Declines in stock price are very common after IPOs, to the point that post-IPO stocks are considered a bad investment until the company can establish a sustainable trend of profitability and growth.
What happens with IPOs is this: a lack of market common knowledge of what the value of a company should be gets combined with the IPO price set by the company and their advisors (usually a big investment bank such as Goldman Sachs). Just before the stock begins trading, you can think of all investors as being divided into 2 groups:
People who are really hyped about the company and think the stock price will go up from the IPO price
Everyone else, who are more skeptical and think the stock price will go down
When the stock actually begins trading, the group 1 people often tend to discover that group 2 is a lot bigger than they are, and there are not many buyers for the high priced post-IPO stock. They are then forced to cut their losses and sell before they lose even more money. This process can result in the stock changing hands quite rapidly until it finally settles to a true market price (what it’s actually worth). This also has the effect of communicating information about the true price of the stock to everyone involved: the hyped people get whacked in the face with reality, and the skeptical people learn that the company does have some value (usually anyway, there have been exceptions).
The true profitability of frontier AI labs remains to be established. Right now we’re constantly seeing this cycle of new models being trained, released, and then scaled back with smaller context windows and hidden behind model routers to avoid paying the full costs of inference. But these tricks blunt the usefulness of the frontier models, leading to competitors stealing customers with their latest models. It’s an arms race where the costs of training dwarf everything else, but even the costs of inference may not be profitable.
At the same time, Chinese companies keep releasing new open weight models which are severely undermining the value proposition of the frontier AI labs. Customers of the frontier labs may be unwilling to subsidize model training if they can bypass that cost and run the open weight models on their own hardware.
Regular industries are always working on the next possible improvement while also having either a path to profitability or are also profitable depending on how long they have existed. Sure, some can take a decade or more to get there, but they are actually on a path with significant income from their work and investment to get past the growing pains.
The larges AI companies have dumped massive amounts of money with minuscule income and exist entirely off of investments with no actual path to profitability other than wishful thinking. There isn’t enough economy to make up for the investments and it isn’t like AI is magically going to create more economy when the goal is to put everyone out of work to increase profits.
The true profitability of frontier AI labs remains to be established.
It has already been established: There is no profitability from frontier AI labs because they have sunk a volume of money into the whole shit show that will never, ever be recouped.
If you just ignore the eventual bankruptcies or stop ignoring the massive waster and start counting at some future date then there could be pretend profits.
Ahhh this is where we disagree: on the meaning of the word established. To me, established means widely accepted and recognized as true. To you, it means obviously true.
This is where the IPO process I described above comes into play. It takes information and establishes it to become widely known and without doubt. The current existence of AI believers/boosters is evidence that AI profitability is not established, even if it is true.
Declines in stock price are very common after IPOs, to the point that post-IPO stocks are considered a bad investment until the company can establish a sustainable trend of profitability and growth.
What happens with IPOs is this: a lack of market common knowledge of what the value of a company should be gets combined with the IPO price set by the company and their advisors (usually a big investment bank such as Goldman Sachs). Just before the stock begins trading, you can think of all investors as being divided into 2 groups:
When the stock actually begins trading, the group 1 people often tend to discover that group 2 is a lot bigger than they are, and there are not many buyers for the high priced post-IPO stock. They are then forced to cut their losses and sell before they lose even more money. This process can result in the stock changing hands quite rapidly until it finally settles to a true market price (what it’s actually worth). This also has the effect of communicating information about the true price of the stock to everyone involved: the hyped people get whacked in the face with reality, and the skeptical people learn that the company does have some value (usually anyway, there have been exceptions).
SpaceX is now mostly xAI and AI companies are known for profitability and growth trends so obviously this is all going to work out.
The true profitability of frontier AI labs remains to be established. Right now we’re constantly seeing this cycle of new models being trained, released, and then scaled back with smaller context windows and hidden behind model routers to avoid paying the full costs of inference. But these tricks blunt the usefulness of the frontier models, leading to competitors stealing customers with their latest models. It’s an arms race where the costs of training dwarf everything else, but even the costs of inference may not be profitable.
At the same time, Chinese companies keep releasing new open weight models which are severely undermining the value proposition of the frontier AI labs. Customers of the frontier labs may be unwilling to subsidize model training if they can bypass that cost and run the open weight models on their own hardware.
Regular industries are always working on the next possible improvement while also having either a path to profitability or are also profitable depending on how long they have existed. Sure, some can take a decade or more to get there, but they are actually on a path with significant income from their work and investment to get past the growing pains.
The larges AI companies have dumped massive amounts of money with minuscule income and exist entirely off of investments with no actual path to profitability other than wishful thinking. There isn’t enough economy to make up for the investments and it isn’t like AI is magically going to create more economy when the goal is to put everyone out of work to increase profits.
Okay, so we don’t disagree then?
No, we don’t.
It has already been established: There is no profitability from frontier AI labs because they have sunk a volume of money into the whole shit show that will never, ever be recouped.
If you just ignore the eventual bankruptcies or stop ignoring the massive waster and start counting at some future date then there could be pretend profits.
Ahhh this is where we disagree: on the meaning of the word established. To me, established means widely accepted and recognized as true. To you, it means obviously true.
This is where the IPO process I described above comes into play. It takes information and establishes it to become widely known and without doubt. The current existence of AI believers/boosters is evidence that AI profitability is not established, even if it is true.
Profit is something objective and measurable. People’s impression and belief about something doesn’t magically create profit.
That wasn’t the argument. The argument is on the meaning of the word established