---
layout: post
title: "Meta's open play: Commoditisation of LLMs"
categories: [articles]
excerpt: >
We look back at how Meta's pushed LLMs from products to commodities and
we guess what's next.
---
In November 2022, OpenAI released ChatGPT, showing that large enough models can
_learn_ language effectively. ChatGPT is an _industrialized product_ to which
users can subscribe to. Its ease of access and its flexibility immediately
sparked lots of use cases (translation, story telling, editing, ...), attracted
tons of news and a _multibillion dollar investment_ in [OpenAI from
Microsoft](https://blogs.microsoft.com/blog/2023/01/23/microsoftandopenaiextendpartnership/).
With ChatGPT, OpenAI turned LLMs from custom-built prototypes into a
ready-to-use product, on which they had almost complete monopoly. Later on, when
Meta entered the market, instead of competing directly against OpenAI, they
decided to use an _open play_ to reduce OpenAI's advantage.
This post looks back at the past few months to analyze Meta's move, show how
LLMs went from products to commodities, and guess what's next.
## A Wardley Map about LLMs
A [Wardley Map](https://learnwardleymapping.com) plots the _user value_ of
activities against their _evolutionary status_. As all maps, it's inaccurate,
but it can be useful. A Wardley Map about LLMs looks as follows:
{:.text-align-center}
{:.centered}
Reading it from top to bottom, we start from the user and their needs. In this
case, ChatGPT understands our intentions, expressed in natural language, and
serves us as a personal assistant knowledgeable in several fields.
Arrows denote relationships of "need" between the entities[^systems]. LLMs fulfill the
user's need and, for this, rank high on the value chain. In turn, LLMs need
large computing power; the user might be aware that there's computing involved
in the LLMs producing an output (for instance, might assume that better
computing would produce output faster), but doesn't need to get too involved
with it. Lastly, computing needs electricity (plus a ton of other needs, but we
don't need them in our map). As users, we know that computing relies on
electricity, but we don't typically think about it. That's pretty low on the
value chain.
[^systems]:
In _systems_ term, boxes represent _stock_ (of capital, e.g. data,
knowledge, financial assets, and so on) while arrows represent _flow_ of
capital between stocks.
Things get interesting when we add the evolutionary status of these activities.
Electricity is the rightmost in our evolution axis (the horizontal one). That's
because it is a **commodity**. It's entirely fungible to the consumer; it is
standardized, and we don't care who we get it from. We will most likely try to
get it as cheaply as possible, we will easily switch from a provider to another,
and we'll be fairly surprised if things fail (e.g. blackouts).
Computing is _not as evolved_ as electricity, but it's _almost there_. The cloud
enables us to deploy standardized software stacks (e.g. containers) across data
centers. Switching cloud providers is painful, but can be pulled off.
Data-center failure is pretty unusual and cloud providers focus more on high
volume and profit optimization than on innovation.
Let's now look at LLMs. Our map tells us of the specific point in time when
ChatGPT was launched. Before (see the _line of the past_) LLMs were
**custom**-built emerging technology used to prove feasibility, assess
performance, and assess demand.
ChatGPT, instead, turned LLMs into **products**. It created a big, growing
market that attracted several _competitors_. Stakeholders started focusing
on constant improvement. Users learned to expect LLMs to be prevalent (think
about personal assistants, chatbots, integration in code editors, etc.) and
compared one to the other based on feature difference. The transition from
custom-built to product encountered some initial _inertia_ (e.g., due to
training data, required computing, capital), but had completed.
## Meta's open play
Maps of this kind are useful to visualize the system, reason about it, make
_educated guesses_ about the future state of things and even strategize possible
_movements_ to influence them -- how to shape the map according to our plans.
In addition, history tells us that when things evolve, they do it due to
_competition_. As activities travel across the horizontal axis, the uncertainty
around them decreases -- and so do profit margins from their operation. Nothing
can stop evolution: if there's competition, it will _always_ lead to evolution.
But smart players can influence its _speed_.
With LLMs too, that's exactly what happened.
Soon after ChatGPT's release, other players started to enter the LLMs market.
Meta, most notably, [introduced
LLaMA](https://ai.meta.com/blog/large-language-model-llama-meta-ai/) in February 2023.
With LLaMA, Meta aced a few things -- that as we'll see will be fundamental to
their play:
- LLaMA's performance was _comparable_ to GPT-3; and
- it was released under a permissive (but noncommercial) license, that required
users to ask Meta for access. Meta could have provided it
"as-a-service"[^bard] or tried to put up other kinds of restrictions. Instead,
they chose a pretty light access model.
[^bard]: As Google later did, with ~~Bard~~ Gemini.
The "ask Meta for access" model lasted for... about a couple of weeks, when
someone leaked the model on 4chan and enabled open distribution.
Once leaked, the community immediately started to tinker with it by fine-tuning
it, quantizing it, and so on (and creating all sort of _funny_ licensing
issues). In a matter of _days_, the community had taken the model, deployed it,
and was now _advancing it_.
{:.text-align-center}
{:.centered}
Meta's _almost-open_ approach was a great strategic play, because once leaked it
_accelerated_ the evolution of LLMs from product to something more similar to a
commodity[^almost]. Meta itself only had to make available a model good enough to compete
with ChatGPT, then the community did the rest.
[^almost]:
We are almost there, but not yet. Failure in commodities/utilities should be
unlikely, but it's not that unusual for ChatGPT to be unavailable for brief
times.
> Open source seemed to accelerate competition for whatever activity it was
> applied to.
>
> _The play and a decision to act.
> [Chapter 5 of Wardley Maps](https://medium.com/wardleymaps/the-play-and-a-decision-to-act-8eb796b1dff1)_
Both openness and performance were _keys_ to the success of the play. Without
them -- for instance, with an "as-a-service" offer -- the APIs would have likely
been incompatible between OpenAI's and Meta's LLMS. The cost of switching from
one to the other would have been higher and the advantages reduced. There would
have been _friction_ (another form of inertia), reducing the effectiveness of
the play. Similarly, were LLaMA not comparable to ChatGPT's performance,
switching would have meant downgrading quality.
Instead, the community quickly standardized the interfaces to these models, with
most tools and frameworks now exposing an OpenAI-compatible REST API schema
while deploying other models. Through these standard interfaces, users can
easily try experiment with different models and pick the best one according to
their needs. They can fine-tune them to optimize performance and, more
importantly, "self-host" them -- a win for privacy too!
All this reduced OpenAI's _moat_, providing alternatives to their offering -- a
great thing for consumers and for Meta as well. Also, as an activity becomes
more industrialized (i.e., evolves), its profit margins decrease and the focus
shifts on improving operational efficiency. We could speculate that is what's
going on behind the scenes at OpenAI.
For _builders_, industrialized LLMs also mean that very there's little payoff in
building new LLMs in-house. Commodities have clear definitions and expectations,
are standard, and should be typically outsourced, so that entrepreneurs can
focus their energy on building what's valuable to (their) users. And as we'll
see, that's exactly what is happening.
## What's next?
The evolution of a practice/activity to a commodity (or utility) also triggers
co-evolution, which at the same time: 1. Deprecates legacy practices and 2.
Fosters the co-development of new practices. This happened several times
already, for example with the move from on-prem infrastructure to the cloud,
which led to the deprecation of most on-prem system administration practices and
gave birth to DevOps.
LLMs-as-commodities are pushing us in the same direction. They gave _genesis_ to
an entirely new set of tools (and dozens of AI-based startups). Sure, there's a
lot of _uncertainty_ about them. Some might be worth billions, others will
disappear. But they are surprising and exciting, and sometimes confusing. Their
market is undefined yet, unstable and will evolve over time. But there might be
hidden gems being developed right now. For all these reasons, I have mapped them
across **genesis** and **custom-built** evolution in the map above. That's where
we will see the innovation that will change how we _do_ and _build_ things.
That's also where high-risk, high-rewards opportunities live.
With their open play, Meta bought time and prevented OpenAI from cementing
their monopoly for good. But OpenAI still sits on a significant advantage in terms
of capital, skills, and iteration velocity. Plus, they can leverage their
privileged position (as API providers) to acquire market intelligence and
_learn_ from their users (those who interact with ChatGPT, with its APIs, and
even provide human feedbacks to their answers). Armed with such intel, they can
get a private peek at what the market is building (remember those exciting new
LLM-based tools?). With their capital, they can evolve it into a product faster
than anyone else[^ILC] -- cannibalizing all others in the industry.
[^ILC]:
This pattern is called "sensing engine" or "innovate - leverage -
commoditise (ILC)". For the systems people reading, it creates a
_reinforcing feedback loop_.
Their _moat_ has shrunk, but it will take a lot more for Meta and the other
incumbents to have a shot at their castle.
## Addendum: from Meta's perspective
After hitting "publish" on this, I was chatting about it with the
[IndieWeb](https://indieweb.org) community. [James](https://jamesg.blog) pointed
me to Meta's 2023 Q1 [earnings
call](https://investor.fb.com/investor-events/event-details/2023/Q1-2023-Earnings/default.aspx),
where Eric Sheridan from Goldman Sachs asks Zuckerberg to follow-up about
open-source with respect to AI.
From page 9 of the transcript:
> For us, it's way better if the industry standardizes on the basic tools that
> we're using and therefore we can benefit from the improvements that others
> make and others’ use of those tools can, in some cases like Open Compute,
> drive down the costs of those things which make our business more efficient
> too.
The transcript adds perspective to Meta's move and shows one more (positive)
outcome of their open play. Not only weakening OpenAI's position, but enabling
_outsiders_ to improve the technology that Meta uses internally and providing a
pool of talent, already trained in the relevant contexts, for them to hire.
---
Do you have any feedback or comment? Please get in touch, I'd love to hear them.
Meanwhile, thank you for reading! 'til next time! đź‘‹
#### Footnotes