This isn't a fully symmetric race, but its the right one to watch:
$TAO incubates subnets aiming for profitability.
$TIG already had verified breakthroughs aiming for commercialization.
One needs sustained usage to prove itself.
The other just needs one buyer to say yes.
My bet: TIG gets there first.
TIG breakthroughs are objectively verified before they ever reach a buyer.
Verification de-risks the sale. And derisked sales are what turn a protocol into a revenue story.
⏳️
Algorithms have never had a liquid, priced market before.
R&D has always been either siloed inside companies(Google's search ranking algorithm isn't tradable) or giving away free(open source, no capture).
$TIG's bet is that if you can objectively benchmark performance, you can create something like a commodities market for algorithmic efficiency.
And the token becomes the unit that prices "how much is this optimisation worth".
That's a real, non-trivial idea. Nobody else is doing exactly this.
A design most people won't clock until the market's already pricing it in.
FhtAbd retweeted
"The Simple Mathematics of Large Language Models" is a concise free paper if you want to understand the mathematics behind modern LLMs and systems such as ChatGPT and Claude.
I have shared several resources in the past that explain the mathematics behind these systems in detail, but they are often quite long. This paper is only about 20 pages, so I think it is a useful resource if you want a compact introduction without starting from a much larger textbook.
It covers token representations, context, attention as learned weighted averaging, projections and bilinear forms, multi-head attention, normalization, positional information, softmax, maximum likelihood, cross-entropy, and gradient descent. Together, these topics give a concise mathematical picture of how a language model works.
Save it to your bookmarks. It can be useful as a quick reference.
crc.business-school.ed.ac.uk…
Prometheus is an open-source system where the output isn't a tool, but the discovery itself.
That's not a better category. it's a new one.
$TIG 🚀
In science, it's called tacit knowledge, the stuff you learn from every failure, every dead end, every wrong turn. The stuff that never makes it into the paper.
@tigfoundation $TIG will launch Prometheus this week, a collaborative AI discovery system for algorithms. It's built with tacit.md to capture every failure, every strategy that didn't work, every lesson learned.
This will be the AI coordination system of the year. You won't regret cheking it out.
Anyone telling you tacit Know-how can't be protected is trying to build a monopoly.
Your experience took a lifetime to earn. Frontier AI absorbs it one prompt at a time, until enough of them add up to your entire way of thinking.
That's the fuel of the next monopoly.
Prometheus by $TIG will teach the world how to use AI to make real breakthroughts, withought giving away a lifetime of know-how for free.
Every monopoly in history is really just a bet on how long you can keep the world from catching up.
$TIG dosn't sell algorithms. It sells the world catching up faster, and charges the ones trying to slow it down.
Crypto has yet to fully get this, and that's exactly what's keeping TIG from a multibillion marketcap. Totally normal at this stage.
My bet: centralized companies will be licencing TIG algorithms before CT even understand what TIG is.
What TIG is building is already the trend over there, they just don't call it that yet.
For the first time, openess isn't the exception you opt into.
It's the default state of nature you now have to buy your way out of.
$TIG
What's in the licensing document everyone has been waiting for?
Its officially out now!!
github.com/tig-foundation/ti…
It covers the full path from innovation to revenue: how TIG patents its algorithms, the complete license structure from open to commercial, how community enforcement works and what free riders actually risk, and pricing principles for a market that has never existed.
Why $TIG?
The billion-dollar mistake already happened.
The trillion-dollar one is being built right now.
Tony Hoare added one shortcut in 1965.
NASA failed. hospitals crashed. Banks went down.
He called it his billion-dollar mistake.
Closed AI labs are making the same bet right now.
Except the algorithms aren't managing variable types.
They're managing supply chains. vehicles. financial systems. healthcare.
Hoare's mistake crashed programs.
This one will crush industries.
TIG is the opposite model, open competition, objective verification, no shortcuts, no black boxes.
The null pointer cost a billion dollars.
We can't afford to find out what this one costs.
A DEVELOPER STOOD UP AT A CONFERENCE AND APOLOGIZED TO THE ENTIRE INDUSTRY FOR ONE LINE OF CODE HE WROTE IN 1965. THAT SINGLE DECISION HAS SINCE CAUSED CRASHES, BREACHES AND ROUGHLY A BILLION DOLLARS OF DAMAGE AND YOU HIT IT EVERY SINGLE DAY
61 minutes from Tony Hoare -- the man who invented quicksort, won the Turing Award, and passed away in early 2026, leaving this as one of his most honest talks.
-> His confession: he added the null reference, letting any variable secretly mean "nothing", simply because it was easy to implement.
That one shortcut became the null pointer, the undefined, the None -- the crash waiting inside almost every program ever written.
He calls it his billion-dollar mistake, and he is not exaggerating. Whole classes of bugs and security holes trace straight back to it.
And this is the deeper lesson under the AI rush: the easy shortcut you ship today becomes the landmine everyone steps on for the next 40 years.
You thought null was just part of how code works. This is the talk where the man who made it tells you it never had to be.
Bookmark & Watch this one. You'll never type null the same way ↓
CT still arguing about tokens.
The centralized world is fighting over who controls the AI layer, aka who controls the world.
$TIG still underneath both fights.
Because the algorithm layer is the real battleground either side hasn't noticed yet.
When they do, it's a 1000x not priced in yet.
You want to know how bad the situation is?
Palantir is attaking OpenAI and Anthropic, arguing that the frontier model layer is tring to become a tax on every enterprise's private knowledge.
They claim enterprises don't want to rent intelligence from a potential future competitor.
While doing that, Palantir is planning to build a AI operating system to replace them.
Meanwhile, Spain just told state-backed companies like Telefónica, Indra, and Navantia to avoid new Palantir contracts over fears that sensitive national security information could be exposed.
France's domestic intelligence agency also terminated its Palantir contract.
Palantir is an American company co-founded by Peter Thiel, funded by the CIA's venture arm, and deply embedded in US intelligence and military operations.
It cannot credibly offer "sovereign AI".
The world needs neutral players. Not American. Not chinese. Just open.
Because the monopolies built by AI will not just be commercial or tech monopolies like what Google built last decades.
They will literally decide who lives and who dies.
$TIG
⚡️Karp is naming the real enterprise AI split.
The frontier labs sold intelligence as a universal product.
Enterprises are discovering that raw model access is not enough, and in sensitive environments it can become a sovereignty problem.
The issue is not only performance. The issue is control.
Who owns the model behavior?
Who owns the weights?
Who owns the prompts?
Who owns the data exhaust?
Who owns the workflow knowledge?
Who owns the company’s operational alpha after the model has observed it?
That is the center of the rant.
Karp is saying the frontier model layer is trying to become a tax on every enterprise’s private knowledge. Companies pay token fees, hand over context, expose workflows, and then fear the model provider can learn from, replicate, commoditize, or eventually compete with the very business it serves. Even if the lab says it will not do that, the trust problem remains because the customer does not control the full stack.
That is why Palantir plus Nvidia matters. The pitch is: keep compute, models, ontology, data, and operational logic under customer control. Use models as replaceable components. Do not let OpenAI, Anthropic, or any single frontier lab become the owner of the enterprise brain.
The strongest line is “they want to know they own the means of production.”
That is the whole architecture war.
In consumer AI, users tolerate dependence. In enterprise, battlefield, manufacturing, healthcare, intelligence, energy, and regulated finance, dependence is unacceptable. The model cannot be a black box that absorbs secret workflows and charges tokens forever. The model has to be embedded inside a controlled operational layer where the customer owns the data, governs the model, controls compute, switches providers, and preserves institutional alpha.
This is extremely bullish for Palantir’s ontology thesis.
Karp is basically saying the value is not just the model. The value is the application layer that turns models into usable, safe, governed action inside real institutions. That is exactly Palantir’s lane. Frontier labs provide cognition. Nvidia provides compute. Palantir provides the operating structure that makes cognition useful in environments where mistakes, leaks, hallucinations, IP loss, or data exposure are existential.
That is also why he is attacking token economics.
Token billing works when AI is a tool. It becomes offensive when AI is sold as transformation but the customer cannot see value, cannot control the stack, and suspects the vendor is capturing the business’s hidden knowledge. Enterprises do not want to rent intelligence from a potential future competitor. They want sovereign capability.
The AI bubble point is more precise than “AI is fake.”
Karp is not saying AI is fake. He is saying the frontier-lab business model may be mispriced, overtrusted, and structurally wrong for serious enterprise deployment. Compute plus application layer plus model is real. Raw model subscription sold as enterprise transformation is weaker. That is the knife.
So the likely outcome is not enterprise AI demand collapsing. The likely outcome is enterprise AI spend migrating away from generic chatbot/token subscriptions and toward controlled stacks: private compute, open or controllable models, Nvidia infrastructure, secure application layers, ontology, governance, auditability, and domain-specific deployment.
That is bullish for Nvidia.
Bullish for Palantir.
Bullish for sovereign AI stacks.
Bullish for open models when paired with secure deployment.
Bearish for frontier labs that assume they can own the customer relationship, the model layer, the data interface, and the economics forever.
The deeper geopolitical read: battlefield AI cannot be outsourced to consensus Silicon Valley. Karp is saying American warfighting, critical infrastructure, and industrial command systems need AI, but they cannot be dependent on model providers whose governance, safety ideology, product incentives, and data posture are misaligned with sovereign use. That is why he keeps saying Department of War, Ukraine, Israel, critical infrastructure. He is framing Palantir as the patriotic enterprise operating layer between uncontrolled frontier labs and national power.
The cleanest read:
The AI stack is splitting.
Model layer: powerful but commoditizing and politically distrusted.
Compute layer: scarce, profitable, strategic.
Ontology/application layer: where enterprise value, trust, control, and workflow ownership live.
Karp is arguing that the model companies overplayed their hand by acting like the model owns the future. Enterprise customers are realizing the future belongs to whoever controls the operational layer around the model.
That is exactly Palantir’s thesis.
And he is probably right.
Why does Big Tech happily release tools like Claude science...
While keeping their most powerful AI-driven algorithm discovery systems locked in secret vaults?
Because they don't want you to have the real weapons.
They want to own the future of intelligence.
$TIG is about to piss them off.
Prometheus drops soon: an AI system that will mine breakthrough algorithms at scale.
Open source everything, and make them available to everyone.
The agent coordination system itself will be open source, so anyone can build on it.
This is the direct opposite of Big Tech's greedy, extractive monopoly strategy.
Prometheus will be even more powerful than Big Tech's closed algorithm discovery systems, powered by open collaboration, while their walled gardens will hit the island closure ceiling.
