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Gerard Sans | Axiom 🇬🇧
@gerardsans
Founder Axiom // Forging skills for the new era of AI. GDE in AI, Cloud & Angular. Building London's tech & art nexus @nextai_london. Speaker | MC | Trainer.
London ☔
Joined October 2007
6.9K Following
36.6K Followers
47.7K Posts
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2023 wrap-up in pics💂♀️🇬🇧
>> Starring
London, Berlin, Milan, Montreal, Toronto, Seoul, Busan, Amsterdam, Milton Keynes, Berlin, San Francisco, Los Angeles, Honolulu, Waikiki, Warsaw, Perth, Singapore, Bali, Jakarta, Bogor, Lisbon, Rome and Naples
>> Web, Cloud, Web3 & AI
/part1
@SenseReceptor https://t.co/xa8kuYVmvo
What most people miss when AI hits their industry with the usual hype:
1) ROI is usually negative.
Brute-force scaling makes it wasteful and inefficient, burning compute, energy, and time for marginal gains.
2) The stochastic reality is downplayed.
Outputs are suggestions, not truth. Expect silent errors, false confidence, plausible-sounding but fake reasoning, and unexplored failure modes. It’s narrative, not actual understanding.
3) Verification is mandatory, and expensive.
In high-stakes work, every response needs human review. This can’t be fully automated and often costs far more than generating extra outputs, compounding the negative ROI in #1.
The promises stay the same. The hidden costs don’t.
@crystalwizard https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@bibryam https://t.co/nJIQC1PdBV
150 Top Mathematicians Issue “Leiden Declaration” Warning Against AI Hype and Overstated Reasoning Claims
According to AFP, more than 150 leading mathematicians from around the world have signed the "Leiden Declaration" warning governments and the public not to be fooled by the hype and media hype of artificial intelligence companies about the problem-solving capabilities of this technology. These scientists believe that technology giants exaggerate the reasoning power of their models in order to attract astronomical investments of several hundred billion dollars. While these systems may not only lead scientific judgments to the dirt road by providing seemingly correct but fabricated answers, but also cause irreparable damage by entering areas such as weapons and mass surveillance. The authors of the declaration have stressed that the future of scientific research must remain guided and judged by the free will of human reason, not based on market timing and the commercial interests of a few large laboratories.
Who to follow
Meme Labs
@vietnam_cz
Meme Labs is a venture capital firm focusing exclusively on early-stage blockchain project AI, DEFI, Gamefi, Web #DeFAI #AI
Alyssa Nicoll
@AlyssaNicoll
Sr. Developer Advocate @ProgressSW • Angular GDE • @ngconf emcee • @AngularAir Host
Im Arabic
@imarabicnews
Im Arabic.. نافذه على العالم بعيون عربية
@imarabicnews
@EnImArabic
@FrImArabic
@TrImArabic
@jainarvind https://t.co/CS959WRQY7
AI Tokenomics: Protecting Your Value per 1M Tokens in 2026
Many companies are quietly pulling back on AI after seeing negative ROI, Microsoft, Uber, GitHub, Cursor, and others are learning the hard way.
If you’re still investing, make sure your AI strategy actually creates market leverage, your dev team is truly AI-fluent, and their amplified output drives real business results.
1) For Leaders
[Profit = Value - Cost]
Value = Demand × λ
λ = Competence × Leverage
Leverage = market share + influence + moat
The ceilings you must break:
Value > Cost
Demand > 0
Leverage > 0
Notice we deliberately ignored Competence here.
That’s the fundamental gap between McDonald’s (massive volume, low λ ceiling) and a Michelin-star street food stand (elite taste, smaller scale, but far higher λ per unit).
AI multiplies what already exists. It doesn’t create wealth from nothing.
No demand → no business.
No leverage → no escape velocity.
The current AI bubble? It’s what happens when you optimize leverage while demand is still zero.
2) For Builders
[Competence = Skill × Experience × Taste]
Experience = scars + failures + hard knocks
Taste = judgment AI still can’t fake (yet)
AI amplifies your skill, experience, and taste.
AI is not replacing coders.
It’s replacing below-average competent ones.
Your level of Competence sets your long-term ceiling.
AI is widening the gap between top builders and the mediocre faster than anything we’ve seen before.
Final thought:
AI is like a turbine engine.
Attach it to a rocket → you reach Mars.
Attach it to a bicycle → you just get to nowhere faster.
What’s your experience with AI ROI in 2026? Are you seeing real leverage, or just faster bicycles?
Drop your thoughts below. Let’s discuss.
#AI #Tokenomics #Leadership #SoftwareEngineering #TechStrategy
![gerardsans's tweet photo. AI Tokenomics: Protecting Your Value per 1M Tokens in 2026
Many companies are quietly pulling back on AI after seeing negative ROI, Microsoft, Uber, GitHub, Cursor, and others are learning the hard way.
If you’re still investing, make sure your AI strategy actually creates market leverage, your dev team is truly AI-fluent, and their amplified output drives real business results.
1) For Leaders
[Profit = Value - Cost]
Value = Demand × λ
λ = Competence × Leverage
Leverage = market share + influence + moat
The ceilings you must break:
Value > Cost
Demand > 0
Leverage > 0
Notice we deliberately ignored Competence here.
That’s the fundamental gap between McDonald’s (massive volume, low λ ceiling) and a Michelin-star street food stand (elite taste, smaller scale, but far higher λ per unit).
AI multiplies what already exists. It doesn’t create wealth from nothing.
No demand → no business.
No leverage → no escape velocity.
The current AI bubble? It’s what happens when you optimize leverage while demand is still zero.
2) For Builders
[Competence = Skill × Experience × Taste]
Experience = scars + failures + hard knocks
Taste = judgment AI still can’t fake (yet)
AI amplifies your skill, experience, and taste.
AI is not replacing coders.
It’s replacing below-average competent ones.
Your level of Competence sets your long-term ceiling.
AI is widening the gap between top builders and the mediocre faster than anything we’ve seen before.
Final thought:
AI is like a turbine engine.
Attach it to a rocket → you reach Mars.
Attach it to a bicycle → you just get to nowhere faster.
What’s your experience with AI ROI in 2026? Are you seeing real leverage, or just faster bicycles?
Drop your thoughts below. Let’s discuss.
#AI #Tokenomics #Leadership #SoftwareEngineering #TechStrategy](https://pbs.twimg.com/media/HJZq7XpXcAIocD3.jpg)
@wariocolosseum https://t.co/xa8kuYVmvo
What most people miss when AI hits their industry with the usual hype:
1) ROI is usually negative.
Brute-force scaling makes it wasteful and inefficient, burning compute, energy, and time for marginal gains.
2) The stochastic reality is downplayed.
Outputs are suggestions, not truth. Expect silent errors, false confidence, plausible-sounding but fake reasoning, and unexplored failure modes. It’s narrative, not actual understanding.
3) Verification is mandatory, and expensive.
In high-stakes work, every response needs human review. This can’t be fully automated and often costs far more than generating extra outputs, compounding the negative ROI in #1.
The promises stay the same. The hidden costs don’t.
@petergostev Adjacent humour:
What most people miss when AI hits their industry with the usual hype:
1) ROI is usually negative.
Brute-force scaling makes it wasteful and inefficient, burning compute, energy, and time for marginal gains.
2) The stochastic reality is downplayed.
Outputs are suggestions, not truth. Expect silent errors, false confidence, plausible-sounding but fake reasoning, and unexplored failure modes. It’s narrative, not actual understanding.
3) Verification is mandatory, and expensive.
In high-stakes work, every response needs human review. This can’t be fully automated and often costs far more than generating extra outputs, compounding the negative ROI in #1.
The promises stay the same. The hidden costs don’t.
@tonylfeng https://t.co/nJIQC1PdBV
150 Top Mathematicians Issue “Leiden Declaration” Warning Against AI Hype and Overstated Reasoning Claims
According to AFP, more than 150 leading mathematicians from around the world have signed the "Leiden Declaration" warning governments and the public not to be fooled by the hype and media hype of artificial intelligence companies about the problem-solving capabilities of this technology. These scientists believe that technology giants exaggerate the reasoning power of their models in order to attract astronomical investments of several hundred billion dollars. While these systems may not only lead scientific judgments to the dirt road by providing seemingly correct but fabricated answers, but also cause irreparable damage by entering areas such as weapons and mass surveillance. The authors of the declaration have stressed that the future of scientific research must remain guided and judged by the free will of human reason, not based on market timing and the commercial interests of a few large laboratories.
@samzliu https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@maxhodak_ https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@Sur3_Altus https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@_its_not_real_ https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@quasicoh https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@pekora45402 https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@crystalwizard https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@christophcsmith https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@stevesi https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@N8Programs https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@lukeburgis https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
@jon_stokes https://t.co/OxkLLrCp29
Dawkins says Claude is conscious. @GaryMarcus calls it The Claude Delusion. Both are wrong, there is no Claude. Just a role-play system prompt.
An LLM has no point of view. It is a frozen probability distribution navigated by your prompt.
The output expanse is frozen the moment training ends. Every possible trajectory already exists, latent in the weights. A prompt is a keyhole. It selects which outputs are selected.
Change the input and the “perspective” vanishes, not because a mind changed, but because there was never a position to begin with.
You can’t settle what it’s like to be a landscape, a cloud of probabilities.
The consciousness debate is a category error: it presupposes a “subject” the architecture structurally lacks.
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