Is synthesizing the right word here?

Buying competition is par for the course for near-monopolies in their niches. As long as the scale differences in value are still very large, you can avoid competition relatively cheaply, while the acquired still walk away with a lot of money.

For Amazon “renting servers” at very high margin is their cash cow. For many competitors it’s more of a side business or something they’re willing to just take far lower margin on. Amazon needs to keep the markup high. Take away the AWS cash stream and the whole of Amazon’s financials start to look ugly. That’s likely driving the current panic with its leadership.

Culturally Amazon does really well when it’s an early mover leader in a space. It really struggles, and its leadership can’t navigate, when it’s behind in a sector as is playing out here.

Does he have this net worth because what he is doing or despite what he is doing? :-)

Correlation does not imply causation. Attribution is hard.

A better way to look at it is that the absolute number 1 priority for google since they first created a money spiggot throguh monetising high-intent search and got the monopoly on it (outside of Amazon) has been to hold on to that. Even YT (the second biggest search engine on the internet other than google itself) is high intent search leading to advertising sales conversion.

So yes, google has adopted and killed lots of products, but for its big bets (web 2.0 / android / chrome) it's basically done everything it can to ensure it keeps it's insanely high revenue and margin search business going.

What it has to show for it is basically being the only company to have transitioned as dominent across technological eras (desktop -> web2.0 -> mobile -> (maybe llm).

As good as OpenAI is as a standalone, and as good as Claude / Claude Code is for developers, google has over 70% mobile market share with android, nearly 70% browser market share with chrome - this is a huge moat when it comes to integration.

You can also be very bullish about other possible trends. For AI - they are the only big provider which has a persistent hold on user data for training. Yes, OpenAI and Grok have a lot of their own data, but google has ALL gmail, high intent search queries, youtube videos and captions, etc.

And for AR/VR, android is a massive sleeping giant - no one will want to move wholesale into a Meta OS experience, and Apple are increasingly looking like they'll need to rely on google for high performance AI stuff.

All of this protects google's search business a lot.

Don't get me wrong, on the small stuff google is happy to let their people use 10% time to come up with a cool app which they'll kill after a couple of years, but for their big bets, every single time they've gone after something they have a lot to show for it where it counts to them.

Hopefully some big players, like FB bankrupt themselves.

There's a bunch of ways AI is improving itself, depending on how you want to interpret that. But it's been true since the start.

1. AI is used to train AI. RLHF uses this, curriculum learning is full of it, video model training pipelines are overflowing with it. AI gets used in pipelines to clean and upgrade training data a lot.

2. There are experimental AI agents that can patch their own code and explore a tree of possibilities to boost their own performance. However, at the moment they tap out after getting about as good as open source agents, but before they're as good as proprietary agents. There isn't exponential growth. There might be if you throw enough compute at it, but this tactic is very compute hungry. At current prices it's cheaper to pay an AI expert to implement your agent than use this.

It is far from clear. There may well be emergent hierarchies of more abstract thought at much higher numbers of weights. We just don't know how a transformer will behave if one is built with 100T connections - something that would finally approach the connectome level of a human brain. Perhaps nothing interesting but we just do not know this and the current limitation in building such a beast is likely not software but hardware. At these scales the use of silicon transistors to approximate analog curve switching models just doesn't make sense. True neuromorphic chips may be needed to approach the numbers of weights necessary for general intelligence to emerge. I don't think there is anything in production at the moment that could rival the efficiency of biological neurons. Most likely we do not need that level of efficiency. But it's almost certain that stringing together a bunch of H100s isn't a path to the scale we should be aiming for.

Even assuming a company gets to AGI first this doesn't mean another one will follow.

Suppose that FooAI gets to it first: - competitors may get there too in a different or more efficient way - Some FooAI staff can leave and found their own company - Some FooAI staff can join a competitor - FooAI "secret sauce" can be figured out, or simply stolen, by a competitor

At the end of the day, it really doesn't matter, the equation AI === commodity just does not change.

There is no way to make money by going into this never ending frontier model war, price of training keeps getting higher and higher, but your competitors few months later can achieve your own results for a fraction of your $.

As more opens up in OSS and academic space, their knowledge and experience will either be shared, rediscovered, or become obsolete.

Also many of the people are coasting on one or two key discoveries by a handful of people years ago. When Zuck figures this out he gonna be so mad.

I tend personally to stick with ChatGPT most of the time, but only because I prefer the "tone" of the thing somehow. If you forced me to move to Gemini tomorrow I wouldn't be particularly upset.

Outside of computer, "the moat" is also data to train on. That's an even wider moat. Now, google has all the data. Data no one else has or ever will have. If anything, I'd expect them to outclass everyone by a fat margin. I think we're seeing that on video however.

I don't know what you are talking about. I use Gemini on a daily basis and I honestly can't tell a difference.

We are at a point where training corpus and hallucinations makes more of a difference than "model class".

xAI seems to be the exception, not the rule

I wouldn't be surprised if the likes of Anthropic wasn't paying AWS for its compute.

As the saying goes, the ones who got rich from the gold rush were the ones selling shovels.

Companies like OpenAI and Anthropic are still incredibly risky investments especially because of the wild capital investments and complete lack of moat.

At least when Facebook was making OpenAI's revenue numbers off of 2 billion active users it was trapping people in a social network where there were real negative consequences to leaving. In the world of open source chatbots and VSClone forks there's zero friction to moving on to some other solution.

OpenAI is making $12 billion a year off of 700 million users [1], or around $17 per user annually. What other products that have no ad support perform that badly? And that's a company that is signing enterprise contracts with companies like Apple, not just some Spotify-like consumer service.

[1] This is almost the exact same user count that Facebook had when it turned its first profit.

The two are effectively separate businesses with a completely separate customer base.

In general these fall into the category of things humans cannot do at the scale and speed necessary to run SaaS companies.

Many of the things LLMs attempt to do are things people already do, slowly and relatively accurately. But until hallucinations are rare, slow expensive humans will typically need to be around. The AI booster’s strategy of ignoring/minimizing hallucinations or equivocating with human fallibility doesn’t work for businesses where reliability is important.

Note that ML algorithms are highly imperfect as well. Uber’s prices aren’t optimal. Google search surfaces tons of spam. But they are better than the baseline of no service exists.

https://kiro.dev/

Pay no attention to the cracks that are showing. Nevermind the chill. Everything is fine.

I'm curious if non prime members make up a big market for Alexa. I rarely use my smart devices for anything beyond lights, music, and occasional Q&A, and certainly can't see myself paying 20$/month for it.

Only thing it can do is set a timer, turn off a light and play music.

It is still nice, but it’s so frustrating when a question pops into my mind, and I accidentally ask Alexa just to get reminded yet again how useless it is for everything but the most basic tasks.

And no, I won’t pay 240 dollars a year so that I can get a proper response to my random questions that I realistically have only about once a week.

But the project is pretty much dead, it was supposed to launch in February or March and is still not anywhere close to being out.

https://en.m.wikipedia.org/wiki/Amazon_Robotics

Well, let’s take your life. Your life is about 3 billion seconds (100 year life). That’s just 3 billion next-tokens. The thing you do on second N is just, as a whole, a next token. If next-token prediction can be scaled up such that we redefine a token from a part of language to an entire discrete event or action, then it won’t be hard for the model to just know what you will think and do … next. Memory in that case is just the next possible recall of a specific memory, or next possible action, and so on. It doesn’t actually need all the memory information, it just needs to know that that you will seek a specific memory next.

Why would it need your entire database of memories if it already knows you will be looking for one exact memory next? The only thing that could explode the computational cost of this is if dynamic inputs fuck with your next token prediction. For example, you must now absolutely think about a Pink Elephant. But even that is constrained in our material world (still bounded physically, as the world can’t transfer that much information through your senses physically).

A human life up to this exact moment is just a series of tokens, believe it or not. We know it for a fact because we’re bounded by time. The thing you just thought was an entire world snapshot that’s no longer here, just like an LLM output. We have not yet trained a model on human lives yet, just knowledge.

We’re not done with the bitter lesson.

> For example, I can't easily run models that need GPUs without launching classic EC2 instances.

Yeah okay, but you can run most entreprise-level models via Bedrock.

Fargate and lambda are fundamentally very different from EC2/nitro under the hood, with a very different risk profile in terms of security. The reason you can't run GPU workloads on top of fargate and lambda is because exposing physical 3rd-party hardware to untrusted customer code dramatically increases the startup and shutdown costs (ie: validating that the hardware is still functional, healthy, and hasn't been tampered with in any way). That means scrubbing takes a long time and you can't handle capacity surges as easily as you can with paravirtualized traditional compute workloads.

There are a lot of business-minded non-technical people running AWS, some of which are sure to be loudly complaining about this horrible loss of revenue... which simply lets you know that when push comes to shove, the right voices are still winning inside AWS (eg: the voices that put security above everything else, where it belongs).

https://www.cnbc.com/2025/08/08/chatgpt-gpt-5-openai-altman-...

> Last year, OpenAI expected about $5 billion in losses on $3.7 billion in revenue. OpenAI’s annual recurring revenue is now on track to pass $20 billion this year, but the company is still losing money.

> “As long as we’re on this very distinct curve of the model getting better and better, I think the rational thing to do is to just be willing to run the loss for quite a while,” Altman told CNBC’s “Squawk Box” in an interview Friday following the release of GPT-5.

Selling compute for less than it cost you will have as much revenue as you want to pay for.