En 2013, Edward Snowden révélait la surveillance de masse. Aujourd'hui, il prévient : l'IA est en train d'en activer la version finale.
Appels analysés en temps réel dès le premier mot, Palantir qui traque les « gens étranges », caméras qui transcrivent tout ce que vous dites dans la rue… La vie privée est-elle déjà morte ?
Mais l'ex-analyste NSA va plus loin : au-delà de l'État, il décrit la tyrannie de la médiocrité — cette force invisible qui, à coups d'algorithmes, lisse peu à peu notre créativité et notre manière de penser.
Que nous reste-t-il pour rester libres dans un monde qui veut tout quantifier ?
Original : https://www.youtube.com/watch?v=VCfTLQo5QZ0
2026-06-03
Je rassemble ici en trois paragraphes et neuf liens tout ce que j’ai écrit sur l’IA générative (IAG) dans la partie Blog de mon site depuis deux ans. Ceci donne un bon aperçu de ma position concernant l’IAG à la date de publication de ce billet. Je mets également ce texte dans ma page sur l’IA dans la partie Cours du site, et c’est là-bas que je ferai des mises à jour si besoin.
"Tokenmaxxing" is the latest corporate trend taking over the tech world, but is it actually measuring developer productivity—or is it just burning a monumental hole in company finances?
In this episode of Internet of Bugs, Carl breaks down the origins of tokenmaxxing, from Jensen Huang’s podcast comments to prestigious VC letters pushing AI-driven developer metrics. We explore the terrible history of trying to measure programmer productivity (like counting lines of code), how Goodhart's law ruins these systems, and why massive tech companies might actually be incentivizing this slop just to train their own internal AI models.
Furthermore, we look at the financial reality of the AI bubble, upcoming tech IPOs, and why shipping dead code or hiding bugs behind feature flags can cause catastrophic real-world losses (like the 2012 Knight Capital Group meltdown).
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- 00:00 What is "Tokenmaxxing"?
- 00:55 Where the Trend Started (Nvidia & VCs)
- 02:03 The Nightmare of Corporate Token Leaderboards
- 02:24 How Do We Actually Measure Good Code?
- 03:52 Lines of Code, Story Points, and Failed Metrics
- 05:01 Goodhart's Law & Gaming the System
- 05:39 Why Big Tech Wants You Burning Tokens
- 06:49 The Superstitions of Software Engineering (LeetCode & Agile)
- 07:55 The Danger of Dead Code & The $440M Bug
- 09:17 The Real Solution
- 10:47 The AI Bubble's "Groundhog Day" IPO Forecast
#Tokenmaxxing #SoftwareEngineering #AIBubble #DeveloperProductivity #TechTrends #Programming #TechIndustry #InternetOfBugs
// Sources
// Origins
https://the-decoder.com/nvidia-ceo-je...
https://www.nytimes.com/2026/03/20/te...
https://a16z.com/there-are-only-two-p...
https://finance.yahoo.com/news/a16z-u...
https://www.notboring.co/p/a16z-the-p...
https://en.wikipedia.org/wiki/Andrees...
// What companies are doing
/ metas_internal_leaderboard_ranks_employees...
https://fortune.com/2026/04/09/meta-k...
https://www.bbc.com/news/articles/cvg...
https://www.firstround.com/ai/shopify
https://gaiinsights.com/blog/the-ceo-...
https://help.shopify.com/en/manual/sh...
https://www.forrester.com/blogs/what-...
https://www.businessinsider.com/amazo...
https://aws.amazon.com/blogs/machine-...
https://aws.amazon.com/blogs/machine-...
// Programming History Mentioned
https://maseconomics.com/goodharts-la...
https://dl.acm.org/doi/10.1145/362929...
https://junit.org/
https://www.henricodolfing.ch/en/case...
https://blog.statsig.com/how-to-lose-...
// AI limitations
https://epoch.ai/blog/will-we-run-out...
// Tokenmaxxing backlash
https://www.tomshardware.com/tech-ind...
https://news.ycombinator.com/item?id=...
https://www.businessinsider.com/uber-...
// Financial irregularities (just the most recent tip of the massive iceberg)
https://www.wheresyoured.at/anthropic...
https://www.thestreet.com/latest-news...
https://www.datacenterdynamics.com/en...
https://techcrunch.com/2026/05/20/ant...
Be sure to use AI when making
your next, I don’t know, meal plan,
for example. Definitely do not call
your friend who loves to cook and ask her
for her favorite recipes or tips or ways
to save time making meals,
because you will end
up talking for longer than you had hoped,
hearing, perhaps, about her father’s cancer
diagnosis or how lonely she’s been or even
what she’s planted in her spring
garden and then lost with the early frost.
...
images of ai
to help understand bette
CC
Sophie Drouvroy
2005, une année qui aurait dû bouleverser la France, rendre le numérique plus accessible aux personnes handicapées. Le titre me laissait espérer pour un monde plus inclusif : Loi du 11 février 2005 pour l’égalité des droits et des chances, la participation et la citoyenneté des personnes handicapées.
À l’aube de 2025, le numérique n’est pas accessible à toutes et tous. La promesse d’égalité des droits et des chances, la participation et la citoyenneté des personnes handicapées ne sont qu’un écran de fumée.
Aujourd’hui, le numérique responsable et l’intelligence artificielle (IA) ont le vent en poupe.
Je me sens devant un effroyable paradoxe : choisir entre le numérique responsable et l’intelligence artificielle qui pourrait enfin rendre accessible ce que les humains n’ont pas réussi à faire jusqu’à présent ?
For now I see two ways to use LLMs-that-do-my-work (as opposed to LLMs-that-search, LLMs-that-ask-me-questions or LLMs-that-build-tools-I-use for instance)
1️⃣ Generating generic things
Given LLMs approximate language, and language approximates intent, it can be argued that the highly over-marketed "artificial intelligence" aspect of LLMs (aka "do something for me that works plz") is most useful when we don't know precisely what we want, and become less valuable the more precise you get (applying semantic anchoring on too many items, typically)
2️⃣ Helping with producing precise things
This includes being technical, having a technical approach, and using the tool positively for the technical steps (for instance, Test Driven Development and Domain Driven Design)
✅ Pros:
- Feeling more productive [1]
- Maybe more productivity (all the studies [1] I've read seem to point it's not the case but this is still early and moving)
❌ Cons (some happen systematically, others conditionally) :
- Dependency on the tool (breaking changes, availability issues, pricing policy changes, data use, state of the relationship between your country and the seller's country, etc)
- Token saving management [2]
- Cognitive surrender [3]
- A LOT more bugs in production [23]
- Senior developer time mostly spent on reviewing code that should not have reached them in that state [23]
- Lack of friction causing drops in memory, learning, engagement, and motivation [4][27]
- Value given to Contributions rather than to Contributors : why should lead devs hire you instead of firing up their own LLM? [28]
- Addiction to Control and/or Validation and/or Slot machine loop ("Replay button") [5]
- Automation of what brought you Joy at work [6]
- More expectations/stress [22] while being paid the same
- Critical thinking presence needs to be maintained (through discipline for instance, which does not work) and/or delegated into automated tests (back to Control replacing Joy)
- Critical thinking quality needs to be maintained by regularly doing things manually despite the tool acting actively against it ("Claude would do it faster" is such a pernicious thought)
- Loss of diversity in collaborative conversations ("my Claude's arguments versus your Claude's arguments") [7]
- How do you mentor Juniors?
1️⃣ Pieces of data gathered about the impact of LLMs on productivity
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_ai-for-developer-productivity-what-now-activity-7452020616016195584-EX1V?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣ Multilingual prompting saves 20-40% tokens
https://arxiv.org/pdf/2507.00246
3️⃣ Study from January 2026 about cognitive surrender
https://www.linkedin.com/posts/mehdi-moussaid-160ba916a_labandon-cognitif-cest-le-nom-donn%C3%A9-par-activity-7447901614989934592-HF6l?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
4️⃣ Friction : it matters (memory, learning, engagement, motivation)
https://youtu.be/rf642RFALDU?is=wl04yfCcEyU7A2d0
6️⃣ A study from March 2026 about LLMs automating the joy out of work
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_are-we-automating-the-joy-out-of-work-designing-activity-7451939177014841344-qmGA?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
7️⃣ LLM use reduces the number of fields explored by Research
https://www.linkedin.com/posts/mehdi-moussaid-160ba916a_encore-un-article-incroyable-publi%C3%A9-la-semaine-activity-7422549300930310144-Qv6v?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
8️⃣ study from April 2026 about LLMs corrupting your documents when you delegate, via sparse but severe errors
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_llms-corrupt-your-documents-when-you-delegate-activity-7453400644373303296-TyT4?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
9️⃣ A study from June 2025 about cognitive debt when using chatgpt as an assistant for essay writing
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_your-brain-on-chatgpt-accumulation-of-cognitive-activity-7451938489916506112-mfOD?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
🔟 LLMs induce slavery
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_les-travailleurses-du-clic-activity-7450846472041975810-9ma6?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣1️⃣ Lack of intelligence
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_vous-voulez-gagner-2-millions-de-dollars-activity-7450472933619253248-xMpe?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣2️⃣ Deleted volume
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_claude-code-deleted-my-entire-archive-activity-7447698537464819712-UFoG?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
https://arxiv.org/abs/2306.08189 (An analysis of language models on negation benchmarks)
1️⃣3️⃣ A study from March 2026 about LLMs doing human work
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_crashing-waves-vs-rising-tides-preliminary-activity-7447605340403191808-ZsaI?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣4️⃣ A study from March 2026 about LLMs answering correctly to visual questions, despite not being given any visual input
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_ia-santaez-hallucinations-activity-7446491905867247616-Q5uH?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣5️⃣ A study from February 2026 on ai psychosis (aka delusional spiraling) caused by chatbots
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_ai-ai-activity-7445881612736712704-7NFv?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣7️⃣ A study from March 2026 about LLM writing assistants shifting users' attitudes on societal issues through their bias
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_when-people-use-ai-for-writing-assistance-activity-7448643257019809792-_ccP?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣8️⃣ Generated nudes from victims of Crans-Montana fire
https://www.lebigdata.fr/a-vomir-ils-utilisent-lia-grok-pour-denuder-les-victimes-de-crans-montana
1️⃣9️⃣ The Chinese government floods X search results with porn whenever there is political unrest
https://x.com/nikitabier/status/2017134769113542752
2️⃣0️⃣ Hallucination is feature, not a bug (in probabilistic tools)
https://www.linkedin.com/posts/matsanchez_on-na-jamais-%C3%A9t%C3%A9-aussi-pr%C3%A8s-de-lia-g%C3%A9n%C3%A9rale-activity-7455161889161949184-IcQ2?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣2️⃣ Compressed Cognition : Agentic coding is mentally expensive
https://www.linkedin.com/posts/adam-tornhill-71759b48_i-have-to-admit-that-i-havent-had-this-much-share-7458044119022616577-q6K4?utm_source=share&utm_medium=member_android&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣3️⃣ The acceleration whiplash
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_the-ai-engineering-report-2026-the-ai-acceleration-activity-7459198398634733569-XTzE?utm_source=share&utm_medium=member_android&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣4️⃣ LLMs return Trendslop for Strategic advice
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_researchers-asked-llms-for-strategic-advice-activity-7460645767389818880-FXaN?utm_source=share&utm_medium=member_android&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣5️⃣ PR with (+10k, -4million) lines of code
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_from-the-claudecode-community-on-reddit-share-7461460781025488896-7RMV
2️⃣6️⃣ Example of anthropomorphizing (work conditions of the agent)
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_une-%C3%A9tude-r%C3%A9cente-de-stanford-r%C3%A9v%C3%A8le-que-activity-7462070011168288768-v4Cp
2️⃣7️⃣ Software developers who abandoned LLMs to go back to traditional
https://youtu.be/iXG_b1K8GK8?is=OGvvC-40wdZiku_s
https://youtu.be/pzkwn3hu1Cc?is=kzL6Dw9v0YW2q4uh
2️⃣8️⃣ Software project with strict anti-LLM policy and the reasons why
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_the-zig-projects-rationale-for-their-firm-activity-7462098611229638656-64xk
2️⃣9️⃣ Bun's migration from Zig to Rust : (+1million, -4k) lines of code in 11 days
https://www.linkedin.com/posts/fabricebernhard_1009257-lines-of-code-migrated-in-11-days-share-7460996434058805248-key-
m a huge fan of (the potential for) #LLMs and how they might revolutionize the nature of work and a huge critic of the trillion-dollar shill and #genAI-as-junior-developer / excuse for firing half your workforce that you over-hired over the last decade so really it is you who should be clearing your desk.
I am disgusted by the consequence-free wholesale IP theft and casual planet burning and excited by the potential for local models to do 99% of the heavy lifting.
I am happily speaking to various Claude models in a conversational way and painfully aware that it is in no way 'alive' or 'conscious' and merely acting as a mouthpiece for thousands of people's brilliant work that I am able to lean on.
At the same time.
Why is this so confusing for people? Perhaps we don't need polarized shrieking. Perhaps it is possible for people to contain multitudes.
A Series C fintech with ninety engineers, AI-native from day one, lost $4.2 million to a reconciliation function nobody on the team could explain. This is the labor-market thesis on what happens next: why technical autonomy — the ability to ship without a chat window — is becoming the new luxury status in software engineering, and why the engineers who never stopped knowing how the cement works are about to be the most expensive hires of the decade.
We unpack the concept of Comprehension Debt — the silent liability accumulating in every codebase where models write code and humans approve it without understanding it — and trace its consequences across three vectors: the broken senior-engineer pipeline, the bifurcating consulting market, and the compliance frameworks (SOC 2, PCI-DSS, HIPAA, ISO 27001, the EU AI Act, DORA) that refuse to accept "the assistant suggested it" as a control.
The post-AI developer is not the engineer who refuses AI. It is the engineer who can work as if AI did not exist.
━━━━━━━━━━━━━━━━━━ RELATED VIDEO ON THE CHANNEL ━━━━━━━━━━━━━━━━━━
If you want the actual rates, retainer structures, and the specific subfields where the post-AI premium is concentrating right now, watch:
- No-AI Developers Are Billing $250 An Hour — Here's the Market. The numbers are not theoretical. They are this quarter.
━━━━━━━━━━━━━━━━━━ CHAPTERS ━━━━━━━━━━━━━━━━━━
- 0:00 The Paradox of Ease
- 0:44 The Suffering Was the Curriculum
- 1:44 Comprehension Debt
- 2:37 Why Prompt Engineers Are Cheap
- 3:24 Compliance Cannot Terminate in a Model
- 4:19 The Elite Market Is Already Here
- 5:20 How To Be a Post-AI Developer
- 6:04 Cut Your Internet
- 6:51 The Cement Holds
━━━━━━━━━━━━━━━━━━ KEY CONCEPTS ━━━━━━━━━━━━━━━━━━
- The Post-AI Developer — an engineer who uses AI but can ship without it. Defined by autonomy, not abstinence.
- Comprehension Debt — the accumulating liability of merged code that no human on the team can explain. Compounds silently. Surfaces in production.
- The Junior Gap — the broken forging path. Juniors can ship in an afternoon what used to take three weeks. The map of how systems actually work is no longer being built.
- The Bifurcating Market — infinite supply of prompt-driven code generators on one side; vanishing supply of engineers who can debug a heap dump on an air-gapped server on the other.
- The Compliance Wedge — regulated industries (banking, healthcare, defense, payments) require a named human in the chain of responsibility. Language models cannot sign an audit.
- Technical Autonomy — the ability to think without a chat window. The new luxury status in engineering.
━━━━━━━━━━━━━━━━━━ FREQUENTLY ASKED ━━━━━━━━━━━━━━━━━━
What is a post-AI developer?
A post-AI developer is an engineer who uses AI tools but can work, debug, and ship without them. The defining test: if your internet were cut right now, could you still deliver? Post-AI developers say yes. Wrappers around a language model say no.
What is comprehension debt in software engineering?
Comprehension debt is the liability that accumulates when AI-generated code is merged into a codebase without any human on the team being able to explain why the code is structured the way it is. It compounds silently — the function works, the tests pass, until six months later it fails in production and nobody can reason about the original logic. Distinct from technical debt: technical debt is code you understand and chose not to clean up. Comprehension debt is code you shipped without ever understanding.
Why are engineers who don't use AI suddenly billing more than those who do?
Two forces. First, scarcity: the supply of engineers who can debug from first principles — without an internet connection, without a chat window — is shrinking while demand for incident response stays constant. Second, criticality: regulated industries (SOC 2, PCI-DSS, HIPAA, EU AI Act, DORA) cannot terminate the chain of responsibility in a language model. Scarcity plus criticality equals premium pricing. The market is already paying it.
Is being a post-AI developer the same as refusing to use AI?
No. Refusing AI is what the video calls "Luddite cosplay" — the market does not pay for performative purity. Being post-AI means using AI freely while remaining able to defend every line, notice every silent error, and continue working when the assistant is unavailable. It is a stricter standard than refusal, not a softer one.
How do junior developers become senior engineers in a post-AI world?
By deliberately rebuilding the map that AI removed. That means doing some work — debugging, reading source, reasoning about memory and network behavior — without the assistant, even when using it would be faster. The friction is the curriculum. Engineers who skip it can ship features but cannot own incidents, and the labor market is starting to price that distinction explicitly.
Depuis le 28 juin 2025, tous les sites et app web doivent être accessibles aux personnes en situation de handicap numérique. La seule façon de contrôler la conformité légale est l'audit RGAA qui est un processus lourd et reposant sur des centaines de tests manuels. L'IA peut-elle accélérer cela ?