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Cool story

Learning languages is a cool type of talent/intelligence

Too bad I suck at it

Don't have any meaningful advice for you, but, I hope this new struggle will make you stronger and lead you to better opportunities

This article is a work of fiction, interesting to read as entertainment, but definitely not a rigorous model.

The premise that just because a lot of investment goes into AI infrastructure means that it has to displace the entire workforce globally is wrong to begin with.

It even admits at the end that the scenario presented is unlikely: "I don’t want to dwell on whether AI can do what these companies claim. It may well be able to, though the current evidence suggests the gap between pitch and product is vast, and serious economists think the productivity gains are a fraction of what the industry projects."

It begs the question, what's the point in being alarmist and anxious about something that even you claim it's unlikely to happen?

Thanks for that

I'm starting my first coding project with Claude Code and your comment gave me some helpful topics to study

I'd qualify your point that Anthropic and OpenAI can't raise prices, that is as of right now. Once the industry will go through a phase of consolidation and the bigger players will have some moat around their product, they'll have more pricing power.

Your last point is common sense in my opinion, I agree with it. At the end of the day most employees are (by definition) of average intelligence and most businesses are average in complexity. Thus, it is logical that average tools (AI models) should do the job for most people and most businesses.

Not OP, but it is a known fact that the cumulative profits of the airlines industry (in US) over it's history has been basically 0. We can say that essentially airlines are in business to support other businesses. I believe this is what OP might've been referring to.

Great points. - At the end of the day those are still private companies (albeit huge ones), so we can only speculate about the state of their private financial situation. Once they will decide it's the right time to IPO, they'll publish all their financials and we'll start to have a clearer picture. - Later, each company will slightly specialize and have a different go-to-market strategy, which will allow us to understand on a deeper level what works in the market and what doesn't (think about how Facebook, Instagram and TikTok are all huge universal social media platforms, but, each with a different target audience and different user base). - Finally, the market will go through a consolidation phase in which winners will gobble up the losers and then the incumbents will have a real moat (against new-comers) and real pricing power on their user base.

Bankruptcies? The winners will gobble up the losers and the few remaining players will have pricing power. Don't be naive thinking that OpenAI or Anthropic can possibly go bankrupt. There will always be someone happy to buy them up for a nice price. Yes, the market will have to go through a consolidation phase though.

The IPOs are months away, potentially 6 months or more. We're in a volatile macro environment. AI companies have all the incentives to not create higher expectations regarding their financial situation a long time before the IPO. Obviously at IPO they will have to disclose their full financial situation.

The market is super hyped anyway for their IPOs. If they raise investors expectations now and things change until the IPO, investors will be disappointed. It's a lose-lose proposition.

The smart play for any company is to keep their cards close to their chest until close to the IPO time.

Once the land grab is over, the market will consolidate and the winners will absorb the losers. Then the few winners will be the only ones with real capital to train frontier models and will have true pricing power. Similar to how social media companies or the gig-economy benefits from network effects, AI companies will benefit from having the lion's share of paying customers (that also constantly feed in more data to train the models on).

When they will actually file to go public, their numbers will be intensely scrutinized. That's all that global headlines will be talking about for weeks on end. Why would they create forward expectations before it's necessary?

Of course they don't want to create forward expectations in a volatile macro environment, with the public listing being 6 months out.

There is no legal minimum free float requirement before deregistration in US, however, different exchanges have different rules

Essentially, a stock has to stay above 1$ per share, have a minimum market cap of $15m, minimum 400 shareholders and "adequate" liquidity If it meets those 4 criteria, it's essentially not at risk of deregistration