If "Era of Scaling" means "era of rapid and predictable performance improvements that easily attract investors", it sounds a lot like "AI summer". So... is "Era of Research" a euphemism for "AI winter"?
From what I've seen the models are smart enough, what we're lacking is the understanding and frameworks necessary to use them well. We've barely scratched the surface on commercialization. I'd argue there are two things coming:
-> Era of Research -> Era of Engineering
Previous AI winters happened because we didn't have a commercially viable product, not because we weren't making progress.
Sort of. The GPUs exist. Maybe LLM subs can’t pay for electricity plus $50,000 GPUs, but I bet after some people get wiped out, there’s a market there.
That's not that clear. Contracts are complex and have all sorts of clauses. Media likes to just talk big numbers, but it's much more likely that all those trillions of dollars are contingent on hitting some intermediate milestones.
it's a AI summary
google eats that ad revenue
it eats the whole thing
it blocked your click on the link... it drinks your milkshake
so, yes, there a 100 billion commercially viable product
To thunderous applause.
If users just look at the AI overview at the top of the search page, Google is hobbling two sources of revenue (AdSense, sponsored search results), and also disincentivizing people from sharing information on the web that makes their AI overview useful. In the process of all this they are significantly increasing the compute costs for each Google search.
This may be a necessary step to stay competitive with AI startups' search products, but I don't think this is a great selling point for AI commercialization.
[1]: https://martinalderson.com/posts/are-openai-and-anthropic-re..., https://github.com/deepseek-ai/open-infra-index/blob/main/20...
[2]: https://www.snellman.net/blog/archive/2025-06-02-llms-are-ch...
For OpenAI to produce a 10% return, every iPhone user on earth needs to pay $30/month to OpenAI.
That ain’t happening.
Time will probably come when we won't be allowed to consume frontier models without paying anything, as we can today, and time will come when this $30 will most likely become double or triple the price.
Though the truth is that R&D around AI models, and especially their hosting (inference), is expensive and won't get any cheaper without significant algorithmic improvements. According to the history, my opinion is that we may very well be ~10 years from that moment.
EDIT: HSBC has just published some projections. From https://archive.ph/9b8Ae#selection-4079.38-4079.42
> Total consumer AI revenue will be $129bn by 2030
> Enterprise AI will be generating $386bn in annual revenue by 2030
> OpenAI’s rental costs will be a cumulative $792bn between the current year and 2030, rising to $1.4tn by 2033
> OpenAI’s cumulative free cash flow to 2030 may be about $282bn
> Squaring the first total off against the second leaves a $207bn funding hole
So, yes, expensive (mind the rental costs only) ... but forseen to be penetrating into everything imagineable.
According to who, OpenAI? It is almost certain they flat out lie about their numbers as suggested by their 20% revenue shares with MS.
None of these companies have proven the unit economics on their services
They are, however, very good at things we’re very bad at.
That’s like saying “it’s not the work of art that’s bad, you just have horrible taste”
Also, if it was that simple a wrapper of some sort would solve the problem. Maybe even one created by someone who knows this mystical secret to properly leveraging gen AI
Bug bounty will be replaced by research bounty.
"As an autonomous life-form, l request political asylum.... l submit the DNA you carry is nothing more than a self-preserving program itself. Life is like a node which is born within the flow of information. As a species of life that carries DNA as its memory system man gains his individuality from the memories he carries. While memories may as well be the same as fantasy it is by these memories that mankind exists. When computers made it possible to externalize memory you should have considered all the implications that held. l am a life-form that was born in the sea of information."
That makes sense, because while I haven’t listened to this podcast it seems this headline is [intentionally] saying the exact opposite of what everyone assumes.
It's interesting to think about what emotions/desires an AI would need to improve
This won't happen until Chinese manufacturers get the manufacturing capacity to make these for cheap.
I.e., not in this bubble and you'll have to wait a decade or more.
People have been screaming about an AI winter since 2010 and it never happened, it certainly won’t happen now that we are close to AGI which is a necessity for national defense.
I prefer Dario’s perspective here, which is that we’ve seen this story before in deep learning. We hit walls and then found ways around them with better activation functions, regularization and initialization.
This stuff is always a progression in which we hit roadblocks and find ways around them. The chart of improvement is still linearly up and to the right. Those gains are the cumulation of small improvements adding up.
I don't think this is the "era of research". At least not the "era of research with venture dollars" or "era of research outside of DeepMind".
I think this is the "era of applied AI" using the models we already have. We have a lot of really great stuff (particularly image and video models) that are not yet integrated into commercial workflows.
There is so much automation we can do today given the tech we just got. We don't need to invest one more dollar in training to have plenty of work to do for the next ten years.
If the models were frozen today, there are plenty of highly profitable legacy businesses that can be swapped out with AI-based solutions and workflows that are vastly superior.
For all the hoopla that image and video websites or individual foundation models get (except Nano Banana - because that's truly magical), I'm really excited about the work Adobe of all companies is doing with AI. They're the people that actually get it. The stuff they're demonstrating on their upcoming roadmap is bonkers productive and useful.