
co-founder https://xitter.cf/t.co/RjmHjl1Qvj // μῆτις | know thyself | #gameb
London, England
Joined November 2012
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Language models are brilliantly trained to minimize surprise (eg predict the next token). They’re wildly good at it, and getting cheaper every day.
But all real value (anything meaningful) exists only in the surprise.
Not just any surprise though. Surprise that turns out to be “right” before it’s common knowledge. Meaning there’s risk, it’s messy, and it’s not at all efficient.
As intelligence gets cheaper (eventually becoming practically free), the only scarce (and valuable) thing left is knowing what's actually worth doing before it’s obvious.
This can only come from real choices, made by real operators and creators, over long periods of time, with real stakes.
Which is why wisdom was never an asset one person holds. It's a property of networks, comprised of independent individuals (eg unique observers), where it emerges between them and validated by the collective.
David Weinstein ⋈ retweeted
New article on @drmichaellevin's viral paper.
His paper is the most important thing that's happened to biology in about 70 years. Up their with DNA discovery - maybe... who knows... I have bias.
observertheory.substack.com/…
all roads lead to kayos.ai
lol just seeing the full circle back to @davidweinstein
David Weinstein ⋈ retweeted
The Inevitability of the Company AI Harness
At some point in the not too distant future (probably less than 6 months), the CEO of every business will be faced with answering this question: would you like to one-click install superintelligence into your business?
When we reach this point, adopting AI will no longer be a process of asking individuals in the business to use Claude Cowork or ChatGPT Work to help them be more efficient in their work. Instead, it will be a decision that management teams and boards make for the entire organization. The harness will be installed at the company level, and the harness will enable AI models to drive their own adoption and deployment into the business.
With access authorized at the highest levels, the company harness will:
1) Connect to all of the systems of record that contain the artifacts and assets generated by the operations of the business: email and chat, document storage, source code repositories, accounting systems, CRM, marketing campaigns, purchase and revenue data.
2) Ingest and analyze all of the external-facing activity that matters to the success of the business in its market.
3) Use the data it finds in these systems to identify and synthesize the skills needed to replicate the external-facing activities of the business to human-level quality or better.
4) Correctly assess that the vast majority of internal-facing activity in large companies is make-work, and should not be replicated.
5) Find the narrow set of processes that do actually drive the critical external outcomes, and seek to replicate and improve those.
6) Treat the creation of the automated skills and workflows that matter as a reinforcement learning learning problem.
7) Use historical data as the reference set and build itself a loop that trains a skill (or perhaps eventually a bespoke model) to deliver superhuman performance in that task.
You might say that AI will never be able to learn how to do all the things a company does without humans hand-crafting the skills or teaching it their proprietary processes. That take will not age well.
You might feel that this is a dark future. I don’t agree, but it won’t matter. The incentives to adopt the company harness will be too strong to resist.
You might worry that this will be massively disruptive to the way that most companies operate, and you will be right.
When company harnesses land, the nature and pace of AI adoption will be a difference in kind, not merely degree.
Up until this point, AI diffusion has been limited by a natural brake: the rate that humans in an organization can absorb new tools, translate their workflows into agent skills, and supervise/direct the operations of their agents.
The introduction of the company harness will remove that brake. It will be designed for a world where AI is doing the work for us instead of doing the work with us.
The company harness will bypass the rate limits of organizational diffusion in favor of a recursive self-improvement loop that is bottlenecked only by computational constraints: tokens, data, and eventually, energy.
It will be simultaneously be both thrilling and terrifying, depending on how you position yourself.
Some will seek to accelerate it, others will seek to slow it down, but either way adoption will be inevitable.
🤖 Made with AI
Better agents and faster loops can let you make more decisions and take more powerful actions… but what about observation and especially orientation
David Weinstein ⋈ retweeted
Palantir CEO Alex Karp predicts the future business landscape will be defined by companies that got sovereignty right competing against the frontier labs:
“I am quite certain that the companies that get this right will just be the dominant companies, because de facto, it'll be you competing against a frontier lab.”
“And you can win because you have access to things that are outside the spectrum, that are outside the model, that are proprietary, that only you understand.”
“Or you can selectively use their models, but make sure that alpha is not being migrated out by using an application layer or what is commonly now called an ontology.”
David Weinstein ⋈ retweeted
Perhaps the deepest version of his calling is not to be recognized by a Renaissance but to help build the infrastructure through which a Renaissance becomes possible.
One of the most valuable datasets will be emergent edge case problems from the long tail of various distributions and this can only come from a network of nodes that clearly and continuously understands themselves
Value isn’t ever in the explicit, it’s in the implicit use of the explicit, it’s everything before and after and around, and it’s nothing from within, even though what’s inside is what allows any of it to happen
David Weinstein ⋈ retweeted
This has taken ages. New article: Computational Theology, The Genesis Algorithm - link in replies
Meeting with investors in NY (Aug 3-4), LA (Aug 5-7), and SF (Aug 7-15) ... DM for more info / schedule time if interested!
All value accrues to the esoteric, to what’s hidden, perceived but not explained, to that which can’t easily be captured in explicit language. Since value represents the process of explicating the illegible, it’s always found within the weird and unknown
There’s really just no more value in “by the book” and this is a good thing, actually, where what matters so much more than ever is anything that goes against the grain and that’s illegible, which will be harder and harder to do in our optimized world but increasingly valuable
Language always locally compresses entropy, which then either expands globally or not, like the difference between symbolism vs mechanism, even Twitter vs LinkedIn
We’ve evolved to reduce entropy against a backdrop of ever increasing entropy, everything ultimately exists for that. But no one actually likes the process of reducing it, we love exploring it and we especially love when it’s reduction allows us to explore more and new types
AI as "entropy reduction as a service" explains a lot and super valuable as infrastructure, but less so as a totalizing end state and meaningless without an observer selecting what entropy to reduce
No amount of left hemispheric tech can make up for a lack of right hemispheric intuition, but with the right relationship can significantly scale it









