Are they failing to understand, or are they manipulating their audience?

Written by an anthropic employee, I mean how seriously can you take that piece? pure hype

This is written with the idea that the exponential part keeps going forever.

It never does. The progress curve always looks sigmoidal.

- The beginning looks like a hockey stick, and people get excited. The assumption is that the growth party will never stop.

- You start to hit something that inherently limits the exponential growth and growth starts to be linear. It still kinda looks exponential and the people that want the party to keep growing will keep the hype up.

- Eventually you saturate something and the curve turns over. At this point it’s obvious to all but the most dedicated party-goers.

I don’t know where we are on the LLM curve, but I would guess we’re in the linear part. Which might keep going for a while. Or maybe it turns over this year. No one knows. But the party won’t go on forever; it never does.

I think Cal Newport’s piece [0] is far more realistic:

> But for now, I want to emphasize a broader point: I’m hoping 2026 will be the year we stop caring about what people believe AI might do, and instead start reacting to its real, present capabilities.

[0] Discussed here: https://news.ycombinator.com/item?id=46505735

> Given consistent trends of exponential performance improvements over many years and across many industries, it would be extremely surprising if these improvements suddenly stopped.

This is the part I find very strange. Let's table the problems with METR [1], just noting that benchmarking AI is extremely hard and METR's methodology is not gospel just because METR's "sole purpose is to study AI capabilities". (That is not a good way to evaluate research!)

Taking whatever idealized metric you want, at some point it has to level off. That's almost trivially true: everyone should agree that unrestricted exponential growth forever is impossible, if only for the eventual heat death of the universe. That makes the question when, and not if. When do external forces dominate whatever positive feedback loops were causing the original growth? In AI, those positive feedback loops include increased funding, increased research attention and human capital, increased focus on AI-friendly hardware, and many others, including perhaps some small element of AI itself assisting the research process that could become more relevant in the future.

These positive feedback loops have happened many times, and they often do experience quite sharp level-offs as some external factor kicks in. Commercial aircraft speeds experienced a very sharp increase until they leveled off. Many companies grow very rapidly at first and then level off. Pandemics grow exponentially at first before revealing their logistic behavior. Scientific progress often follows a similar trajectory: a promising field emerges, significant increased attention brings a bevy of discoveries, and as the low-hanging fruit is picked the cost of additional breakthroughs surges and whatever fundamental limitations the approach has reveal themselves.

It's not "extremely surprising" that COVID did not infect a trillion people, even though there are some extremely sharp exponentials you can find looking at the first spread in new areas. It isn't extremely surprising that I don't book flights at Mach 3, or that Moore's Law was not an ironclad law of the universe.

Does that mean the entire field will stop making any sort of progress? Of course not. But any analysis that fundamentally boils down to taking a (deeply flawed) graph and drawing a line through it and simplifying the whole field of AI research to "line go up" is not going to give you well-founded predictions for the future.

A much more fruitful line of analysis, in my view, is to focus on the actual conditions and build a reasonable model of AI progress that includes current data while building in estimations of sigmoidal behavior. Does training scaling continue forever? Probably not, given the problems with e.g., GPT-4.5 and the limited amount of quality non-synthetic training data. It's reasonable to expect synthetic training data to work better over time, and it's also reasonable to expect the next generation of hardware to also enable an additional couple orders of magnitude. Beyond that, especially if the money runs out, it seems like scaling will hit a pretty hard wall barring exceptional progress. Is inference hardware going to get better enough that drastically increased token outputs and parallelism won't matter? Probably not, but you can definitely forecast continued hardware improvements to some degree. What might a new architectural paradigm be for AI, and would that have significant improvements over current methodology? To what degree is existing AI deployment increasing the amount of useful data for AI training? What parts of the AI improvement cycle rely on real-world tasks that might fundamentally limit progress?

That's what the discussion should be, not reposting METR for the millionth time and saying "line go up" the way people do about Bitcoin.

[1] https://www.transformernews.ai/p/against-the-metr-graph-codi...

Who's he doing the interview with?

If you think there are as or more interesting podcasts out there, feel free to name them.

Same with that "MIT" interviewer who wasn't even at MIT.

And that girl Altoff ...

Literal nobodies suddenly interviewing Elon Musk, etc... within weeks.

Things rarely go "viral" on their own these days, everything is controlled, even who gets the stage, how the message is delivered, etc... as you have noticed.

With regards to who's behind, well, we might never know. However, as arcane as it might sound, gradient descent can take you close to the answer, or at least point you towards it.

I like this recent meme of Christof from Truman Show saying things like "now tell them that there's aliens" or crap like that.

He introduced me and many others to Sarah Paine. I think that helped launch him. Maybe that was already after he had an audience though.

The funny thing is his questions to her were terrible. But she rescued it anyway.

But I think he has improved markedly as an interviewer I will say

He knew people, caught a wave, and was roommates with Dylan Patel of SemiAnalysis. They networked, got to meet the right people, developed a web of contacts and sources, and the rest is history. Treat your friends well, and it often comes back multiplied.

The marketing effect was them catching the wave at the right time, and they're just surfing the hell out of it.

Isn't it just the usual feedback loop that happens with popular podcasters? They have connections and get a few highly popular guests on. As long as their demeanor is agreeable and they keep the conversation interesting other high profile guests will agree to be on and thus they've created a successful show.

The thing which distinguished him was getting good guests, before the hype hit. And he generally asks good questions and then shuts up while his guests talk.

His Sarah Paine episodes have way more listeners than his normal fare. I doubt that's the whole story, but that's surely part of it.

It seems that AI people have moved on from Lex Fridman to Dwarkesh. A couple of years ago the YouTube algorithm spammed Fridman in response to basically anything, now it is Dwarkesh. Maybe they need a new face periodically.

The IPO hype is in full swing.

He knew Bryan Caplan, and interviewing him early on kickstarted things, I believe.

> who Dwarkesh’s patron is that boosted him in podcast world

The Indian consumer market.

Unlike China, Indians use western social media platforms so Indian tastes and trends are becoming increasingly common on the internet.

This is also why you see entirely different trends on TikTok (banned in India, allowed elsewhere), Western Social Media (banned in China, allowed elsewhere), and Chinese social media (only used by Chinese and the diaspora).

What Ben Thompson predicted with his "Four Internets" theory 6 years ago has started playing out [0].

Over the next decade, more Indian media like Dwarkesh will leak into Western social media.

[0] - https://stratechery.com/2020/india-jio-and-the-four-internet...

Yeah I also don't understand how he is able to get such high profile guests. His interview with Jeff Dean and Noam Shazeer last year[1] is so hilariously bad. Jeff and Noam kept trying to give really insightful answers on how they see AI development shaping in coming years and he was just steering the conversation to shallow and silly tabloid gossip (why don't you "just" let AI improve the next version in a loop so we can quickly have singularity, Jeff Dean AI running in a DC, evil Jeff Dean AI escaping containment and on and on). It was just embarrassing. The interview would have been so much better with just Jeff and Noam without him.

[1] https://www.youtube.com/watch?v=v0gjI__RyCY

In my opinion, he asks the right questions and lets the guests speak, which is something that can't be said about the rest of tech podcasts.

For example, at some point I grew very tired of the superficiality of the questions that Lex Friedman asks his very technical guests. He seems to be more interested into taking the conversation into a philosophy freshman's essay about technology instead of talking about technology itself.

Hearing the Dwarkesh podcast was a breath of fresh air in that regard.

I guess you could look at his early guests and see what (if anything) many of them have in common

There was a small network of AI-intellectualism (and rationality) that grew highly relevant when AI took off post chatgpt. It feels adjacent to Tyler Cowen's network + tpot + hn/lesswrong. (I can't remember if Tyler specifically gave him a fast grant, but his first few interviews were GMU-centric.)

I personally liked that he stayed away from navel-gazing in politics when the blogosphere/podcasts went pretty heavy into that.

It did very well on twitter with a large number of high-follower-count tech people, and soon to be high-follower-count (basically AI employees). He had followed the zeitgeists general wisdom well (bat signal, work in public, you-can-just-do-things, move-to-the-arena, You-Are-the-Average-of-the-Five-People-You-Spend-the-Most-Time-With, high-horsepower). And he's just executed very well. Other people have interviewed similar people and generally gotten lower signal content. This moxie marlinspike interview is great though - https://www.youtube.com/watch?v=cPRi7mAGp7I .

I never knew about him until a few months ago when he started appearing in my YouTube recommendations, and naturally I thought the same thing because a 'nobody' like him (not in a derogatory sense) started doing interviews with the top AI bros. And the interviews are terribly boring because they feel like a cheap PR campaign. You could sit Lex Fridman instead of Dwarkesh Patel and it would feel exactly the same.

Similar wonderings occurred to me at that point in the vid where he struggled to understanding Amodei's explanation of the economics, which was pretty straightforward. Unless he was just being deliberately arsey.

Nobody in their little bubble.

I’m honestly trying to understand the state of the art and unfortunately the industry is so grifty it’s hard to tell…

Can I ask what happened with your Claude Code rollout?

> Nobody disagrees, there is zero disagreement, there is no war in Ba Sing Se.

This captures my chief irk over these sorts of "interviews" and AI boosterism quite nicely.

Assume they're being 100% honest that they genuinely believe nobody disagrees with their statement. That leaves one of two possible outcomes:

1) They have not ingested data from beyond their narrow echo chamber that could challenge their perceptions, revealing an irresponsible, nay, negligent amount of ignorance for people in positions of authority or power

OR

2) They do not see their opponents as people.

Like, that's it. They're either ignorant or they view their opposition as subhuman. There is no gray area here, and it's why I get riled up when they're allowed to speak unchallenged at length like this. Genuinely good ideas don't need this much defense, and genuinely useful technologies don't need to be forced down throats.

> My company tried this, then quickly stopped: $$$

How much were devs spending to become a sticking point?

I'm asking because I thought it'd be extremely expensive when it rolled out at the company I work for, we have dashboards tracking expenses averaged per dev in each org layer, the most expensive usage is about US$ 350/month/dev, the average hovers around US$ 30-50.

It's much cheaper than I expected.

Nobody out of people remotely worth listening to. There's always people deeply wrong about things but over 70 years at this point is a pretty insane position unless you have a great reason like expecting Taiwan to get bombed tomorrow and slow down progress.

> 100% of today’s SWE tasks are done by the models.

Meanwhile, Claude Code is implemented using a slop framework and has 6000 open issues, many of which are utterly trivial to fix.

Can someone explain to me what AGI means? What is the concrete technical definition? How do we know it is achieved?

> We pay humans upwards of $50 trillion in wages because they’re useful, even though in principle it would be much easier to integrate AIs into the economy than it is to hire humans

While I fully agree with your sentiment it’s striking Dario said “this century”. He likely won’t even be alive for about 50%, assuming he lives to 80, of his prediction window. It’s such a remarkably meaningless comment.

This is my experience, which is why I stopped altogether.

I think I'm better off developing a broad knowledge of design patterns and learning the codebases I work with in intricate, painstaking detail as opposed to trying to "go fast" with LLMs.

I have the issue that I run into some bug that it just cannot fix. Bear in mind I am developing an online game. And then I have to get into the weeds myself which feels such an gargantuan effort after having used the LLM, that I just want to close the IDE and go do something else. Yes, I have used Opus 4.6 and Codex 5.3 and they cannot just solve some issues no matter how I twist it. Might be the language and the fact that it is a game with custom engine and not a react app.

I talked with my coworker today and asked which model he uses, he said Opus 4.6 but he said he doesn't use any AI stuff much anymore since he felt it makes him not learn and build the mental model which I tend to agree a bit with.

It's the intermittent reward model, and the reward hits, but the reward might be hallucinated in the fuller sense.

If you're disturbed by this your comment gets flagged and removed by Anthropic astroturfers.

Uh...there's constant talk from people being disturbed by it. One of the Democratic candidates in 2020 had his platform based around this, and I can assure you that it's not gotten less attention since ChatGPT came out.

That’s the point.

AI marketing is dystopian. They describe a world where most people are suddenly starving and homeless, and just when you start to think “hey this sounds like the conditions to create something like a French Revolution but where Bastille is a data center” they pivot to BUY MY PRODUCT SO YOU DON'T GET LEFT BEHIND.

It’s advertising straight through the amygdala.

I have no idea if they actually believe this. But it’s repulsive behavior.

They probably have the ability to give executives higher quality/more expensive models in the backend.

Amodei isn't a grifter; the difference is that he really believes powerful AI is imminent.

If you truly believe powerful AI is imminent, then it makes perfect sense to be worried about alignment failures. If a powerless 5 year old human mewls they're going to kill someone, we don't go ballistic because we know they have many years to grow up. But if a powerless 5 year old alien says they're going to kill someone, and in one year they'll be a powerful demigod, then it's quite logical to be extremely concerned about the currently harmless thoughts, because soon they could be quite harmful.

I myself don't think powerful AI is 1-2 years away, but I do take Amodei and others as genuine, and I think what they're saying does make logical sense if you believe powerful AI is imminent.