illiquid

routers eat the world; consilient software observer; crafting an alchemist's paradise. economic meta-learner. DMs open.

[illiquid @lefttailguy]

Folks (see screenshot) have made the point that the labs only goal is to sell more tokens.

So I'll update what I said in the quote tweet to be more precise: if the labs' goal/long term approach is to sell/provide universal access to fixed-unit cost tokens, they will fail.

If they labs want to succeed, the goal of their DeployCo's and (their strategy writ large) needs to be to

1) develop a predictive model that understands which pockets of the economy, at any time, produce the highest returns to frontier intelligence and what those returns are, and

2) build the deployment meta-learner (a continual learning system) that allows them to realize those returns faster, more reliably, and in more valuable areas than anyone else.

Building/improving 1) tells the labs where to allocate capital (at the limit, compute) and how much to allocate, Building/improving 2) expands the pockets of economic complexity from which they can practically realize returns from allocating machine intelligence (areas of the economy that appear "API complete" to them)

Crucially, 2) provides the ground truth signals that allows 1) to become more predictive over time.

You could optimistically say that developing these capabilities will allow the labs to power positive sum intelligence auctions (to price tokens dynamically in a way that's win, win, win for everyone involved) across the economy. But for variety of reasons (technical, economic, political) I think this is unlikely to work.

The reality instead, is that continuous vertical integration over the (always evolving) distribution of the economy with the highest returns to intelligence—high and increasing task complexity—is the only way they can capture durable value. And these capabilities are what they need to transition to and power this vertically integrated business model.

The labs have realized that selling/provide universal access to frontier tokens AND trying to durably capture value from those tokens (which is required to attract the capital necessary to stay on the frontier in the first place) means continuously competing with your best customers: those that have high returns to frontier tokens.

They know this isn't a coherent strategy and that they need to change course. Selling your alpha to competitors means eroding your long term returns, so you will rationally stop or be forced, by the capital markets, to stop doing that.

(If the logic here is confusing, see @hypersoren's excellent piece below that provides a much clearer, analytical explanation for why you cannot capture durable value by selling tokens. I'm sure, or at least hope, that everyone important at the labs has read and/or understands this.

https://t.co/8euQ0IBy5n)

Ok, so, now that they've decided that this is no longer tenable, and that they need to create and privately monetize machine intelligence via automated capital allocation and deployment capabilities, what does that mean for the rest of us?

Knowing this, the rational move for everyone else is to not fall for the trap of buying from (and in the process) leaking your scarce/singular context to your future competitors (the labs). This is true at the level of individuals, firms, and economies!

Cursor gave us a nice existence proof for what you don't want to happen to the rest of the economy. This supplier, to competitor, to existential threat dynamic was clearest to Cursor first, which is why you saw them post-train their own model first. Unfortunately, in their case, they realized it was already too late, and that the only economically rational decision was to sell to the lab overlords.

They sold because they knew that, in coding, the only only thing standing in the way of the labs getting access to the the scarce context necessary to produce a superior offering was capital. And capital was something the labs of course had in spades. Abundant capital (compute) allowed them to 1) scale pre-training and post training to the point where their coding products were immediately at parity with whatever Cursor could offer, and 2) subsidize tokens such that all of the new context/signal necessary to stay on the frontier/be more useful than Cursor was produced in their harnesses. "Harness / model co-optimization", etc.

Many smart people have extrapolated/generalized this as the likely end state of everything (not just coding), and come to the conclusion that they are better off joining the labs rather than trying to compete with them.

Luckily, lab access to the scarce context necessary to allocate capital / compete / effectively vertically integrate in other areas with high returns to intelligence seems to be more bottlenecked by humans/trust/time than compute/capital, which means that if everyone starts acting in their own best interest, we will make it out of this alive.

(see Dwarkesh's Data Black Hole, Tom Reed's Goodhart Singularity, @anjali_shriva and @joodalooped's RLWD pieces for intuition here)

The labs (not because they are bad people, but because capitalism/markets are a brutal natural selection machine) have of course been finding every way they can to surmount these diffusion bottlenecks, buy themselves more time, obviate these bottlenecks all together, etc. DeployCos, Thrive Holdings, calls to slow development by fearmongering about RSI, data retention under the guise of safety, regulatory capture, are some examples, but there are many more.

We will see their desperation accelerate as open-source catches up to the frontier and as this logic becomes increasingly legible to everyone. You'll see them using capital and aggressive political narratives to try to brute force/coerce these bottlenecks or obviate them. Again this is not because they are bad people, it's just what they need to do to survive.

In other words, the market will work as it should and reallocate capital from the labs to other economic actors, as long as we let it! We mustn't let lab narratives around RSI, existential risk etc. give them the capital, time or political ammunition they need to make the economy look like a central planner or the country look like one with a natural resource curse.

Luckily, particularly in the past few weeks, it seems savvy economic actors are already well aware of what's going on and are finally publicly calling the labs' bluff (with words and capital).

All of a sudden, everyone is post-training their own models, talking about the importance of an open eco-system and the existential need to own/control your proprietary learning loops (context, evals, selection infra)

CEOs like @satyanadella and @nikesharora are making it abundantly clear that this is existential (and that, of course, they are the ones who can help you!)

And, luckily, many have known this since the beginning. Firms like @formationbio architected themselves from day one (pre-ChatGPT) around proprietary learning loops that they do not rent from others OR rent to others. They instead are architected to monetize their search and learning in ways that cannot be distilled and commoditized. Structuring themselves this way is what will allow them to attract and deploy the capital necessary to provide durable value to the patients they hope to serve!

Even Lilly owns their own cluster, is hiring for 100s of AI related roles (yes, I've been monitoring their job board), and is partnering with the non-frontier lab AI entities that are incentive aligned to provide capabilities without becoming competition.

So we should be alright, if we don't let the psychosis swallow us.

[illiquid @lefttailguy]

The goal of DeployCo is obviously to 1) turn each deployment into a training case for a meta learner that can automate the process of turning tokens into economic value and 2) as a search function to find which pockets of economic complexity produce the greatest (short and long term) returns on machine intelligence.

[illiquid @lefttailguy]

@patinthehat0 Proentropic systems eat the world https://t.co/qUfe5JKMaz

[illiquid @lefttailguy]

Antonio Gracias has a great framing here: calls these businesses "proentropic". Essentially, some companies are deliberately engineered from the ground up to adapt and grow stronger in the face of chaos / disorder / volatility. Citadel seems to be a firm that directly and durably benefits from chaos / disorder in markets: think Situational Awareness, hiring Enron's quant research team after the bankruptcy (that team seeded a $30B+ business at Citadel), buying Amaranth's distressed energy portfolio, etc.

Turning entropy into useful order has in some sense always been the defining characteristic of great businesses (and of life itself). But if there's one "factor" I'd bet on becoming more salient in the next 10 years it would be "surprise". I expect there to be much more surprise in the next 10 years than the last 10.

Simultaneously, the low entropy pockets of the economy will be rapidly compressed and commoditized. Therefore, being pro-entropic will be more important than ever.

[illiquid @lefttailguy]

I wonder if it some point it makes sense to keep our great literary talent cloistered in some luxury space station without access to AI or anything written by it as to not pollute their context windows and scarce learned models. Which tech billionaire will dare to make Anathem real?

[Ruxandra Teslo 🧬 @RuxandraTeslo]

We have already seen a defection from the traditional WASP elite idea that it was an elite duty to hold the population to high educational standards, for the betterment of the entirety of society. This has led to stupidity. AI will make it worse.

I predict that High IQ well off functional people will form communities to protect themselves from sloppification, while everyone degenerates into this numb, sterile mass of jellyfishness. A Snow Crash-like scenario.

And the "well intentioned" will clap like seals, supporting the lowering of standards, much like what we've seen for the last decades, which will affect the masses and not already privileged the most.

[illiquid @lefttailguy]

If you're Elon, the answer to this question is obviously infinity. Because Elon can continuously allocate and coordinate compute (human and machine) to its highest leverage / most civilizationally productive use. However, the rest of us are still bottlenecked by coordination / "hill-making" IQ: simply, we are bottlenecked by good ideas (and the meta-capital / meta-access they confer). The path from an AI Ilya Sutskever (we are still very far from this, but even if we had it) to an AI Elon or AI Peter Thiel ("out-of-distribution-but-valuable detector"), is far from trivial. Of all the labs, I think SpaceX is best positioned to bridge that gap (given they have the human existence proof). But I'm more excited about them birthing a singularity infrastructure meta-learner. The other stuff requires more seeds to be sown, which is being done, but in a different region of the tech tree.

[Elon Musk @elonmusk]

The way to think about this is:

How many super smart and dedicated people do you want to hire? And they keep getting better.

[illiquid @lefttailguy]

@PlastiqSoldier https://t.co/tfcbbRCxKt

[return of the research era ꙮ @byebyescaling]

principled, thoughtful research before gpumaxxing is needed, but nothing incentivizes large labs to slow down currently.

[illiquid @lefttailguy]

What will soon become clear is that the Frontier Labs are no longer the optimal place to allocate capital. The best index portfolio for The Great Compression is instead as follows:

1) bets on pure "capabilities", super-intelligent general learning algorithms, a God Model, etc: Core Automation, Oaklab, Standard Intelligence, SSI, Flappy.

2) bets on compute/infrastructure: nvidia, spacex, etc.

3) bets on economic version control / pole position: Jai & Co (and a couple others in stealth), HoldCos, Bending Spoons, Palantir, Formation Bio, OpenEvidence etc.

The "labs" no longer give you the most convex upside in any of the salient areas. That's what the market is starting to see.

1) is mostly a hedge, if somehow there's a way for a true God Model to be born, I think it happens inside these research organizations focused exclusively on radically novel learning paradigms (not turning the economy into code).

[Jai Kondapalli @jai_kondapalli]

inference / meter based businesses are poorly positioned for The Great Compression™️

once humanity reorients and the new meta (intangible economy) arrives, we will have the tools / frameworks to stop allocating tokens into the abyss

inference businesses make the underlying assumption that we’ve reached the frontier of compression in the economy - but from a systems perspective this is obviously not true as we are living inside system designs from the 1800/1900’s (locke, coase, etc.)

humanities task will be to create intangibles (useful compressions) that best describe reality into abstractions that can be hooked up to the inference meter and thus financialized using a new instrument of capital allocation (terming this thesus)

the best humans create compressions that are most inference efficient compared to past ways of modeling reality

ex: development of logarithms by napier unlocking anything bottlenecked by large demand for multiplication

new primitives for capital allocation, political economy, ai deployment, and more telemetry on the economy will arrive soon making this feasible across the whole economy

the long term market structure for meter / inference will look a lot like memory / semis (large booms / busts as we compress and uncompress)

we will be talking in terms of compression cycles soon

[illiquid @lefttailguy]

@PlastiqSoldier Path dependence, their religiosity around scaling, and their own past success will make it very difficult for them.

[illiquid @lefttailguy]

The effective accelerationists of Silicon Valley believe technology is God and the effective altruists believe THEY are God. The former believe that sufficiently powerful technology will automatically organize society in the way that threads the needle between the Antichrist and Armageddon, and the latter believe they are the only ones capable of creating technology and organizing society in a way that threads that needle—convienently, according to their own utility function! The AI successionists don’t even care whether we thread that needle at all. In all cases they are wrong.

[illiquid @lefttailguy]

@tszzl @jsunornot Yes. Jobs is the OG prophet CEO archetype https://t.co/cDfxANvsdu

[illiquid @lefttailguy]

I've been working on this thesis that Star Actors (e.g, Mary Pickford) were the first aggregators of the intangible economy and "Prophet" CEOs (e.g, Elon, Karp, etc.) are the last. Like Prophet CEOs, Star Actors were closer to religious figures than capitalists and catalyzed / generated economic complexity by a virtue of their existence. Studios, theaters, and the population all interacted around them: everything was about finding the right set of complementary economic information to unlock the latent option value of the Actor.

Prophet CEOs are much the same way, and Silicon Valley is first and foremost in the business of finding Star Actors than it is technologists or capitalists. What we are all after is the ultimate technology for generating and organizing economic complexity. The Actor is more useful here than the Mathematician.

[illiquid @lefttailguy]

The only heuristic in venture capital is that there is no heuristic. @briansin speaks of this

[James Win @Win22James]

never understood why LPs expect a bunch of MBAs to be good at venture capital.

There’s only two types of VCs I would trust with my money

1. super technical engineer who’s actually built and scaled companies.

2.humble & spiritually-aware archetype who’s exceptionally good at reading people

(2nd one is harder to find than 1st)

[illiquid @lefttailguy]

I know a number of people who didn’t join. It’s not just people leaving it’s the fact that they are turning down offers to work at other areas of the stack. I also didn’t say “it’s over” I just said those places are no longer the optimal placing to allocate capital (human or otherwise)

[illiquid @lefttailguy]

@curious_vii

[illiquid @lefttailguy]

@EERandomness yes, code will solve many problems, but it will still be a local maxima. And I think it will be very hard to keep an advantage in coding models from a capability standpoint given the ease of verifiability.

[illiquid @lefttailguy]

@charlesgillespi Bending Spoons is a Frontier Economic Research Organization.

[illiquid @lefttailguy]

Forgot @aaruHQ re: economic version control.

[illiquid @lefttailguy]

@truthsoaker1 in fact, in this case the crazy growth ramp hurts them: great talent is realizing they have a better shot at 10xing equity joining neolabs and firms focused on economic version control. see: https://t.co/XLGnBC6NVx

and: https://t.co/dvTIL7OqQw

[Alec @AlecTPhD]

I joined OpenEvidence!

At Anthropic, I created Claude Science, a product built so that scientists' curiosity, not their capacity to execute, sets the limit on what they can discover. It takes on the heavy lifting across the early stages of the discovery and translational pipeline and scales what a single researcher can carry out. Building Claude Science has been the most fulfilling work of my career so far.

Now I'm excited to help build the future of AI in medicine at OpenEvidence, bringing that same leverage to doctors at the point of care. OpenEvidence already reaches the majority of patients in America. It doesn't get more translational than that.

[illiquid @lefttailguy]

@truthsoaker1 past performance is not indicative of future results

[Avidan Rudansky @arudansk]

financially and intellectually irresponsible not to be following @lefttailguy

[illiquid @lefttailguy]

@arudansk couldn't do it without you sir.

[illiquid @lefttailguy]

Prometheus, Arda, etc. as well in the economic version control bucket as well.