Man I love Waymo everytime I'm in SF. Truly feel like I'm living in the future when I sit in one
Readit Newsmgfist commented on Waymo granted permit to begin testing in New York City cnbc.com/2025/08/22/waymo... · Posted by u/achristmascarl
mgfist commented on What if A.I. doesn't get better than this? newyorker.com/culture/ope... · Posted by u/sundache
When you combine that with serving millions of users, it also gets amortised over several million users.
> But most people want output now, not in 10 hours.
At 65t/s, that's 2.5 million tokens output.
Yes, but usage is not uniform even when you have millions of users. It smooths the usage lines, but the peaks and troughs become more extreme the more users you have. At 3am usage in the US goes down to effectively 0. Maybe you can use the compute for Asia customers, but then you compete with local compute that has far better latency.
Then you have seasonal peaks/troughs, such as the school year vs summer.
When you want 4 9s of uptime and good latency, you either have to overprovision hardware and eat idling costs, or rent compute and pay overhead. Both cost a lot.
mgfist commented on Databricks is raising a Series K Investment at >$100B valuation databricks.com/company/ne... · Posted by u/djhu9
What’s the obvious rationale for going through the whole alphabet of funding rounds, instead of going public / IPO after «the usual» number of raising money.
Wouldn’t the current strategy result in some serious stock dilution for the early investors?
Both have benefits. Staying private means a lot less distractions, less investor scrutiny (good and bad), and the general ability to do whatever you want (good and bad).
It's a lot easier to stay long-term focused without investors breathing down your neck. As a private company you're not dealing with shortsellers, retail memers, institutional capital that wants good earnings now, etc..
Of course, the bad side is that if the company gets mismanaged, there's far less accountability and thus it could continue until it's too late. In the public markets it's far easier to oust the C-suite if things go south.
It's a shame that the trend of staying private longer means retail gets shut out from companies like this.
mgfist commented on Databricks is raising a Series K Investment at >$100B valuation databricks.com/company/ne... · Posted by u/djhu9
Whenever companies release glowing fluff PR about their amazing financials they key word in there is “non-GAAP.”
i.e. when we exclude a bunch of pesky costs and other expenses that are the reason we’re not doing so well, we’re actually doing really well!
Non-GAAP has its place, but if used to say the company is doing well (vs like actual accounting) that’s usually not a good sign. Real healthy companies don’t need to hide behind non-GAAP.
Yes but free cash flow is free cash flow, and that's what matters for survival (i.e. run-rate). So long as fcf is positive, you'll never go bankrupt.
Really what they don't tell you is how much SBC they have. That's what crushes public tech stocks so much. They'll have nice fcf, but when you look under the hood you realize they're diluting you by 5% every year. Take a look at MongoDB (picked one randomly). It went public in 2016 with 48.9m shares outstanding. Today, it has 81.7m shares outstanding. 67% dilution in 9 years.
mgfist commented on Databricks is raising a Series K Investment at >$100B valuation databricks.com/company/ne... · Posted by u/djhu9
Why don't early investors put clauses in their investment to protect themselves against being screwed over by later investors? It seems like an obvious thing to ask for if you're giving someone a lot of money, so I'm assuming there must be a very good reason it's not done.
Early investors (the main ones at least) usually get pro-rate rights - which means you can invest in later rounds to maintain your ownership percentage (i.e a later round dilutes your ownership, so you invest a bit until the ownership stays the same).
But the pref stack always favors later investors, partly because that's just the way it's always been, and if you try to change that now no one will take your money, and later investors will not want to invest in a company unless they get the senior liquidity pref.
mgfist commented on Databricks is raising a Series K Investment at >$100B valuation databricks.com/company/ne... · Posted by u/djhu9
This. To me if you are still unprofitable after 15 years you are not really a business.
However genuinely curious about the thesis applied by the VC’s/Funds that invest in such a late stage round? Is it simply they are taking a chance that they won’t be the last person holding the potato? Like they will get out in series L or M rounds or the company may IPO by then. Either ways they will make a small return? Or is the calculus diff?
> However genuinely curious about the thesis applied by the VC’s/Funds that invest in such a late stage round
1) It's evaluated as any other deal. If you model out a good return quantitatively/qualitatively, then you do the deal. Doesn't really matter how far along it is.
2) Large private funds have far fewer opportunities to deploy because of the scale. If you have a $10B fund, you'd need to fund 2,000 seed companies (at a generous $5m on $25m cap). Obviously that's not scalable and too diversified. With this Databricks round, you can invest a few billion in one go, which solves both problems.
mgfist commented on What if A.I. doesn't get better than this? newyorker.com/culture/ope... · Posted by u/sundache
> inference is very expensive
I am surprised that this claim keeps getting made, given the observed prices.
Even if one thinks that the losses of big model providers are due to selling below operating costs (rather than below that plus training costs plus the cost of growth), then even big open-weights models that need beefy machines, look like they eventually* amortise the cost so low that electricity is what matters; so when (and *only* when) the quality is good enough, inference is cheaper than the food needed to have a human work for peanuts — and I mean literally peanuts, not metaphorical peanuts, as in the calories and protein content of bags of peanuts sufficient to not die.
* this would not happen if computers were still following the improvements trends of the 90s, because then we'd be replacing them every few years; a £10k machine that you replace every 3 years cost you £9.13/day even if it did nothing.
https://www.tesco.com/groceries/en-GB/products/300283810 -> £0.59 per bag * (2500 per day/645 per bag) = £2.29/day; then combine your pick about which model, which model of home server, electricity costs etc. with your estimate of how many useful tokens a human does in 8,760 hours per calendar year given your assumptions about hours per working week and days of holiday or sick leave.
I know that even just order-of 100k useful tokens is implausible for any human because that would be like writing a novel a day, every day; and this article (https://aichatonline.org/blog-lets-run-openai-gptoss-officia...) claims a Mac Studio can output 65.9/second = 65.9 * 3600 * 24 = 5,693,760 / day or ~= 2e9/year, compare to a deliberate over-estimate of human output (100k/day * 5 days a week * 47 weeks a year = 2.35e7/year)
The top-end Mac Studio has a maximum power draw of 270 W: https://support.apple.com/en-us/102027
270 W for *at least (2e9/year / 2.35e7/year) 85 times* the quantity (this only matters when the quality is sufficient, and as we all know AI often isn't that good yet) of output that a human can do with 100 W, is a bit over 31 times the raw energy efficiency, and electricity is much cheaper than calories — cheaper food than peanuts could get the cost of the human down to perhaps about £1/day, but even £1/day is equivalent to electricity costing £1/(24 hours * 100 W) = £0.416666… / kWh
Running a local model is not an apples comparison. Yes, if you run a small model 24/7 without a care for output latency and utilization is completely static with no bursts, then it can look cheap. But most people want output now, not in 10 hours. And they want it from the best models. And they want large context windows. And when you combine that with serving millions of users, it gets complicated and expensive.
mgfist commented on The beauty of a text only webpage albanbrooke.com/the-beaut... · Posted by u/speckx
> I would pay good money to watch a clear-glasses-framed youngster pitch Buffet on turning the BH website into a progressive web app.
How about pitching an hour of work to make it easy to read on mobile? Not that I think BH cares, but in this day and age making it layout nicely on mobile is the least you can do and isn’t particularly difficult anymore.
> How about pitching an hour of work to make it easy to read on mobile? Not that I think BH cares, but in this day and age making it layout nicely on mobile is the least you can do and isn’t particularly difficult anymore.
I think it looks great on mobile. It's fast as shit and I'm still just a 2 clicks away from an annual report. Frankly I often prefer the desktop layout even on mobile.
mgfist commented on OpenAI raises $8.3B at $300B valuation nytimes.com/2025/08/01/bu... · Posted by u/mfiguiere
> Still, it's pretty clear Enron and OpenAI are massively different companies.
They are. But there are enough parallels to me to be wary of them and their competitors. To me the amount of investments and hype doesn't match what's essentially a fancy autocomplete.
Well the biggest difference is most of the big foundation labs (OpenAI, Anthropic, xAI) are private companies, so it's not like you have to go out of your way to not hold any equity. This wasn't the case with Enron which was part of the SP500 and thus included in many passive funds.
You could say there's spillover with Nvidia and the clouds and such, but that still makes this a different case than Enron. It could be like the dotcom bubble, but bubbles != fraud.
mgfist commented on What if A.I. doesn't get better than this? newyorker.com/culture/ope... · Posted by u/sundache
What happens is they go out of business: "these firms spent five hundred and sixty billion dollars on A.I.-related capital expenditures in the past eighteen months, while their A.I. revenues were only about thirty-five billion."
DeepSeek (and the like) will prevent the kind of price increases necessary for them to pay back hundreds of billions of dollars already spent, much less pay for more. If they don't find a way to make LLMs do significantly more than they do thus far, and a market willing to pay hundreds of billions of dollars for them to do it, and some kind of "moat" to prevent DeepSeek and the like from undercutting them, they will collapse under the weight of their own expenses.
DeepSeek is also undercutting itself. No one is making a profit here, everyone is trying to gobble market share. Even if you have the best model and don't care to make a dime, inference is very expensive.