This has been coming for a long time and it is why I use local models only. I'm willing to give up capabilities in exchange for being able to trust that whatever biases may exist in the models I do use remain static and predictable.

The hangover will be painful for some people/companies for sure

Future ain't what it used to be. The web is dead (worse actually, it's a putrid rotting zombie, destroying our children's lives and ours), but the internet will survive.

Even if unintentional, pushing of products is already happening. If you ask any AI for a tech stack to create a web app you'll get recommendations for Vercel, AWS and co. This is going to be the new SEO

The subsidy is blatantly obvious when you compare the cost of self-hosting versus subscription, and the subscription is dramatically dramatically cheaper.

The difference between Blue Apron and many AI tools is that the value add does exist. You can cut meal prep from your life, but by 2030, cutting whatever agentic code copilot exists by that point will be like cutting off your fingers for many workers and businesses.

Then the extortionate pricing can start rolling in

The bet is that the cost for delivering the same results will go down, through hardware or software advancements. This bet still seems reasonable based on how things have gone so far. Providers right now are willing to burn money acquiring a customer base, it's like really really expensive marketing.

I am somewhat baffled at the economic models of LLMs right now. Ever since MS decided to gift me a copilot on my desktop that appears to have no limits and is entirely servicable for a range of tasks I'm failing to immediately see the monetisation.

I feel like even trying to game the LLM into creating product placement is a relatively complex feat that might not be entirely reliable. Some of the groups who spend the most on advertising have the worst products, so is it going to be successful to advertise on a LLM that is one follow up question away from shitting on your product? I imagine instead of product placement, the token tap might simply be throttled and a text advert appear in the text loop, or an audio advert in a voice loop. Boring, old-school but likely profitable and easy to control. It lets us still use adsense but maybe a slightly different form of adsense that gets to parse the whole context window.

I don't think this is correct yet. At the moment the various companies are still competing for customers. Model scalability seems to still be improving and local models are still somewhat feasible on high powered devices.

I expect that somewhere between where it is now and superintelligence is where the consumers get cut off from intelligence improvements.

The zero-interest-rate money went into stocks. The stocks have now grown to monstrous valuations able to subsidize free products for decades. If in danger, there is a loot of leeway for layoffs in all tech companies. Whatsapp was 10 employees. The subsidy will go on

So what exactly is the "AI lifestyle subsidy"? The article doesn't seem entirely clear on it seeing as the last line essentially asks this same question. Some friends and I have been taking advantage of cheap GPU time from a company trying to break into that space, and of course lots of AI tools are being sold below cost but is that really it? Compare "GPU time is cheaper" to the classical "$10 steaks delivered directly to your house", I'm never going to get steaks delivered at the real price but I'm still going to rent a GPU when I need it even if the cost is sustainable. All these tools might get more expensive, or the models will get better so you don't need top end one, or maybe we'll just figure out how to run models for cheaper, but real steak prices and the cost of delivery have only gone up. I just don't think this is quite as comparable.

I just released a very minor update to one of my iOS apps.

The approval took 3 days. It hasn't taken 3 days in almost a decade.

The Mac version was approved in a couple of hours.

I'm quite sure that the reason for the delay is that Apple is being deluged by a tsunami of AI-generated crapplets.

Also, APNS server connections have suddenly slowed to a crawl. Same reason, I suspect.

As far as I'm concerned, the "subsidy" can't end fast enough.

I liked this. It got me thinking:

Are there any large consumer software companies (just software; no hardware or retail) that are not advertising based?

This blog post makes a historical analogy, which at best is useful for imagining what might happen when investors become less giddy about funding AI.

There are underlying trends that are directly opposed. Efficiency is improving, but with agents, people are finding new ways to spend more. How that plays out seems difficult to judge.

For consumers, maybe the free stuff goes away and spending $20/month on a subscription becomes normalized? Or do costs decline so much that the advertising-supported model (like Google search) works? Or does inference become cheap enough to do it on the client most of the time?

Meanwhile, businesses will likely be able to justify spending more.

I’m not sure. I’m paying for several different AI subscriptions and once things settle down, I’ll probably be paying for one or two, so I’m not sure that I’m benefiting in the sense that everyone is just moving very fast.

1833: first known case of "lose money on every sale but make it up on volume." Amazing. Birth of postmodern capitalism right there.

https://barrypopik.com/blog/we_lose_money_on_every_sale_but_...

In 2024 OpenAI generated some $3.5B in revenue and still lost like $5B. It means they spent something like $8.5B to run this thing [1].

They would have lost less money if they had been selling dollars at 50 cents.

[1] https://www.cnbc.com/2024/09/27/openai-sees-5-billion-loss-t...

The problem is that it's really hard to capture the market when there are so many well financed players competing. Plus distilled local models are within 10% or so of the frontier models and are fine for most questions so you could see a shift where local dominates and you'll only need to go to the cloud for hard problems. Finally, I think most people are willing to pay for AI - it's more utility than a streaming service or a newspaper.

I think the author hasn't considered the potential for improvements to ad blocking algorithms, particularly considering that local open source models could be directed toward ad filtering for a wide variety of content, including other LLM interactions. I would bet (and hope) that subscriptions are going to win out over ad revenue models.