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tim sweeney is right and maybe I should reconsider my stance of epic games/ue
ai is going to be how most games get made in the future. this is an objective truth.
it used to be thousand of hours between your idea and something playable. and a lot of good ideas died once someone realized how much time and money they'd have to invest just to get a demo.
ai is a tool that deletes that gap. one person with taste can now do what used to only be accomplished by a studio.
and every tool that made building cheaper got called cheating first. and every time the people who are mad about it were defending the cost of entry.
so what happens when the cost of making a game goes to zero?
you get 100x more games. most are bad. a few are brilliant. the old gatekeepers won't like it. the shelves will fill up. the gatekeepers will lose control of what counts as a real game.
that's why the fight is about labels and not quality. you can't win on quality against something that's getting better every month.
sweeney knows this because he built the engine. he knows every layer of the stack gets automated, and the studios that adapt will ship circles around the ones that don't.
in 5 years "made with AI" will mean nothing. the only thing that matters is whether the game is good or not. "fun" or "not fun".
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@Filmbot There is a real economic cost to Valve’s foray into forced political disclosures. Besides taking 30%, they’re also taking a potentially even larger fraction of developers’ revenue and livelihood by undermining their business with these AI hate campaigns.
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Tesla is the only AV operator that can take riders to and from the F1 race in Austin, Texas this weekend. It’s important to understand why.
Tesla has long highlighted the advantages of its approach: an end-to-end neural network, no HD maps, and camera-only vision.
This weekend, the geofence for the race. It could do so quickly because, unlike other AV operators, it doesn’t need to HD-map an area before driving there safely.
It adapts on the fly and validates areas with minimal effort.
Congrats to the Robotaxi team for doing what no other company could.
Red is Tesla; blue is Waymo for reference.
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AI revenue is ~$200bn today. The capex already spent needs ~$900bn in annual revenue to earn an acceptable return, and next year's capex raises that bar by roughly another 700bn.
Everyone points to a tight spot market for compute. Spot is 1-2% of the market! Is anyone able to an investment bubble?
Short term, the market is a voting machine. Long term, it's a weighing machine.
The hard part is timing. 😅🤔
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Agentic engineering is engineering but with more leverage. What’s not to like?
If you know me, you're probably aware that I ignored AI for most of the last two years and got pretty depressed about it
I felt like AI stole the magic of what made software development fun, and felt like my career was over
In the last five weeks, a switch flipped
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Elon just said that most neoclouds will eventually die.
And we might already be seeing it play out.
Even if AI is the biggest tech revolution of our lifetime, that doesn't mean every company building AI infrastructure will survive.
Some will thrive. Many will struggle. Most d we've seen this before during the internet boom.
The internet changed the world, but plenty of companies that built the infrastructure went bankrupt along the way.
This time won't be different.
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My take: SaaS is dead. Software is charity now, make money elsewhere.
No one can make $$ selling the same features for years anymore.
Instead:
- Use good software to collect user data and sell data.
- Use good software to deliver outcome and sell outcome.
- Arbitrage tokens under the guise of selling software - buy bulk, sell retail.
- Use good software internally to drive your own margin higher for your services/products
- Build RSI agent in a domain that has the fastest learning loop so it has the most, latest expertise: e.g. a marketing agent that knows the latest tricks to get viral in X coz it's doing it for others every day and learn and evolve.
- Give away all your features for free, it's futile anyways. Open source them. Make money elsewhere.
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My theory is @Railway has been compromised for a while now - 4 months ago I had to rotate an openai key bc of sudden usage, and my twilio account has been suspended for suspicious activity twice in the same time frame. Both of these are only used for a personal tool nobody else knows about, and both are on railway. Only now that that incident happened did I put the pieces together.
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There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused.
The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc).
The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays).
Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false.
This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology.
Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions.
Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible.
This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use.
How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months.
I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.
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Alexandr Wang explains the number robotaxi companies don't publish: one human for every 3 to 5 driverless cars.
Wang, who built Scale AI, was asked on Y Combinator's Lightcone podcast what humans will still do once AI agents do the work.
"Most of a manager's job, speaking as a g out fires, dealing with problems, dealing with issues that come up."
"I think people often jump to this extreme reality... you're just going to manage the agents and you're going to live this kind of Victorian life where all your problems are solved. But no, I think it's still going to be pretty complicated, getting agents to coordinate well with one another and coordinating the workflows and debugging the issues that come up."
"Having seen what happened in self-driving, which was more or less that it's easy to get to 90%, very very hard to get to 99%... I think that something similar will happen as with large scale agent deployments, and that final 10% of accuracy... will require a lot of work."
The host pointed out that robotaxis still have remote assist for the edge cases. Wang went further:
"And the ratio, by the way, the companies don't publish them, but I think the ratio is something like five cars to one tele-operator, or maybe even less than maybe three cars per tele-operator. So the ratio is much lower than people think. I think humans are much more involved even in self-driving cars than I think most people appreciate."
"For an optimistic version of the future where unemployment is still low, you just have to believe that humans are almost insatiable in their desire and their demand, and that prices will go down... the economy will become more efficient and we'll just want more."
Interested in the latest with ai/tech and business?
Follow @GeniusGTX and never fall behind.
I track ChatGPT, Claude, and how every new agent is changing how we work and think, then hand you the pure signals.
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Of course. It’s dumb to read or write code by hand.
A friend at a leading inference company told me they're moving toward fully agent-driven development - agents write and **review** all the code.
I believe every company will follow within 2-3 years.
Eventually, there will be no more "engineers." Everyone will become a "researcher."
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The servicewoman at the gym was absolutely astonished that I showed up in a golden self-driving car with gullwing doors.
We are so early.
We are so early.
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What will soon become clear is that the Frontier Labs are no longer the optimal place to allocate capital. The best index portfolio for The Great Compression is instead as follows:
1) bets on pure "capabilities", super-intelligent general learning algorithms, a God Model, etc: Core Automation, Oaklab, Standard Intelligence, SSI, Flappy.
2) bets on compute/infrastructure: nvidia, spacex, etc.
3) bets on economic version control / pole position: Jai & Co (and a couple others in stealth), HoldCos, Bending Spoons, Palantir, Formation Bio, OpenEvidence etc.
The "labs" no longer give you the most convex upside in any of the salient areas. That's what the market is starting to see.
1) is mostly a hedge, if somehow there's a way for a true God Model to be born, I think it happens inside these research organizations focused exclusively on radically novel learning paradigms (not turning the economy into code).
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Introducing Octopus OS, a reinvented agent harness.
Agents can edit their own harness in the middle of a task, on their own. Unlike Harness² and similar projects, self-improvement isn’t a separate stage bolted onto the agent. The whole architecture is just the environment they , chasing self-improvement that feels truly native, I dropped the usual agent loop entirely and designed a new architecture that works a lot like a real operating system, with a 45k-line Rust kernel holding it all together. The system state lives in JSON files: goals act as real environments where agents work, along with inputs, outputs, and custom triggers they respond to. Agents can edit all of it while they work inside.
It was also built as a product from day one, not a research demo. The full desktop app took 3 months, built on top of the Codex source.
The client already works, and you can use it today like any other agent, Codex included.
And it’s open source.
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