer, issue tracker, and knowledge graph. I built a sketch of it in fifteen minutes with Claude Sonnet 4.5, and the rest was history. Today Beads has a sophisticated, scalable backend, a strong contrib team, and 1.5M downloads.
You may have your own favorite tools, but Beads is every agent's favorite tool. Show it to your agent today.
blog.gascity.com/posts/fun-f…
Thomas Roux retweeted
a couple months back i was on a pod with @vtahowe and @ianlivingstone chatting w/ a Billions-of-market-cap CISO about how to balance giving developers tools to make coding agents useful (think, mcps to all kinds of useful context link ticketing, email, slack etc) while avoiding data exfiltration, prompt injection, credential hijacks, etc. There was some hand-wavy "mcp gateway" talk but really cool to see this important question materialized into a formal spec for builders.
There's a lot more to build here, but this is a great start
Today we're publishing the first draft of the Agent Baseline Software Factory Reference Architecture!
An opinionated blueprint for securing increasingly autonomous software factories, built with folks across the community.
It's open for feedback and contributions!
agentbaseline.org/reference-…
In 2012, Michel Serres saw Petite Poucette challenge authority: knowledge was now in everyone’s pocket. AI takes this further. As agents take on deep expertise, our value shifts to framing problems, judging outputs and connecting ideas. Authority comes from helping others grow.
One thing to test today? @humanlayer_dev
Don't know where to start your day? Start here.
RPI workflows, human-in-the-loop on the design doc, no 2,000-line surprises. This is what AI-native engineering actually looks like.
Great work @dexhorthy & @0xblacklight
Thomas Roux retweeted
One of the most important misconceptions about AI today is that “everyone is already using it”
They are not
According to figures shared by @SpartDev to HEC Paris MBA students:
- 84% of the world has never used AI at all
- 16% have only experimented with free chatbots casually
- around 0.3% actually pay for AI tools
- and only about 0.04% use AI as a serious workflow or production tool
That final group — the people building with AI, automating with it, coding with it, researching with it, creating with it daily — is still extraordinarily small relative to the global population
Why this matters
We are still extremely early
What feels “oversaturated” online is often just a visibility illusion created by highly active tech circles on:
- X
- LinkedIn
- GitHub
- Reddit
- and startup communities
Meanwhile, most of the world:
- has never prompted an LLM
- does not understand AI workflows
- and has not integrated AI into education, business, or daily productivity
That creates a massive opportunity gap
The people learning AI today are not competing against 8 billion people
They are competing against a tiny fraction of the population actively developing practical AI leverage
The deeper lesson here is important:
AI should not replace thinking
It should amplify thinking
The individuals gaining the most value from AI are usually not passive users asking random questions
They are:
- experimenting
- challenging outputs
- refining prompts
- building systems
- automating workflows
- and combining domain expertise with curiosity
AI works best as:
- a co-pilot
- research assistant
- simulator
- tutor
- brainstorming engine
- and productivity multiplier
not as a substitute for judgment
The bigger picture
We are likely entering a transition period similar to:
- the early internet era in the 1990s
- smartphones in the late 2000s
- or social media in the early 2010s
At first, adoption looks niche
Then suddenly entire industries reorganize around the technology
That is why the current phase matters so much
People who develop AI literacy early may gain disproportionate advantages in:
- productivity
- learning speed
- business creation
- communication
- research
- and career adaptability
Bottom line
The real AI divide today is not between humans and machines
It is between people learning how to work with AI effectively — and those ignoring the shift entirely
#GenoInsights #AI #ArtificialIntelligence #Technology #Innovation #FutureOfWork #DigitalTransformation #Productivity #Automation #Education
Elon Musk built rockets and cars without coming from aerospace or automotive.
Tim Berners-Lee built the Web without coming from hypertext or networking.
Outsiders with depth see what insiders can't. That's where the AI decade gets built.
Thomas Roux retweeted
Any London-based AI events looking for a conference speaker, and planning to professionally film their talks?
I have a banger of a talk lined up that I need to let loose somewhere
Thomas Roux retweeted
I found this info shared by @SpartDev to HEC Paris MBA students and I thought I’d amplify here cause it is super insightful.
84% of the world has never used AI. Not once.
16% tried a free chatbot on a Sunday night.
0.3% pay $20/month for it.
And 0.04%?
That's around 3 million people out of 8.1 billion who actually use AI as a real working tool.
That's the red dot in the image.
→ If AI scares you, good. Fear is energy. Use it. Every job is being reshaped right now, not someday.
→ You're the driver, not the passenger. AI is like a GPS. It calculates the best route. But you decide where to go. The moment you stop thinking, you become the passenger. Don't outsource your thinking.
→ Curiosity beats expertise. You can learn a domain in a week instead of six months. But only if you push, question, challenge. AI is a sparring partner, not a vending machine.
A few thoughts.
Not everyone knows how to mentor. Mentoring is a skill on its own, not a byproduct of being senior. That's the real bottleneck, not AI.
AI doesn't remove friction, it shifts it. From "finding the answer" to "asking the right question and challenging the output."
What I see with our interns: they use AI more creatively than most seniors. Sometimes I learn from them.
Curiosity beats expertise. The risk isn't AI, it's passivity.
you don’t hire interns because they’re gonna ship mad features
Every intern is an investment in a future hire. Perhaps A modern day apprenticeship.
You don’t hire juniors cause you need that extra 2-3 bug fixes per day. You hire juniors so you can turn them into seniors
When you say “ai will replace juniors” all you’re telling me is you don’t understand mentorship in software careers, or you think of every engineer as a ticket factory.
We do need to rethink how we upskill this new generation of SWEs though. The best engineers are great at AI (coding, context eng, etc) and great at software engineering - (systems, algorithms, debugging, architecture)
More seniors are good at the latter, more juniors are good at the former, but a lot of ai coding takes away the “friction” which, as @badlogicgames so helpfully pointed out, is where you learn