> Profitable isn’t related to “worth the dollars”. You need to look at income and how much is being reinvested into growth. Amazon famously remained unprofitable due to reinvestment and waiting for them to become profitable before investing was a bad bet.
Amazon met profitability requirements and went into the SP500 at around $2.40 in November 2005. Two years before it was $2.70. Six Years before it was $4.40.
Two years _after_ listing it was $4.50. Six years after it was ~$10.
Waiting for profitability seems like it was a good bet.
It has been crazy seeing the explosion of Polymarket and Kalshi in comparison to Betfair.
Betfair doesn't present itself as anything other than what it is: a gambling exchange where the punters make the book. In my 10+ years time of using it they despite some occasionally shoddily worded markets they have only properly screwed up a market twice (The Australian Open in 2022 when Djokovic was denied entry to Australia and Betfair basically forgot the winners market was cross matched when they went to remove him and the 2020 Presidential election where they suspended the market when Trump went to hospital with Covid despite the fact the market rules said that the market would still settle if any participant died and obviously most people had been taking into account that both candidates being mega ancient whilst a global pandemic was happening might be a factor). Betfair is boring, staid and reliable.
Compare that to Kalshi and Polymarket who do everything they can to pretend they are not gambling platforms. It's kind of gross.
> (so infrastructure for clean water and all chemicals)
Fabs are some of the most complex chemical engineering sites (dealing with some of the most dangerous substances) in the world. So don't underestimate the complexity of this part.
Every year I ask the latest version of Chat GPT a basic facts question about rugby results. It almost always gets it wrong - even when it does web search and cites sources. Wrong scores, hallucinated matches, wrong locations - just gob smacking amounts of wrongness.
The latest "Thinking" version gets it reliably right but spent about 3 minutes coming up with the answer that 10 seconds of googling answers.
So I don't believe we are currently in a situation where LLMs are an effective replacement for search engines.
> It's also not very great at meeting summaries especially those where many speakers are in a room on the same microphone.
It is astonishingly poor at this. My intuition was that it should be good at this (it is basically a translation problem right? And LLMs are fundamentally translation systems) but the practical results are so poor. Not just mis-identifying speakers (frequently saying PersonX responded to PersonX) but managing complete opposite conclusions from what was actually said.
I'm genuinely intrigued as to what approaches have been taken in this space and what the "hard problem" is that is stopping it being good.