streetcat1

Wait until token based pricing. And then the open AI / antropic IPO (This will be the top).

Do you mind open sourcing the code, I want to take a look.

AKA intellectual skills atrophy.

The strong domino supports the weak domino.

For some reason miss two important points:

1) The AI code maintainence question - who would maintain the AI generated code 2) The true cost of AI. Once the VC/PE money runs out and companies charge the full cost, what would happen to vibe coding at that point ?

How do you know that he works at Meta?

Nowhere they are stuck in the prisoner's dilemma.

Its not the AI that causing it, but the AI race. Companies need to pay for the large capex.

They all spend with one purpose - replacing expensive humans, saying other wise does not make sense.

Any other app does not have moat - anyone can do the same app if it basically wrap the LLM.

If anything, LLM just destroy thier current moat, I.e. if everything is getting behind a chat interface, no one would would see ads.

Closed the labor arbitrage.

You are not taking into account section 174, It takes you 15 years to depreciate foreign salary vs first year (post the BBB).

You forgot the tax advantage and the protection against inflation (if you have to fix rate mortatge).

The main benefit of YC startups is not that they have 10x engineers but rather that they are starting from scratch; hence, the AI works well in a greenfield project. Enterprise customers are a totally different ball game. Ninety percent is legacy critical code, and some decisions are not made to optimize the dev time.

Also, for Y Combinator startup, let say that the AI introduces a bug. Since there are no customers, no one cares. Now imagine the same AI introduces a bug in the flight reservation system that was written 20 years ago.

Dont worry.

The competition for big LLM AI companies is not other big LLM AI companies, but rather small LLM AI companies with good enough models. This is a classic innovator dilemma. For example, I can imagine a team of cardiologists creating a fine tune LLM model.

Give them non boring work.

Probably due to money ? I think they are changing their corporate structure and hence some people might not get the equity they were hoping for.

The question is what priced in, not what is publicly known. Buy put if you think that the market does not know the above reasoning.

Ofcourse they don't work. If they did work why would anybody tell you about them?. The fact that they use thier time to tell you about them rather than trading and making money means that they do not work.

The article fails to describe the core agent algorithm. I.e. how does the agent decide what to code ? If the agent did coded something, how does the agent decide that the code is correct ? (Ecepsically if the core model is based on auto complete and lacks any logic).

Like if the agent decides to search for a file X on task Y why file X and not file Z ?

Sounds like you will be a good fit.

So what will happen if the AI has a bug? Who is liable, you?

You are correct.

So don't be fooled. The cloud computing business model is a lot like a gym. I.e. they already have the capacity in place, so they need warm bodies. A lot of those cloud ramp up, is in the form of rebates or giving money to customers to move between clouds.

Use streaming ?

So can't you use Leverage? If the software is already working what kind of funds do you need to keep going?

So why you don't change strategy after the first few days? I.e. why don't you stop the strategy as soon as it starts to lose?

I am not sure, it depends on the cost. If they charge per token, a large context will mostly be irrelevant. For some reason, the article did not mention it.

So a quick trick.

To get you hooked, they MUST let you win. This will usually happen in the first rounds of the gamble. At that point, you MUST leave the game.

Not sure, it might be a way to judge which employee is loyal to the company