[nstart]

I'm a little confused here. Cost of revenue is lower than revenue. That's good. R&D is the main contributor to losses here and this seems normal in an industry like this. For OpenAI specifically, I think this is problematic. They were the first movers but despite the large R&D they've lost so much ground to Anthropic despite Anthropic seemingly gifting them with weird PR self owns. But if we were to extrapolate this to the industry as a whole, this seems more positive than negative. Am I reading this incorrectly? Unless there's an assumption that R&D costs have to forever go up in order to increase revenue, I feel like this shows that the AI industry is actually on a path to profitability in the long term.

Whether it can physically be as all encompassing as it makes itself out to be or whether it will just be healthily profitable remains to be seen. Kind of like how Uber went from "We'll autonomously drive the world" to "Look, we deliver food, goods, and people to locations and we figured out how to do that in a way that makes profits. Also, ads".

[EmiDub]

> Cost of revenue is lower than revenue.

I’m not sure how people are looking at numbers that show, even if we wipe off the enormous R&D expenditures, they are still in the red for inference + sales/marketing + admin and responding “this seems positive”.

It’s like being a sold a car and being told “well if you ignore the fact it has no engine it’s a good buy” yet it also has no wheels.

> Unless there's an assumption that R&D costs have to forever go up in order to increase revenue, I feel like this shows that the AI industry is actually on a path to profitability in the long term.

There are three futures right, I’ll rank them in order of fantasy -

1. Someone achieves AGI. At that point the economics of an individual company don’t even matter.

2. R&D costs do have to forever continue, because LLMs can be continually iteratively improved. Much like chip development, there is no end in sight, at least not on a near term timescale. If you are not continually at the frontier, customers will use a competitor or open/local alternatives.

3. LLMs reach a plateau of functionality. Further gains are minimal, quality reaches the apex of what the technology permits. In this scenario the hyperscalers have no business because open/local models will rapidly reach that same plateau as well.

[surgical_fire]

The leaked numbers completely ignore how much of their compute is subsidized.

It also ignores how much of "R&D" is actually needed for the thing they offer to keep working. Looking at the thread everyone seems to be presuming "R&D" is all "training new models", but that is uncertain.

[grey-area]

Cost of revenue is lower than revenue. That's good. R&D is the main contributor to losses here

What is counted as R&D is completely arbitrary. These figures are just playing accounting games to attempt to hide the massive ongoing costs.

We’ll see a little better when they IPO and are forced to attempt to make money but I wouldn’t invest in this business.

[Certhas]

The Uber comparison makes no sense. This is the opposite situation. Uber lost money on rides, OpenAI is (possibly) making money on inference. Uber used an R+D moonshot to autonomous driving to justify capturing an established industry without reducing costs meaningfully. OpenAI has a core product that risks becoming a commodity with open source models only 6 months behind.

[simianwords]

Uber didn’t lose money on rides other than some edge cases. What’s your source for this claim?

[Certhas]

Do you have a source for the claim that Uber was making money on rides during its decade of enormous unprofitability?

Its public stance was that growth was more important than profit. Why wouldn't they be subsidizing rides to fuel growth if that is their publicly stated goal?

And anyway, we got the Uber Files some years ago which made it explicit:

"In October 2014 in Madrid, the presentation shows, the hourly subsidy to drivers of $17.50 was almost twice the hourly fare it charged, which was only $9.10. In Berlin, the gross hourly fare Uber charged was $2.20, while the subsidy it paid out to drivers was $10.20 an hour. Uber burned through cash to “buy revenue”, in the words of the presentation."

https://www.theguardian.com/news/2022/jul/12/they-were-takin...

[disgruntledphd2]

The vast, vast amounts of money they spent on driver incentives city by city would seem to support the OPs claim (source: I was familiar with their spend on ads in the US approximately 10 years ago).

[simianwords]

There is no evidence that Uber was systemically losing money per ride instead of at edge cases. Share your evidence please.

[disgruntledphd2]

> Share your evidence please.

This is an impossible ask unless one works at Uber. I can tell you that i saw how much they were spending on ads back in 2016, and how long it continued and can assure you that they were 100% losing money back then.

Like, even now their margin is around 10% (they made 5bn on 50bn of revenue). Other software companies make a much, much, much better margin because Uber is basically not a real software business, it's an app attached to a low-margin delivery business.

[s1artibartfast]

ads =/= rides

[lenkite]

Uber kept fares artificially low while simultaneously paying high bonuses to drivers to build a massive network. After burning through roughly $30+ billion over its first decade, Uber then pivoted its business model by raising rider fares, increasing restaurant fees on Uber Eats, and cutting driver pay.

Basically, win market through subsidy -> establish monopoly -> increase price -> profit.

[raincole]

> Revenue: $13.07 billion

> Cost of Revenue: $7.5 billion

It's almost too good to be true. Did OpenAI intentionally leak this? It singlehanded eliminate the biggest concern: that tokens are sold at loss.

[dofm]

I think it does look like an intentional leak, but I disagree that it even shows with any clarity that inference is profitable.

[JCW2001]

"Cost of revenue" isn't the entire cost of running the company, (ie R&D, operations, sales, marketing, etc). It's just a cost they've associated with revenue IN ADDITION to the other costs I mentioned.

HSBC say they need to turn a 13b revenue to 200b by 2030 AND also find another 204b, in order to become profitable.

[thewebguyd]

> It's just a cost they've associated with revenue

Its a little less arbitrary than that. Cost of Revenue/Cost of Sales/Cost of Goods Sold are clear, if you're following GAAP. To label these expenses as cost of revenue, they must meet the matching principle in that the expenses must be directly tied to the generation of specific revenue. If you didn't make that "sale" then that specific cost would not exist.

Other operating expenses come later on the income statement.

Total Revenue - Cost of Revenue = Gross Profit first, then you subtract OpEx from there for EBIT.

For OpenAI, I'd assume cost of revenue is almost directly inference costs + customer support & app dev.

[Refreeze5224]

How in the world could you read that article and think there is anything positive about OpenAI's prospects? We've been hearing for months that these companies need to make trillions of dollars in a handful of years, growing at record rates in order to break even and justify their massive outlay.

It's not going to happen.

[nstart]

I tend not to focus on that future too much. I used to do so long ago. For example, how could Facebook possibly justify their losses while asking for such a big valuation? Same for Uber. Same for any number of big companies. And it turns out that growth in the future is impossible to predict accurately. Shopify is a good example where at the time the addressable market of online stores was tiny. But it turned out that Shopify created its own market which is huge today. Technology improvements have a way of creating new markets which far surpass today's total addressable market. Factor in currency depreciation and whatnot and sometimes, futures that looked impossible turn out to be possible.

Not saying anyone is wrong in pointing at the buildouts for AI and questioning its feasibility. Just making the argument for why I personally only look at operational costs and revenue because it's the only real-ish value I can look at and judge if a business can grow sustainably.

As a counter point, the red flag to all of this is R&D costs growing for each model release. If that continues and revenue cannot outstrip it, then these companies have a problem and it'll probably be that just 1-2 frontier labs can survive this once the dust settles.

[mike_hearn]

I don't think Uber was a great ROI for investors though. It lost almost all the money they gave it in return for a business with entirely average profit margins (average across all industries, far lower than average for a SaaS app).

[hollerith]

Since Uber's never paid dividends, ROI is easy to calculate.

At the end of its first day of trading (in May 2019), Uber's price was $41.57 per share, and it is currently $72–73 per share for a compound annual growth rate (CAGR) of roughly 8.2% per year.

In comparison, the BVP Nasdaq Emerging Cloud Index earned roughly 22–25% CAGR over the same time interval.

So (as usual) Mike Hearn is correct.