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Building LLMs is probably not going be a brilliant business

Building LLMs is probably not going be a brilliant business


The Netscapes of AI

image of early 20th century train advert for Watford
Railways improved the lives of millions – but investors were
rewarded with a
dramatic bust

Large language models (LLMs) like Chat-GPT and Claude.ai are whizzy and cool.
A lot of people think that they are going to be The Future. Maybe they are —
but that doesn’t mean that building them is going to be a profitable business.

In the 1960s, airlines were The Future. That is why old films have so many
swish shots of airports in them. Airlines though, turned out to be an
unavoidably rubbish business. I’ve flown on loads of airlines that have gone
bust: Monarch, WOW Air, Thomas Cook, Flybmi, Zoom. And those are all busts
from before coronavirus – times change but being an airline is always a bad
idea.

That’s odd, because other businesses, even ones which seem really stupid, are
much more profitable. Selling fizzy drinks is, surprisingly, an amazing
business. Perhaps the best. Coca-cola’s return on equity has rarely fallen
below 30% in any given year. That seems very unfair because being an airline
is hard work but making coke is pretty easy. It’s even more galling because
Coca-cola don’t actually make the coke themselves – that is outsourced to
“bottling companies”. They literally just sell it.

Industry structure – what makes a business good

If you were to believe LinkedIn you would think a great business is made with
efficiency, hard work, innovation or some other intrinsic reason to do with how
hardworking, or clever, the people in the business are. That simply is not the
case.

What makes a good business is industry structure.

Airlines – unfavourable industry structure

To be an airline is to be in an almost uniquely terrible market position.
For starters, there are only two makers of aeroplanes (Airbus and Boeing). For
reasons of training and staff efficiency, you have to commit to one or the
other, which gives the aeroplane makers very strong pricing power.

And buyers of airline tickets are incredibly fickle and have no loyalty. They will
switch from one “carrier” to another over even small differences in price.
Annoyingly, there are loads of other airlines and they’re all running the same
routes as you!

Worse yet, starting a new airline is surprisingly easy. Aircraft hold their
value so banks will happily lend against them. There are loads of staff
available that new entrants can hire. So randos will continually enter your
market, often selling tickets below cost for quite a while before they go bust.
And to top it off, there are plenty of substitutes for air travel – from
government-subsidised high speed trains to Zoom calls.

Airlines that get more efficient, work harder or come up with innovations
aren’t going to be able to “capture” the value of what they’ve done. If you
make more than the bare minimum to survive Airbus will notice that you’re being
undercharged and you’ll find that the next renewal on your service contract
eats up the difference.

Fizzy-drinks – very favourable industry structure

Being the Coca-cola company is pretty great though.

Coke is just water, colourant, flavouring, caffeine and sweetener. Those are
all widely available and really cheap. And as I said, you don’t even have to
combine them yourselves – bottling companies will do that for you for almost
nothing.

Handily, consumers are really picky about what goes in their mouth. The
unofficial motto of your main competitor is “Is Pepsi ok?”. This is despite
the fact that they are identical in both taste and colour. And a significant
minority of people actually say no!

And it isn’t easy for new competitors to enter the market. They can’t call
their new drink “coke” due to trademarks. They have to call it something else.
And consumers will generally refuse it because drinking an alternative is
considered some kind of weird statement.

What is industry structure?

Classically, there are five basic parts (“forces”) to a company’s position:

  1. The power of their suppliers to increase their prices
  2. The power of their buyers to reduce your prices
  3. The strength of direct competitors
  4. The threat of any new entrants
  5. The threat of substitutes

It’s industry structure that makes a business profitable or not. Not
efficiency, not hard work and not innovation.

If none of the forces are very much against you, your business will do ok. If
they are all against you, you’ll be in the position of the airlines. And if
they’re all in your favour: brill, you’re Coca-cola.

The industry structure of LLM makers: OpenAI/Anthropic/Gemini/etc

So is the position of LLM makers any good? I’m afraid it’s not good news.

LLM makers sometimes imply that their suppliers are cloud companies like Amazon
Web Services, Google Cloud, etc. That wouldn’t be so bad because you could
shop around and make them compete to cut the huge cost of model training.

Really though, LLM makers have only one true supplier: NVIDIA. NVIDIA make the
chips that all
models are trained on – regardless of cloud vendor. And that gives NVIDIA
colossal, near total pricing power. NVIDIA are more powerful relative to
Anthropic or OpenAI than Airbus or Boeing could ever dream of being.

How much power do buyers have over LLM token prices? So far, it seems fairly
high. Most LLM users seem willing to change from Chat-GPT to Claude, for
example. It doesn’t seem like brand loyalty is being built up. And companies
that build AI into their businesses are starting to do so via abstraction
layers that allow them to switch model easily. That makes LLMs
interchangeable – which is bad for those who sell them.

What’s the strength of direct competitors? Again, it is considerable. There
are loads of LLM vendors and pricing appears
competitive
. Worst of all,
Facebook basically dump their model on the market for no cost. It’s
reminiscent of Internet
Explorer

not exactly a great portent.

And it seems fairly easy for new entrants to build brand new models. That is
why there are so many LLM makers. Most of the techniques for making LLMs are
openly published in papers. Even bad models can gain customers if they are
cheap, which allows new entrants to gain a foothold.

The situation on substitutes is mixed. Instead of having Chat-GPT write some
text you could pay a person to do it instead. That is likely to be much more
expensive but also less likely to hallucinate, which might be important for
some usecases (law is the field least likely to use LLMs). And then there is
the trend that metadata tends to displace artificial
intelligence
once particular application has been proved out –
so as soon as you find a solid usecase you stand to be replaced.

A single mildly positive point does not make a profitable business. LLM makers
look a lot more like Netscape – who invented graphical web browsers, then went
bust – than Google, who made something good that ran on top of the the web
browsers.

How are they raising so much money?

If LLM makers seem cursed to an airline-style business destiny, how come they
are able to raise so much money? OpenAI raised $6.6
billion

at a valuation of $157 billion less than two months ago. That might be the
biggest VC round of all time.

What do they know that I don’t? It is a mystery – but let’s consider the
options.

Perhaps they are hoping to develop their own chips to reduce their dependance
on NVIDIA. $6.6 billion is not enough to build a new fab but it might be
enough to get a new chip designed which allows them to migrate off NVIDIA.
That would save them paying so much money for GPU time. But, NVIDIA are
actually one of the investors in the round (although only a fairly small
amount) – so it’s unlikely “develop an NVIDIA competitor” was on any of the
pitch deck slides.

Perhaps OpenAI are hoping to build a strong brand so that customers won’t
switch to competitors so easily. It’s not impossible, there is proof the
branding and lock-in can work in technology – but it
seems difficult to manage given that LLMs themselves generically have a textual
interface – meaning that there is no real API as such – you just send text, and
it sends text back.

Can they do anything about new entrants? Possibly. If investing $6.6bn allows
them to develop a major improvement in their model then that would raise
everyone else’s costs considerably and probably force some of their smaller
competitors out of the market. The trouble is the money is the most fungible
of all goods (that is the point, after all) and that $6.6bn is not all that
much of it. So this round wouldn’t, by itself, be enough to dissuate others.
I used to work at a bank and I can tell you that individual bond raises can be
a lot more than $6.6bn.

It’s worth saying that even companies that raise huge sums of money sometimes
turn out to have no viable business. WeWork ultimately raised over $10bn at a
valuation of $47bn before it was realised that their business simply did not
make sense. WeWork were valued at just $0.56bn in their most recent financial
restructuring – having lost well over 95% of what was invested.

Not all AI companies are doomed

If LLM makers aren’t going to be good businesses, does that bode ill for The
Future?

Firstly, it does not mean the technology will be bad. Whether the technology
ends up being good or not is mostly unrelated to whether Open
AI/Anthropic/Mistral/whoever makes any money off it. Container virtualisation
technology is pretty well developed even though Docker made almost nothing on
it. Web browsers are extremely advanced pieces of software even though making
a browser is such a bad business that most don’t usually count it as a business
at all. And CRMs are terrible despite the fact that Salesforce is tremendously
successful. Technology success and business success are mostly unrelated.

And then: not all AI businesses are building models. Ideally, if I were
running an AI business I would avoid building a model at all costs. Building
your own models looks like a undifferentiated schlep. Using a tiny bit of some
expensively trained model that Anthropic has produced could be very cost
effective and might make some business idea work that wouldn’t have 5 years
ago.

Beware software companies that aren’t software companies

Software companies are really good businesses. You have no real suppliers,
your software is often unique (so no competitors) and the substitute is just
users doing the job themselves. For this reason, software companies tend have
really great margins.

The problem is that not all technology companies are software companies. If
you have a hugely powerful single supplier like NVIDIA then the economics of
your company are going to look less like Microsoft Office and more like
Pan-Am.


Contact/etc


Other notes

The AI safety movement is a fantastic hypeman for LLMs as a technology.
Implying (pretty dubiously) that we are 10 minutes from
midnight
in some kind of
Ghost-in-The-Shell style AI crisis is in fact an extremely effective form of
product marketing. Perhaps that is why OpenAI and others employ so many AI
safety specialists.

The Coca-cola company mainly sit back and rake in the megabucks – but they do
spend a little bit of their earnings on research. And a little bit of a lot is
still significant. It’s interesting that that coke’s market research has
discovered that coke works better as a gender segregated product: Coke Zero is
Diet Coke, but for men.

If you want to read more about industry structure and market strategy, the
place to start is with Michael Porter. He reworked his famous essay The Five
Forces that Shape Corporate
Strategy

in 2008. It’s not the last word, but it probably should be the first word you
read if you want to learn more. And if you like it, he has a lot more.



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