I really doubt this actually. To me, Jev is a great example ofcounter positioning. When you consider just how hyper optimized the labs are around auto regressive LLMs, and just how much money they have already invested and are pre committed to investing in an entire stack for auto regressive transformers... then responding to Jev becomes nearly impossible actually. They would just be giving up too much.

Just think, everything from their current sources of revenue, the sales use cases they tout, the marketing on the websites, the messaging to customers, then technically to the APIs, their internal batching and scheduling algos, their GPU configs, the chips themselves. ALL OF IT is designed with generative text models in mind. Jev breaks all of it.

I think basically no chance of a response any time soon.

Comments URL: https://news.ycombinator.com/item?id=49692669

Points: 1

# Comments: 1

* Setting a strong, non-consensus vision. -> AI's are trained to produce the median / mode answer. So this almost disqualifies them by default.

* Having courage to do the correct, obvious thing even when your team or investors don't think it's the right call. (This might be firing someone everyone likes but who's role is no longer needed, it might be changing strategies away from the product everyone just spent 6 months on, etc. etc. the list goes on). -> Ben Horowitz has called this kind of decision basically the entire value of a CEO. This is also EXACTLY where today's LLMs keep getting worse and worse. They are utterly spineless.

* Having unique skills and credibility to build a team -> This can come from many sources (track record, vision, charisma, specific achievements, network, etc.) , but people need to want to join YOUR team, not someone else's. They have to believe that YOU are going to win against the market. There is no way you're going to have an edge by using a commercially available AI API with an open source harness at the helm. That is just so utterly uncompelling to top candidates.

* Fundraising -> On the surface, very ammenable to AI, but again, as an investor, would you seriously invest in a CEO that is literally replicatable by any other company?

The value I can see for something like this is a human to get a "2nd pair of eyes". But humans have tons of advisors already and generally speaking, getting takes on what you should do as CEO is not the hard part. It's seeing through the fog of war, finding clarity, and then implementing that clarity across the org, which is surprisingly hard.

* Your amount of "lost" time with kids is often less than you think. On one side, you very likely socialized more, traveled more, were less disciplined about what you worked on, etc. On another side, some things that you are going to do anyway (dishes, maybe calling your parents, groceries), you can often do with kids, and so this time isn't really "lost"

* If you are fortunate enough to have some means, you might consider not outsourcing parenting, but outsourcing "life admin" work. Specifically things like laundry, dishes, buying groceries, picking up around the house, etc. I recently got someone to come to our house 3x / week for 3 hours and it's a game changer for me and my wife (who also works). (I know my point above says you can do some of this with kids, and that's kind of true, but not always, and it still lets you do other things with your time)

* Assuming you have a partner, and the premise here is you still want to be a very active parent, then I recommend being very deliberate about almost never parenting together. As in, one of you take all the kids, and the other can do what they want for 1-4 hours at a time.

Lastly, while I'm a pretty active dad myself, I reject the premise put forth here that your job is to maximize the time spent with your kids, as if this is the main metric by which parenting success should be judged. Just no. Actively no. Kids having time alone and with their own friends outside of the eye or ear of their parents is a really important part of growing up. Kids having time with various caregivers who are not their parents (be that grandparents, aunts, uncles, friend's parents, camp counselors, babysitters) is also good and healthy.

The struggle is real though. There's no easy answer. Hope these thoughts help!

It's not simple weights and numbers all the way down. The available output is pre-set by the tokens we allow it to predict.

There was a whole bit in there about not having a language module or using words. But it does. We tell it.

Humans do not come pre programmed with a set of possible "tokens". We just figure it out and I believe that fact captures something very essential. Maybe the missing piece of AGI. The fact that humans can just be awash in pure sense data, and somehow just figure out what is important and what to do. Never ceases to amaze me.

Physical goods like clothes or cars have variable costs. The marginal unit always costs > 0, and thus the price to the consumer is always greater than zero. Industrialization lowered this variable cost, while simultaneously increasing production capacity, and thus enabled a new segment of "low cost, high volume" products, but it does not eliminate the variable cost. This variable cost (eg. the cost of a hand made suit) is the "umbrella" under which a low cost variant (factory made clothes) has space to enter the market.

Digital goods have zero marginal cost. Many digital goods do not cost anything at all to the consumer! Or they are as cheap as possible to actively maximize users because their costs are effectively fixed. What is the "low value / low cost" version of Google? or Netflix for that matter? This is non-sensical because there's no space for a low cost entrant to play in when the price is already free.

In digital goods, consumers tend to choose on quality because price is just not that relevant of a dimension. You see this in the market structure of digital goods. They tend to be winner (or few) take all because the best good can serve everyone. That is a direct result of zero marginal cost.

Even if you accept the premise that AI will make software "industrialized" and thus cheaper to produce, it doesn't change the fact that most software is already free or dirt cheap.

The version of this that might make sense is software that is too expensive to make at all because the market size (eg. number of consumers * price they would pay) is less than the cost of the software developer / entrpreneurs time. But by definition those are small markets, and not anything like the huge markets that were enabled by physical good industrialization.

One thought... in the video, Ken makes the observation that it takes way more complexity and steps to find a given shape with SGD vs. open-endedness. Which is certainly fascinating. However...

Intuitively, this feels like a similar dynamic is at play with the "birthday paradox". That's where if you take a room of just 23 people, there is a greater than 50% chance that two of them have the same birthday. This is very surprising to most people. It seems like you should need way more people (365 in fact!). The paradox is resolved when you realize that your intuition is asking how many people it takes to have your birthday. But the situation with a room of 23 people is implicitly asking for just one connection among any two people. Thus you don't have 23 chances, you have 23 ^ 2 = 529 chances.

I think the same thing is at work here. With the open-ended approach, humans can find any pattern at any generation. With the SGD approach, you can only look for one pattern. So it's just not an apples to apples comparison and sort of misleading / unfair to say that open-endedness is way more "efficient", because you aren't asking it to do the same task.

Said another way, I think with the open-endedness, it seems like you are looking for thousands (or even millions) of shapes simultaneously. With SGD, you're kinda flipping that around, and looking for exactly 1 shape, but giving it thousands of generations to achieve it.

They claim OOM faster learning, and robustness acro domains. There's enough detail to probably do your own PuTorch implementation, though they haven't released code. The paper has been accepted into AMLDS2025. So peer reviewed.

At first blush, this sounds really exciting and if results hold up and are replicated, it could be huge.

This approach strikes a nice balance. It gives you enough time to be able to really do something and change directions if you want. But not so much time that you can really waste any. It forces you to ask the hard questions about whether your day to day is truly connecting with your dreams, and whether you're on a path to get there.

Of course, Seneca didn't have life expectancy tables to work with. But I think he would have approved. :)

I've come to find this "live each day like it's your last" advice to be pretty unhelpful. My favorite quote about it is, "all that goes to show you is some people would spend their last day giving you stupid advice".

The problem is that if it actually was your last day, most people would give the finger to all of their responsibilities and go party, eat cake, see friends, familiy, lovers, etc. Which is simply not an actual way to live your life. It's a way to exit your life.

An alternative framing that I've come to find more helpful is to take your life expectancy, and cut it by 2/3. Now what do you do? For example, if you're 20 years old and your life expectancy is 80 (ie. 60 more years), pretend that you only have 20 more, so you'll only live until you're 40. It's nice cause it naturally adjusts as you get older. You'll have smaller windows to work with.

This approach strikes a nice balance. It gives you enough time to be able to really do something and change directions if you want. But not so much time that you can really waste any. It forces you to ask the hard questions about whether your day to day is truly connecting with your dreams, and whether you're on a path to get there.

Of course, Seneca didn't have life expectancy tables to work with. But I think he would have approved. :)

It's not about having crazy life altering, world-bending experiences (though that can happen). It's just about helping you get into a state of mind that allows for an effective therapy session. Sort of like... would you want to do your therapy session in a crowded bar, next to your mom? No, probably not. We all recognize that such a setting would not be conducive to good therapy. So similarly, we should be able to recognize that having the right setting, both mentally and physically can affect the quality of your session. Psychadelics can do exactly this.

It's also worth noting my friend has done "regular" therapy for 2 years, and she felt like there was a step change after the guided session. Her therapist noticed it as well.

When you consider that pain meds have ruined literally millions of lives through addiction, and that also virtually (maybe literally?) no one has ever died due to overdose of psilocybin, it's very confusing why one is prescribed all the time, and the other is considered incredibly dangerous. The U.S.'s perspective on drugs is so very backwards.