That's a quite different thing, OpenAI has billions of USD/year cash flow, and when you have that there's many many potential way to achieve profitability on different time horizons. It's not a situation of chance but a situation of choice.
Anyway, how much that matters for an investor is hard to form a clear answer to - investors are after all not directly looking for profitability as such, but for valuation growth. The two are linked but not the same -- any investor in OpenAI today probably also places themselves into a game of chance, betting on OpenAI making more breakthroughs and increasing the cash flow even more -- not just becoming profitable at the same rate of cash flow. So there's still some of the same risk baked into this investment.
But with a new startup like LeCun's is going to be, it's 100% on the risk side and 0% on the optionality side. The path to profitability for a startup would be something like 1) a breakthrough is made 2) that breakthrough is utilized in a way that generates cash flow 3) the company becomes profitable (and at this point hopefully the valuation is good.)
There's a lot of things that can go wrong at every step here (aside from the obvious), including e.g. making a breakthrough that doesn't represent a defensible mote for your startup, failing to build the structure of the business necessary to generate cashflow, ... OpenAI et al already have a lot of that behind them, and while that doesn't mean that they don't face upcoming risks and challenges, the huge amount of cashflow they have available helps them overcome these issues far more easily than a startup, which will stop solving problems if you stop feeding money into it.
It's also because around 20 years ago there was a "reset" when we switched from x86 to x86_64. When AMD introduced x86_64, it made a bunch of the previously optional extension (SSE up to a certain version etc) a mandatory part of x86_64. Gentoo systems could already be optimized before on x86 using those instructions, but now (2004ish) every system using x86_64 was automatically always taking full advantage of all of these instructions*.
Since then we've slowly started accumulating optional extensions again; newer SSE versions, AVX, encryption and virtualization extensions, probably some more newfangled AI stuff I'm not on top of. So very slowly it might have started again to make sense for an approach like Gentoo to exist**.
* usual caveats apply; if the compiler can figure out that using the instruction is useful etc.
** but the same caveats as back then apply. A lot of software can't really take advantage of these new instructions, because newer instructions have been getting increasingly more use-case-specific; and applications that can greatly benefit from them will already have alternative code-pathes to take advantage of them anyway. Also a lot of the stuff happening in hardware acceleration has moved to GPUs, which have a feature discovery process independent of CPU instruction set anyway.
In my experiments claude4 opus generated by far the best code (for my taste and purposes) but it's also a pretty expensive model. I think I used up $40 in one evening of frantic vibe-coding.
It's even worse than that, because the way they extract the causal link is just a regex, so
"vaccines > autism"
because
"Even though the article was fraudulent and was retracted, 1 in 4 parents still believe vaccines can cause autism."
I think this could be solved much better by using even a modestly powerful LLM to do the causal extraction... The website claims "an estimated extraction precision of 83% " but I doubt this is an even remotely sensible estimate.
Is there any strong reason to use GeoParquet instead of straight up parquet if all I'm interested in is storing and operating on lat/lons?
I'm curious if it compresses them better or something like that. I see lots of people online saying it compresses well (but mostly compared to .shp or similar) but normal parquet (.gz.parquet or .snappy.parquet) already does that really well. So it's not clear to me if I should spend time investigating it...
I mostly process normal parquet with spark and sometimes clickhouse right now.
Anyway, how much that matters for an investor is hard to form a clear answer to - investors are after all not directly looking for profitability as such, but for valuation growth. The two are linked but not the same -- any investor in OpenAI today probably also places themselves into a game of chance, betting on OpenAI making more breakthroughs and increasing the cash flow even more -- not just becoming profitable at the same rate of cash flow. So there's still some of the same risk baked into this investment.
But with a new startup like LeCun's is going to be, it's 100% on the risk side and 0% on the optionality side. The path to profitability for a startup would be something like 1) a breakthrough is made 2) that breakthrough is utilized in a way that generates cash flow 3) the company becomes profitable (and at this point hopefully the valuation is good.)
There's a lot of things that can go wrong at every step here (aside from the obvious), including e.g. making a breakthrough that doesn't represent a defensible mote for your startup, failing to build the structure of the business necessary to generate cashflow, ... OpenAI et al already have a lot of that behind them, and while that doesn't mean that they don't face upcoming risks and challenges, the huge amount of cashflow they have available helps them overcome these issues far more easily than a startup, which will stop solving problems if you stop feeding money into it.