[ExI] [Extropolis] Was Musk Smart to pay $44 billion for Twitter?

[Keith Henson]

The GPT response is most interesting.

But it misses something. Musk could have bought a full Twitter feed for
much less than $44 billion.

Owning Twitter/X though, gives him freedom to post whatever he wants and
the ability to shut down anything that puts him in an unfavorable light.

Is that worth $44 B? Maybe it is to Musk.

Keith


On Wed, Aug 12, 2026 at 6:48 AM John Clark <johnkclark at gmail.com> wrote:

> *I asked Mr. GPT the following question: "Some people have claimed that
> Elon Musk was smart to pay $44 billion for Twitter because he could use it
> to train his AI, and I can see how in the early days the data from Twitter
> would be helpful in training an AI about how language works, but today we
> are far beyond that point, AIs already know how do use language. Twitter
> (a.k.a. X) is not exactly known for its close reasoning or as a well of
> rocksolid facts; so it seems to me that today if you train an AI on Twitter
> you'll probably make it stupider, not smarter. What do you think?"*
>
> *This is how Mr. GPT responded: *
>
>
> GPI: "I think your intuition is *substantially right*, with one important
> qualification: *X is probably much more valuable to an AI company as a
> real-time information source and as a source of human-feedback data than as
> a giant corpus for teaching an LLM how to reason.*
>
> And there's an interesting distinction between *"Twitter was valuable
> when Musk bought it"* and *"Twitter's data justified paying $44 billion
> for Twitter."* Those are very different claims.
> 1. You're right that the original linguistic argument has largely
> disappeared
>
> In the early days of language models, enormous quantities of ordinary
> human-written text were enormously valuable. Twitter contained:
>
>    - billions of examples of informal language
>    - slang and abbreviations
>    - conversations rather than just polished prose
>    - multilingual material
>    - rapidly changing vocabulary
>    - arguments, jokes, sarcasm, etc.
>
> There is still some specialized value in that. Researchers have
> demonstrated that models specifically trained on Twitter can become better
> at Twitter-like language tasks. Twitter itself built models from billions
> of tweets for precisely this reason.
>
> But that isn't the same thing as saying Twitter is particularly good *general-purpose
> training data for frontier reasoning models*.
>
> By 2026, the frontier-model problem isn't primarily:
>
> "How do we teach the neural network what English looks like?"
>
> It's increasingly:
>
> "How do we get the model to reason correctly, use tools, learn from
> experience, and reliably distinguish truth from falsehood?"
>
> And *Twitter/X is not an especially good source for the latter*.
> 2. In fact, there is evidence for exactly the problem you're describing
>
> A particularly interesting 2025 study found that training language models
> on large amounts of *low-quality social-media material* could actually
> make them worse at retrieving accurate information and reasoning. *Nature*
> summarized the finding under the wonderfully appropriate headline *"Too
> much social media gives AI chatbots 'brain rot.'"*
>
> The underlying issue is straightforward.
>
> Suppose your training corpus contains:
>
> "Scientists discover that X causes Y!!!"
>
> followed by thousands of people repeating it, arguing about it,
> embellishing it, making memes about it, and eventually producing a dozen
> contradictory versions.
>
> A language model doesn't automatically know that the original scientific
> paper is epistemically superior to the 10,000 tweets.
>
> *Popularity is not truth.*
>
> And X is particularly problematic because its reward structure selects for
> things such as:
>
>    - outrage
>    - novelty
>    - emotional intensity
>    - tribal signaling
>    - humor
>    - confident assertions
>    - political controversy
>    - engagement
>
> Those are almost the opposite of the characteristics you'd want in a
> corpus intended to teach *epistemic reliability*.
>
> So I would actually sharpen your argument:
>
> *Training indiscriminately on X isn't merely inefficient; sufficiently
> indiscriminate training can teach a model undesirable statistical habits.*
>
> That doesn't mean *all* X data is bad. It means *data selection becomes
> crucial*.
> ------------------------------
> 3. But X has something that Wikipedia and books don't: the real-time human
> world
>
> This is where Musk's acquisition makes considerably more sense.
>
> The really valuable property of X isn't necessarily:
>
> *"Here's 10 petabytes of text with which we can pretrain an LLM."*
>
> It's:
>
> *"Here's a gigantic continuously updating sensor network consisting of
> millions of humans talking about what's happening right now."*
>
> That's potentially extremely valuable.
>
> Imagine something happens at 9:03 AM:
>
>    - an earthquake occurs
>    - a rocket explodes
>    - a politician says something
>    - a company announces a product
>    - a sporting event ends
>    - a strange phenomenon is observed
>    - a local disaster occurs
>
> People may be posting about it on X within seconds.
>
> xAI explicitly identified this as a major advantage from the beginning.
> When Grok was introduced, xAI described its access to X as giving it *real-time
> knowledge of the world*.
>
> And that remains part of Grok's architecture today. X says Grok can decide
> whether to search public X posts and the web when answering questions.
>
> That's much more interesting to me than using X as a giant English
> textbook.
> ------------------------------
> 4. There's an even more interesting resource: the interaction data
>
> This is potentially much more valuable for AI development.
>
> Consider what happens when millions of people interact with an AI:
>
> *User:*
> "What's the latest information about the hurricane?"
>
> *Grok:* gives an answer.
>
> *User:*
> "That's wrong. The National Hurricane Center just changed the forecast."
>
> Then the user provides the relevant information.
>
> That produces a potentially useful training example:
>
> *question → model response → human correction → better response*
>
> Multiply that by millions of interactions.
>
> X's current policy explicitly says that X may share public X data *and
> users' interactions, inputs and results with Grok* with xAI for training
> and fine-tuning.
>
> That is a considerably more compelling AI asset than simply having a
> warehouse containing old tweets.
> ------------------------------
> 5. And there's a third resource that I think is particularly valuable: the
> social graph
>
> This is something that is easy to overlook.
>
> X doesn't merely contain:
>
> text
>
> It contains:
>
> *people + text + relationships + reactions + timing + engagement +
> communities*
>
> For example:
>
> *Person A* posts a claim.
>
> *10,000 people* react.
>
> *Person B* provides a correction.
>
> *Person C* provides a link to the original source.
>
> *Person D* disputes the correction.
>
> *Community Notes* eventually attaches a qualification.
>
> That's potentially an enormous dataset about *how humans collectively
> evaluate information*.
>
> Twitter researchers were already exploiting the social graph and
> engagement information in specialized language models before Musk bought
> the company. One Twitter research project used billions of tweets plus
> social-engagement information to improve multilingual representations and
> social-media understanding.
>
> That could be useful for building AI systems that understand *human
> behavior, information propagation, persuasion, disagreement and collective
> sense-making*.
>
> Those are very different objectives from "learn English."
> ------------------------------
> 6. But there's a nasty catch: X can also become a feedback loop
>
> This is perhaps the biggest problem.
>
> Suppose you train an AI on X.
>
> Then you deploy that AI on X.
>
> The AI generates enormous quantities of text.
>
> People react to that text.
>
> You collect the resulting X data.
>
> You train the next AI on it.
>
> Now you're increasingly training an AI on *AI-generated material produced
> by previous generations of AI*.
>
> That's potentially dangerous.
>
> It's analogous to repeatedly photocopying a photocopy. If you don't
> carefully preserve high-quality original material, the statistical
> distribution can progressively deteriorate.
>
> And this isn't merely theoretical. Modern AI training increasingly has to
> worry about distinguishing *human-generated, verified data from synthetic
> data*.
>
> So I would absolutely *not* recommend:
>
> "Take everything posted on X and dump it into the next frontier model."
>
> I'd recommend something much more like:
>
> *Use X as a gigantic raw sensor, then aggressively filter, verify, rank
> and annotate the useful information before using it.*
>
> ------------------------------
> 7. So was Musk smart to pay $44 billion?
>
> This is where I would be much more skeptical.
>
> There are really three separate propositions:
>
> *A. X contains valuable data.*
>
> Yes.
>
> *B. X gives xAI capabilities that would otherwise be difficult to obtain.*
>
> Definitely yes—particularly real-time information, social interaction data
> and an enormous stream of human feedback. X itself confirms that its data
> is being used to train and improve Grok.
>
> *C. Therefore X was worth $44 billion because of its value as an
> AI-training dataset.*
>
> *That does not follow.*
>
> The value of the raw text corpus by itself is almost certainly not
> anywhere remotely close to $44 billion.
>
> And here's the really important point: *a lot of the information on X is
> publicly observable anyway.* You don't necessarily need to own the entire
> company to obtain useful information from it. You can license data, crawl
> public material where permitted, use APIs, search it in real time, etc.
>
> Owning X gives xAI *privileged integration, enormous scale, control over
> the platform, user interactions, recommendation systems, social graph, and
> potentially the ability to change the product to serve AI purposes.*
>
> Those things could be worth a great deal.
>
> But that's a much stronger and more complicated investment thesis than:
>
> *"We paid $44 billion for a gigantic pile of tweets that we can use to
> train our LLM."*
>
> I would regard *that particular justification as pretty weak*.
> ------------------------------
> And there's an amusing irony
>
> The strongest version of Musk's strategy may actually be almost the
> opposite of what your friend is suggesting.
>
> The valuable pipeline isn't necessarily:
>
> *X → training data → smarter LLM*
>
> It may be:
>
> *X → real-time observations of the world → AI searches/interprets them →
> humans correct AI → verified interactions → better AI*
>
> In other words, *X may be more valuable as an experimental laboratory and
> real-time sensory organ for an AI system than as its textbook.*
>
> And given where frontier AI is in 2026, I think that's a much more
> defensible argument."
>
>
> *John K Clark*
>
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