illiquid

routers eat the world; consilient software observer; crafting an alchemist's paradise.

[illiquid @lefttailguy]

People know that enterprise software has never been that fancy, but that enterprise distribution is hard.

The first point is becoming clearer as software gets ever easier to build, but the distribution point means that the "incumbents can use Cursor too" argument should have legs. In other words, having the distribution apparatus built out should give incumbents massive advantage. But the market is obv. not buying that.

Two potential explanations:

1) it's actually becoming less difficult to distribute software and to understand what to build. Biggest story here is @OpenAI as a workflow intent aggregator that can route that intent to the apps/tools best suited to solve the problem in question. If the primary interface becomes an AI system that routes requests to various specialized tools, then distribution increasingly means being the tool that ChatGPT (or whoever wins that aggregation race) chooses to route to.

This advantages the orchestration layer/aggregators of course (see this interesting piece in the Diff for a fun theory there: https://t.co/x4rtMP94jW), but also means that startups should have a relative advantage in optimizing everything for this new paradigm/definition of distribution. This is somewhat analogous (with caveats) to how Google partially shifted distribution from being contingent on salespeople and brand ads to SEO/adwords, but even more extreme.

or 2) if you don't buy that, PE firms or these newish AI transformation firms (like Brain Co, GC's Percepta, SaxeCap, Palantir/Scale, Anthropic, etc.) will leverage their reputational capital, AI-native DNA, and/or ownership stakes to leapfrog both AI-native startups and incumbents by building and distributing/deploying tools themselves (the Bain anecdote).

Understanding how distribution dynamics/difficulty are trending feels like it should be very important if you want to have a sophisticated view on how software economics will eventually look, but it's pretty understudied imo. Especially relative to the question of how much easier it is becoming to build product.

1) taken to it's logical conclusion means that @OpenAI (or whoever wins) matches workflow intent to digital services/solutions as effectively as search/social match consumer intent to consumer goods. And that OpenAI/AI more broadly makes the production of digital services as automated/fossil-fueled as the production of physical goods.

If you are building/selling software this means you have two new/more powerful rent seekers than in the past: the aggregators/orchestration layer and the chip/infra layer. This sounds a lot like e-commerce market structure/economics to me. They also pay large rents to aggregators (meta, amazon, google, etc.) and infrastructure layer (manufacturers).

In e-commerce, moats exist in the form of brand, economies of scale (in certain instances), etc. but moats and margins in e-commerce are obviously weaker than moats and margins in enterprise software.

Folks like @cpaik have argued that AI will make the economics of software look more like the economics of media, which is sort of the maximalist take on vibecoding completely eliminating software moats.

But the reality IMO (at least for foreseeable future) will be somewhere closer to e-commerce. Software won't become completely free like a lot of media has, but just selling software will become less lucrative. Some solutions will commoditize significantly, like certain consumer goods did when mass manufacturing and search/direct response ads went live. Some will retain/command more pricing power as a function of true account/data gravity, regulation/compliance, end-to-end workflow complexity etc. (this is probably why you see vertical solutions outperforming).

In any case, I find it harder and harder to argue that the long run equilibrium isn't relatively bleak unless you're an aggregator, vertically integrated operating company (the AI-native opco/AI transformation approach), or NVIDIA.

would love thoughts @matt_slotnick, @sebkrier, @ChairliftCap, @MangotreeA, @huntermmonk , @yrechtman, @BucknSF

[illiquid @lefttailguy]

@signulll https://t.co/3Owe9qSDlI

[illiquid @lefttailguy]

I think mainstream GLP-1s are going to usher in a social renaissance by giving us the confidence, connection, and self-control necessary to escape our current food/gambling/AI slop addicted trajectory. This is the beginning of our transhumanist future. One where we become biologically and behaviorally optimized to align our natural desire for sensory novelty with true epistemic novelty.

[illiquid @lefttailguy]

@josephrobison https://t.co/jLjRiQJZ7J

[illiquid @lefttailguy]

In times like this it's important to ask: "why didn't the CIA build Google?"

[Techmeme @Techmeme]

Kirkland & Ellis, the world's highest-grossing law firm, is setting aside $500M to build its own AI platform rather than rely on tools available to its rivals (Financial Times)

(Visit Techmeme dot com for the link and full context!)

[illiquid @lefttailguy]

@josephrobison They hired Palantir for that!

[illiquid @lefttailguy]

@rennyzucker Yes, but they didn't build Google! Aka, the thing that made the internet useful.

[illiquid @lefttailguy]

@geoffreywoo Scale and Palantir working with them dawg.

[illiquid @lefttailguy]

@matt_slotnick @geoffreywoo https://t.co/jLjRiQJZ7J

[illiquid @lefttailguy]

@Memetic_Theory bruno mars is probably the only one

[illiquid @lefttailguy]

I have a thesis that xAI no longer needs their own human data pipeline because for coding they will rely on Cursor traces/data efforts, and for everything else they are shifting to video pretraining a computer action model (this will be what underpins Macrohard)

[illiquid @lefttailguy]

We're actually seeing the opposite though, right? World models, like Google's Genie, seem to be fairly good at training robots (sim2real), but OpenAI can't seem to make good Tax Agents without the learning that comes with real world deployment. They are rolling up $1B of accounting firms. But I agree with you that not all context is equal! That's the entire game.

"Most context (both that exists right now and that will be created in the future) will be completely commodity beta. Winning will be about getting to and instrumenting the right asset (context production factory) first. And yes, there are right and wrong answers."

https://t.co/P6cOOgmGas

[illiquid @lefttailguy]

@matt_slotnick @arjunblj oh wow

[illiquid @lefttailguy]

CTRL+F "context" in a16z's 2 new investment announcements this morning...one consumer SaaS vampire one SMB SaaS vampire. Everyone wants context, but doesn't realize that "trust" is antithetical to the nature of the vehicles they are using to acquire it.

[illiquid @lefttailguy]

literally everyone rn

[illiquid @lefttailguy]

@kabalabsinc @hypersoren Yup, they are learning at the wrong unit of analysis.

[illiquid @lefttailguy]

literally everyone rn

[illiquid @lefttailguy]

Solving the science of asset selection in a future (or indeed the present) where every company is a "Context Acquisition Company" is the real frontier.

I love that everyone is getting around to the idea that the secrets (scarce context) currently illegible to/hidden from computers (human or machine) are everything. Now the next leap for people to make is that the science of sourcing, selecting, and monopolizing that context (really THE ASSETS that produce it) is everything. If AI progress is a function of compute and data (most algorithmic progress is really just data progress; h/t @BerenMillidge, @_kevinlu, @mentalgeorge, @GarrettLord, etc.), then every company is going to have a context desk just like they will (or already do) have a compute desk. The difference is, CONTEXT IS NOT FUNGIBLE. Most context (both that exists right now and that will be created in the future) will be completely commodity beta. Winning will be about getting to and instrumenting the right asset (context production factory) first. And yes, there are right and wrong answers. To do this kind of asset selection well requires an extremely scarce meta-capability: the ability to coordinate the right kind of access and the right kind capital at the right time.

These assets (and the secrets within them) are structurally difficult to access, evaluate and instrument. They are not floating around in banked processes, to be frictionlessly purchased on listed exchanges, or willingly coming through Mercor or Handshake's expert portal. (Yes, a context production asset can be (very often is) a single person or collection of people.)

When @WillManidis talks about a Deal Guy Yuga, what he means is that there are people who have deeply internalized the fact that at the limit, in a world of infinite intelligence, access to/monopoly on the right permissioned data streams is all that matters. Getting yourself to a position (meta-access, meta-capital) where you have the ROFR on those permissioned data streams, means being a generational Deal Guy. This is a very different and specific kind of "Deal Guy" though.

Knowing which asset(s) are going to give you the right context to create, compound, and commercialize the best vertical world model now and into the future is the new form of security analysis. But the triple-exceptional combination of domain expertise, meta-access, and technical ability that’s required to execute this new security analysis effectively is scarcer than the talent at quant firms, YC combined, and dare I say, the labs, combined. Palantir understood this and it's why they focused on getting root-access (or something close) to the "highest-status" institutions, and the data streams they produce, first. If you have the talent that can get access to and create value within those institutions, everything else should be a forgone conclusion.

If you want examples of the teams that (I believe) actually understand this new science of asset selection and long term value capture in a world of infinite intelligence, study Long Lake and @formationbio. They know and have known that it's all about being able to get the right asset (context), in the right market, with the right team (machine and human) first. These two companies are very far ahead on the scientific frontier of context acquisition.

GC backed Long Lake last year. Do you think it’s a coincidence that Long Lake chose to work with General Catalyst? My bet is that Long Lake knew they wanted to acquire Amex GBT before they partnered with GC, and that they partnered with GC because Ken Chenault (the ex-CEO of Amex) is General Catalyst’s Chairman. That gave them the right access at the right time to a very valuable context asset (Amex Global Business Travel)

A superhuman vertical-specific Elon operating every company means market leading monopolies in every single slice of the unstructured economy. The thing is you have to build this superhuman Elon while flying the plane. You can't build this superhuman Elon without the very specific context that operating specific assets in the real world gives you. In fact, there's only one stream of context that was able to produce human Elon! Knowing which context stream is likely to do the same a priori is so extremely difficult, but probably possible.

I’ll let you intuit why Amex GBT is both most likely to be the market leading monopoly if it were operated by the superhuman Elon of business travel and why it’s also the most likely to produce the context to build that superhuman Elon.

The labs of course are very large acquirers of context at present and I think they will continue to play and improve their capabilities here. Through their deplyoment companies, they have already chosen the PE funds that they deem to be the best Context Acquisition Funds. Through in-house deployment focus on Life Sciences they have chosen the vertical they see as containing the most valuable context producing assets. They will acquire very seemingly unrelated companies and will acquihire very interesting people just to get tokens, they will create a Context Acquisition Fund of Funds. But it's not a foregone conclusion that they become the best performing context acquisition companies. Or that they even view it this way. And that presents an opportunity for anyone that does.

[illiquid @lefttailguy]

@pixelFX__ @uv @Cod3xOrg interesting, thanks!

[illiquid @lefttailguy]

@Noahpinion you'd really enjoy https://t.co/VE4Fl7dMH7

[illiquid @lefttailguy]

@matthewhoffman if you're alive, pivot to being an RL mummy

[illiquid @lefttailguy]

@tenobrus Morning swim in the Bay. The sense of awe is unparalleled.

[illiquid @lefttailguy]

@Noahpinion Nisei (Russian Hill), Mr. Pollo (Mission), Rich Table (Hayes)

[illiquid @lefttailguy]

@ItzSuds @mathematics_inc My bets on @AntithesisHQ !!