> There is no technical moat in this field, and so OpenAI is the epicenter of an investment bubble. Thus, effectively, OpenAI is to this decade’s generative-AI revolution what Netscape was to the 1990s’ internet revolution. The revolution is real, but it’s ultimately going to be a commodity technology layer, not the foundation of a defensible proprietary moat. In 1995 investors mistakenly thought investing in Netscape was a way to bet on the future of the open internet and the World Wide Web in particular.
OpenAI has a short-ish window of opportunity to figure out how to build a moat.
"Trying to spend more" is not a moat, because the largest US and Chinese tech companies can always outspend OpenAI.
The clock is ticking.
Amazon might be a good analogy here. I'm old enough to remember when Amazon absorbed billions of VC money, making losses year over year. Every day there was some new article about how insane it was.
There's no technical moat around online sales. And lots of companies sell online. But Amazon is still the biggest (by a long long way) (at least in the US). Their "moat" is in public minds here.
Google is a similar story. As is Facebook. Yes the details change, but rhe basic path is well trodden. Uber? Well the juries still out there.
Will OpenAI be the next Amazon? Will it be the next IBM? We don't know, but people are pouring billions in to find out.
This is very different from OpenAI: if you show me a product that works just as well as ChatGPT but costs less--or which costs the same but works a bit better--I would use it immediately. Hell: people on this website routinely talk about using multiple such services and debate which one is better for various purposes. They kind of want to try to make a moat out of their tools feature, but that is off the path of how most users use the product, and so isn't a useful defense yet.
Apparently old enough to forget the details. I highly recommend refreshing your memory on the topic so you don’t sound so foolish.
1. Amazon had a very minimal amount of VC funding (less than $100M, pretty sure less than $10M)
2. They IPO’d in 1997 and that still brought in less than $100M to them.
3. They intentionally kept the company at near profitability, instead deciding to invest it into growth for the first 4-5yrs as a public company. It’s not that they were literally burning money like Uber.
4. As further proof they could’ve been profitable sooner if they wanted, they intentionally showed a profit in ~2001 following the dotcom crash.
Edit: seems the only VC investment pre-IPO was KP’s $8M. Combine that with the seed round they raised from individual investors and that comes in under $10M like I remembered.
Right now, OpenAI's brand is actually probably its strongest "moat" and that is probably only there because Google fumbled Bard so badly.
Facebook has an enormous network effect. Google is the lynchpin of brokering digital ads to the point of being a monopoly. Someone else mentioned Amazons massive distribution network.
I don't think that's true. I think it's actually the opposite. Global physical logistic is way harder than software to scale. That's Amazon's moat
Not to take away from the rest of your points, but I thought Amazon only raised $8m in 1995 before their IPO in 1997. Very little venture capital by today’s standard.
no, the amazon moat is scale, and efficiency, which leads to network effect. The chinese competitors are reaching similar scales, so the moat isn't insurmountable - just not for the average mom and dad business.
The moat is actually huge (billions of $$). What is happening is that there are people/corps/governments that are willing to burn this kind of money on compute and then give you the open weight model free of charge (or maybe with very permissive and lax licensing terms).
If it wasn't for that, there will be roughly three players in the market (Anthropic and recently Google)
Gruber writes:
" My take on OpenAI is that both of the following are true:
OpenAI currently offers, by far, the best product experience of any AI chatbot assistant. There is no technical moat in this field, and so OpenAI is the epicenter of an investment bubble. "
It's amusing to me that he seems to think that OpenAI (or xAI or DeepSeek or DeepMind) is in the business of building "chatbots".
The prize is the ability to manufacture intelligence.
How much risk investors are willing to undertake for this prize is evident from their investments, after all, these investors all lived through prior business cycles and bubbles and have the institutional knowledge to know what they're getting into, financially.
How much would you invest for a given probability that the company you invest in will be able to manufacture intelligence at scale in 10 years?
Retail margins are razor thin, so the pennies of efficiency add up to a moat
When was this true? Amazon was founded Jul 1994, and a publicly listed company by May 1997. I highly doubt Amazon absorbed billions of dollars of VC money in less than 3 years of the mid 1990s.
https://dazeinfo.com/2019/11/06/amazon-net-income-by-year-gr...
As far as I can tell, they were very break even until Amazon Web Services started raking it in.
1) Almost the best price, almost all the time
2) Reliably fast delivery
3) Reliably easy returns
4) Prime memberships
Same for OpenAI. Anytime I talk to young people who are not programmers, they know about ChatGPT and not much else. Never heard of Llama nor what an LLM is.
Anyone can sell online. But not just anyone has those advantages like same day abs next day shipping
2) If you had some secret algorithm that substantially outperformed everyone, you could win if you prevented leakage. This runs into the issue that two people can keep a secret, but three cannot. Eventually it'll leak.
3) Keep costs exceptionally low, sell at cost (or for free), and flood the market with that, which you use to enhance other revenue streams and make it unprofitable for other companies to compete and unappealing as a target for investors. To do this, you have to be a large company with existing revenue streams, highly efficient infrastructure, piles of money to burn while competitors burn through their smaller piles of money, and the ability to get something of value from giving something out for free.
At the beginning of ride sharing, people believed there was absolutely no geographical moat and all riders were just one cheaper ride from switching so better capitalized incumbents could just win a new area by showering the city with discounts. It took Uber billions of dollars to figure out the moats were actually nigh insurmountable as a challenger brand in many countries.
Honestly, with AI, I just instinctively reach for ChatGPT and haven't even bothered trying with any of the others because the results I get from OAI are "good enough". If enough other people are like me, OAI gets order of magnitudes more query volume than the other general purpose LLMs and they can use that data to tweak their algorithms better than anyone else.
Also, current LLMs, the long term user experience is pretty similar to the first time user experience but that seems set to change in the next few generations. I want my LLM over time to understand the style I prefer to be communicated in, learn what media I'm consuming so it knows which references I understand vs those I don't, etc. Getting a brand new LLM familiar enough to me to feel like a long established LLM might be an arduous enough task that people rarely switch.
First, get government regulation on your side. OpenAI has already looked for this, including Sam Altman testifying to Congress about the dangers of AI, but didn't get the regulations that they wanted.
Second, put the cost of competing out of reach. Build a large enough and good enough model that nobody else can afford to build a competitor. Unfortunately a few big competitors keep on spending similar sums. And much cheaper sums are good enough for many purposes.
Third, get a new idea that isn't public. For instance one on how to better handle complex goal directed behavior. OpenAI has been trying, but have failed to come up with the right bright idea.
That's half the point of OpenAI's game of pretending each new thing they make is too dangerous to release. It's half directed at investors to build hype, half at government officials to build fear.
They don’t need to make a moat for AI, they need to make a moat for the OpenAI business, which they have a lot of flexibility to refactor and shape.
Patents. OpenAI already has a head start in the game of filing patents with obvious (to everybody except USPTO examiners), hard-to-avoid claims. E.g.: https://patents.google.com/patent/US12008341B2
By knowing a lot about me, like the details of my relationships, my interests, my work. The LLM would then be able to be better function than the other LLMs. OpenAI already made steps in that direction by learning facts about you.
By offering services only possible by integrating with other industries, like restaurants, banks, etc... This take years to do, and other companies will take years to catch up, especially if you setup exclusivity clauses. There's lots of ways to slow down your competitors when you are the first to do something.
Alternatively, a model that takes a year and the output of a nuclear power plant to train (and then you can tell them about your tricks, since they aren't very reproducible).
Also, I suspect that the next breakthrough will be kept under wraps and no papers will be published explaining it.
The competitions are mostly too narrow(programming/workflow/translation etc) and not interesting.
Or maybe nvidia has the moat. Or silicon fabs have it.
Meta and X proven surprisingly resilient in the face of pretty overwhelming negative sentiment. Google maintains monopoly status on Web Search and in Browsers despite not being remarkably better than competition. Microsoft remains overwhelmingly dominant in the OS market despite having a deeply flawed product. Amazon sells well despite a proliferation of fake reviews and products. Netflix thrives even while cutting back sharply on product quality. Valve has a near-stranglehold on PC games distribution despite their tech stack being trivially replicable. The list goes on.
A vertically integrated system that people depend on with non-portable integrations is a moat.
Regulatory Capture is a moat.
Maybe they were just too early, later on it turned out that the browser is indeed a very valuable and financially sound investment. For Google at least.
So having a dominant market share can indeed be even if the underlying tech is not exactly unobtainable by others.
Once this bubble bursts, local inference will become even more affordable than it already is. There is no way that there will be a moat around running models as a service.
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Similarly, there probably won't be a "data moat". The whole point of large foundation models is that they are great priors. You need relatively few examples to fine tune an LLM or diffusion model to get it to do what you want. So long as someone releases up to date foundation models there is no moat here either.
and they are probably going to go with regulatory moat over technical moat.
See the scramble to make themselves arbiters of "good AI" with regulators around the world. It's their only real hope but I think the cat's already out of the bag.
But greeed isgood!
OpenAI's C-suite: Well, they earned the C, but it was a letter grade.
What a profoundly unimaginative strategy. No matter the industry, a large-scale diversion of resources towards a moonshot goal will likely get you to that goal. They haven't made an argument as to why we should do that just for them, especially with all of the other alternatives.
And no, advertising your previously secret testing models (e.g. o3) as if they were market competitors is not how to prove they should have our money.
Then why do they look about average to Anthropic, Mistral, DeepSeek, and many others of that cohort despite having x100 the amount of resources?
What “we”? They’ve already raised billions, and I suspect they’re about to succeed at raising tens of billions more, despite the skepticism of random HN users.
The Unreasonable Effectiveness of Huge Sums of Money in the Natural Sciences
Unimaginable Sums of Money Considered Harmful
The long term future of LLMs sure looks like the LLMs themselves will be commodities and the real value will lie in the use of those LLMs to deliver value
AI progress is driven by strong valuable data. Detailed important conversation with a chatbot are much more valuable that quick search queries. As LLMs extend past web UIs, there is even more interaction data to capture and learn from.
The company that captures the most human-AI interaction data will have a TREMENDOUS moat.
If I have the LLM translate a text from French to English... what is there to learn from that? Maybe the translation is great maybe it's awful, but there's no "correct" translation to evaluate the LLM against to improve it.
If I ask the chatbot for working code and it can't provide it, again, there's no "correct" code to train it against found in the conversation.
If I ask an LLM to interpret a bible passage, whether it does a good job or a terrible job there's no "correct" answer that the provider has to use as the gold standard, just the noise of people chatting with arbitrary answers.
When the big companies say they're running out of data, I think they mean it literally. They have hoovered up everything external and internal and are now facing the overwhelming mediocrity that synthetic data provides.
I disagree, that phenomenon is tied to the social network like phenomena we saw with WhatsApp and Facebook and to the aggregator business model of Amazon and Google.
Mathematically we can describe the process by which these monopolies form as nodes joining graphs:
Each node selects graphs to connect to in such a way that the probability is proportional to the number of nodes in the graph.
Sure Amazon and google feature two types of nodes, but the observation still fits: Selling on Amazon makes sense if there are many customers, buying on Amazon makes sense if there are many offers.
OpenAIs business does not have this feature, it does not get intrinsically get more attractive by having more users.
Claude is noticeably better
The word "open" is still under threat in this scenario too.
The world‘s largest oil producer?
Models are commodities, even in the case Open ai goes through another major breakthrough nothing can stop some of their employees to run to other companies or founding their own and replicating or bettering the OpenAI results.
In fairness I realize that I don't use any of OpenAI's models. There are better alternatives for coding, translating or alternatives that are simply faster or cheaper (Gemini) or more open.
The best moats are scale (Walmart), brand (“Google” means search), network effects (Facebook, TikTok). None of those are perfect but all are better than just having better tech.
OTOH, WD40 has a technical moat since 1953, without a patent. There are a number of companies who rely on technical moat mixed with excellent technical image: Makita or Milwaukee Tool come to mind. You can have also a company based on technology moat that theoretically shouldn't exist (patents expired, commodity products), but it does (Loctite, 3M).
Eight racks of GB200's? You're talking about 1 megawatt of power. Well over that if you include PSU losses, cooling, and all the networking, storage, memory...
Yet Chrome for Google did help create a moat.
A moat that’s is so strong the DoJ is investigating if Chrome should be a forced divesture from Google/Aplhabet.
Note: I do generally agree with the article, but this also shows why you shouldn’t use analogies to reason.
The result is Chrome, Google invests in it because they don't want to be intermediation between them and eyeballs - it's a strategic play, not necessarily a direct money maker. So that's why they do it. But there is no moat, they have the number 1 browser because they spend enormous sums of money hiring top engineers to build the number 1 browser.
And this has been true of the browser for a long time, Internet Explorer won because they forced people to use it and used a tonne of anti-competitive practices to screw the competition. Firefox still managed to disrupt that simply by building a better product. This is the clear evidence there is no moat - the castle has been stormed repeatedly.
OpenAI can't do anything comparable to Google at this point because they have no other product. If anything they're more like Netscape.
This is getting so repetitive now that it is stated as a truism.
Isn't it the same bet yahoo was betting on in 2000 that it would win because their product branding is better? And now, Yahoo's and Microsoft's search engine is worse than Google from 2 decades ago.
The so-called open source models are getting better and better and even if OpenAI suddenly discovered some new tech that would allow for another breakthrough, it will be immediately picked up by others.
The data backs it up, Anthropic make most of their revenue from API while ChatGpt makes most of its revenue from the plus plan.
SamA wants to take everything the non-profit built and use it directly in HIS for profit enterprise. Fuck him.
The question is - why would that one company want this hot take out there?
> OpenAI’s board now stating “We once again need to raise more capital than we’d imagined” less than three months after raising another $6.6 billion at a valuation of $157 billion sounds alarmingly like a Ponzi scheme
Ponzi scheme. If he's shilling something I guess it's "shorting".
I don't know who this Gruber guy is, but it's relieving to hear at least someone talk some sense about the ludicrous levels of investment being poured into something that most users go "heh that's neat" and would never consider paying for.
LLMs and AI is not where Meta adds value to the market. They add value via advertising. Therefore, AI is not Meta's product, and thus Meta should aim to make AI as cheap as possible.
They did the same thing for infra. Meta doesn't sell cloud computing, so they want to make infra as cheap as possible.
LLMs in their current form are very useful tools for many people. Most programmers I know use them daily. I have even friends who aren't tech savvy who paid the subscription to ChatGPT and use it all the time.
https://www.theguardian.com/technology/2024/jan/08/ai-tools-...
OpenAI has the potential to create the next groundbreaking innovation, like the iPhone. Who needs apps when the AI itself can handle everything?
Uh, they were advertising in 1999 already?
You might find their perspective on advertising surprising.
Returning to my earlier point, please expand your argument about why OpenAI may fail. Let’s aim to elevate the discussion.
I’d like to see more thoughtful discussion here. The debate often feels stuck between oversimplified takes like “It’s just a commodity” and “No, it’s a game changer!” Let’s assume for a moment that OpenAI operates as a commodity business but still figures out how to become profitable, even with small margins. After all, US Steel was a commodity and became hugely successful.
Given these assumptions, why do you think OpenAI might not succeed? Let’s move the conversation forward.
[citation needed]
Google's goal was always advertising just like every other search engine.
https://blogs.cornell.edu/info2040/2019/10/28/the-academic-p...
You might find their perspective on advertising surprising.
Returning to my earlier point, please expand your argument about why OpenAI may fail. Let’s aim to elevate the discussion.
