I understand your point about misattribution but it cuts both ways. How about when a company is better than competitors because they executed better because they had a superior organizational culture. Or not successful and this is due to poor culture.
YC sets the prime examples. It is never product at the expense of who the team is and in what proven way they have worked together and plan to execute at scale.
Why would you assume culture is immaterial? And to make this less emotional let’s take the micro scale; don’t you think the culture of doing engineering doesn’t affect outcomes team to team within the same company, or company to company within the same country or even country to country within the same company?
I don't know why you'd think your analogy wasn't clear in the first place. But your analogy can't support you on the assertion that optimizations will be sophisticated and reliable enough to completely forget about the programming language underneath.
If you have any first principles thinking on why this is more likely than not, I am all ears. My epistemic bet is that it is not going to happen, or somehow if we end up there the language we will have to use to instruct them is not going to be different than any other high level programming language that the point will be moot.
First problem is turning engineers into accountability sinks. This was a problem before LLMs too, but now a much bigger and structural problem with democratization of the capacity to produce plausible looking dumb code. You will be forced to underwrite more and more of that, and expected to absorb the downsides.
The root cause is the second problem; short of formal verification you can never exhaustively prove that your code works. You can demonstrate and automate that demonstration for a sensible subset of inputs and states and hope for the state of the world approximately staying that way (spoiler: it won't). This is why 100% test coverage in most cases is something bad. This is why sensible is the key operative attitude, which LLM suck at right now.
The root cause of that one is the third problem; your job is to solve a business problem. If your code is not helping the business problem, it actually is not working in the literal sense of the work. It is an artifact that does a thing, but it is not doing work. And since you're downstream of all the self-contradicting, ever changing requirements in a biased framing of a chaotic world, you can never prove or demonstrate that your code solves a business problem and that is the end state.
At no point compilers produced stochastic output. The intent user expressed was translated down with a much much higher fidelity, repeatability and explainability. Most important of all, it completely removed the need for the developer to meddle with that output. If anything it became a verification tool for the developer‘s own input.
If LLMs are that good, I dare you skip the programming language and have it code in machine directly next time. And it is exactly how it is going to feel like if we treat them as valuable as compilers.
> What got me past that point was short bursts at BPMs way past my comfort zone and building synchrony _after_ I stumbled upon more efficient motions that scaled.
This is actually pretty close to what Stetina says. I just probably didn’t do a good job expressing it.
You’re oscillating above and below the comfort zone and that iteration like you say affords insights from both sides, and eventually the threshold grows.
Funny how this exactly applies to instrument playing. Unearned speed only begets sloppiness. The only way to go past a certain velocity is to do meticulous metronome work from a perfectly manageable pace and build up with intention and synchrony. And even then it is not a linear increase, you will need to slow back down to integrate every now and then. (Stetina's "Speed Mechanics for Lead Guitar"; 8 bpm up, 4 bpm down)
Language is not humanness either; it is a disembodied artifact of our extended cognition, it is a way of transferring the contents of our consciousness to others or to ourselves over time. This is precisely what LLMs piggyback on and therefore are exceedingly good at simulating, which is why the accuracy of "is this human" tools are stuck at %60-70's (%50 is a coin flip), and are going to be bounded for a foreseeable future.
And I am sorry to be negative but there is so much bad cognitive science in this article that I couldn't take the product seriously.
> LLMs can be scaled almost arbitrarily in ways biological brains cannot: more parameters, more training compute, more depth.
- Capacity of raw compute is irrelevant without mentioning the complexity of computation task at hand. LLM's can scale - not infinitely - but they solve for O(n^2) tasks. It is also amiss to think human compute = a singular human's head. Language itself is both a tool and protocol of distributed compute among humans. You borrow a lot of your symbolic preprocessing from culture! Like said, this is exactly what LLM's piggyback on.
> We are constantly hit with a large, continuous stream of sensory input, but we cannot process or store more than a very small part of it.
- This is called relevance, and we are so frigging good at it! The fact that machine has to deal with a lot more unprioritized data in a relatively flat O(n^2) problem formulation is a shortcoming, not a feature. Visual cortex is such an opinionated accelerator of processing all that massive data that only the relevant bits need to make to your consciousness. And this architecture was trained for hundreds of millions of years, over trillions of experiment arms - that were in parallel experimenting on everything else too.
> Humans often have to act quickly. Deliberation is slow, so many decisions rely on fast, heuristic processing. In many situations (danger, social interaction, physical movement), waiting for more evidence simply isn't an option.
- Again a lot of this equivocates conscious processing to entire cognition. Anyone who plays sports or music knows to respect the implicit, embodied cognition that goes on to achieve complex motor tasks. We are yet to see a non-massively-fast-forwarded household robot do a mundane kitchen cleaning task, and go play table tennis with the same motor "cortex". Motor planning and articulation is a fantastically complex computation; just because it doesn't make it to our consciousness or instrumented exclusively through language doesn't mean it is not.
> Human thinking works in a slow, step-by-step way. We pay attention to only a few things at a time, and our memory is limited.
- Thinking, Fast and Slow by Kahneman is a fantastic way of getting into how much more complex the mechanism is.
The key point here is as limited in their recall, how good humans are at relevance, because it matters, because it is existential. Therefore when you are using a tool to extend your recall, it is important to see its limitations. Google search having indexed billions of pages is not a feature if it can't bring the top results well. If it gets the capability to sell me whatever it brought up was relevant, that still doesn't mean the results are actually relevant. And this is exactly the degradation of relevance we are seeing in our culture.
I don't care if the language terminal is a human or a machine, if the human was convinced by the low relevance crap of the machine it just a legitimacy laundering scheme. Therefore this is not a tech problem, it is a problem of culture; we need to be simultaneously cultivating epistemic humility, including quitting the Cartesian tyranny of worshipping explicit verbal cognition that is assumed to be locked up in a brain; we have to accept that we are also embodied and social beings that depend on a lot of distributed compute to solve for agency.
We know from the era of data the power of JOIN. Bring in two different data sources about a thing and you could produce an insight neither of them could have provided alone.
LLMs can be thought as one big stochastic JOIN. The new insight capabilities - thanks to their massive recall - is there. The problem is the stochasticity. They can retrieve stuff from the depths and slap them together but in these use cases we have no clue how relevant their inner ranking results or intermediary representations were. Even with the best read of user intent they can only simulate relevance, not really compute it in a grounded and groundable way.
So I take such automatic insight generation tasks with a massive grain of salt. Their simulation is amusing and feels relevant but so does a fortune teller doing a mostly cold read with some facts sprinkled in.
> → I solve problems faster by finding similar past situations → I make better decisions by accessing forgotten context → I see patterns that were invisible when scattered across time
All of which makes me skeptical of this claim. I have no doubt they feel productive but it might just as well be a part of that simulation, with all the biases, blind spots etc originating from the machine. Which could be worse than not having used the tool. Not having augmented recall is OK, forgetting things are OK - because memory is not a passive reservoir of data but an active reranker of relevance.
LLMs can’t be the final source of insight and wisdom, they are at best sophists, or as Terrence Tao put it more kindly, a mere source of cleverness. In this, they can just as well augment our self-deception capacity, maybe even more than counterbalancing them.
Exercise: whatever amusing insight a machine produces for you, ask for a very strong counter to it. You might be equally amused.
> I'm always surprised how many 'logical' tech people shy away from simple determinism, given how obvious a deterministic universe becomes the more time you spend in computer science, and seem to insist there's some sort of metaphysical influence out there somewhere we'll never understand. There's not.
You might be conflating determinism with causality. Determinism is a metaphysical stance too because it asserts absence of free will.
Regardless of the philosophical nuance between the two, you are implicitly taking the vantage point of "god" or Laplace's Demon: infinite knowledge AND infinite computability based on that knowledge.
Tech people ought to know that we can't compute our way out of combinatorial explosion. That we can't even solve for a simple 8x8 game called chess algorithmically. We are bound with framing choices and therefore our models will never be a lossless, unbiased compression of reality. Asserting otherwise is a metaphysical stance, implicitly claiming human agency can sum up to a "godlike", totalizing compute.
In sum, models will never be sophisticated enough, claiming otherwise has always ended up being a form of totalitarianism, willful assertion one's favorite "framing", which inflicted a lot of pain in the past. What we need is computational humility. One good thing about tech interviews that it teaches people resource complexity of computation.
My bad, skipped a chain of thought there. Since medicare pays less than private insurance, hospitals can and do shift costs (which in reality is "opportunity cost of profit") to the latter, which pushes to private premiums up. Regardless, this is a minor effect. Very little of the inflation is justified with productivity gains, as you said it is a very inefficient healthcare system. US prices clock 2x-4x of comparable OPEC peers, admin percent is higher etc.
Employers paid for healthcare in 1970s too, and even for higher percentages of the workforce. If there is a premium inflation surpassed the CPI, that is still inflation, not real growth. If there’s an inflation problem in delivering a temporally comparable service, that is not a “real wage” item for the employee [1]. So what the nominal figure today shouldn’t be relevant.
I agree it shouldn’t be an employer item too, but whatever employers lose on premiums, they get more on an overall stickier and cheaper labor supply.
[1] one could argue the productivity of healthcare increased, and the data indeed supports this with the overall life expectancy increase from 70s to now mid 70s plus quality of life treatments. But again most of the spend is actually on the tail end at this age group, which raises the workers’ premium without delivering the benefit. Therefore not much structural gain for the actual working age employee.
> what are fair ways to extract value from citizens for the shared value of the state?
The right question is who benefits the most from state’s services. For example if a whole lot of security, legislative or admin services go to protecting the capital, then those who has the most capital need to chip in the most.
> redistribution is usually that “more” people reach a higher standard of living, then adding taxes and friction to processes like automation may conflict with that goal
This is basically a 50 year old trickle down argument. But real wages have not increased in comparison to gdp since 70s, so nothing trickled down. We are demonstratedly bad at sharing what we have achieved together, no reason to believe more tech will magically get better treatment than that.
Besides redistribution is not about shifting the curve up, but making it flatter - see gini coefficient.
> the core benefit of automation, which is to delete non-needed work, make things cheaper, and make the value creator richer.
Except the era of classical capitalism and inventor’s profit is over, since 70s it is rentiers unreciprocated extraction on top of purported value people didn’t necessarily ask for or need in the first place. Likewise most people aren’t dying for AI automation, and not even for structural threats; it is not even proven that it will provide a net total productivity gain when the hype cools down, despite being shoved down people’s throats.
Let’s not kid ourselves, there is little concern for real value creation but a capture-the-flag on a gigantic data-moated compute monopoly. Whatever democratic means enabled proper taxation would have already prevented this type of speculative berserk, failures of which I assure you will be socialized.
So friction = societal consent, internalizing externalized costs, revealing what is actually value versus monopolist’s rent. It is healthy for the society, it is healthy for capitalism.
It is a black box. We don’t know what happens on the other side of the RPC call; good and bad, therefore it could be any number of knobs.
User has two knobs called the thinking level and the model. So we know there are definitely per call knobs. Who can tell if thinking-high actually has a server side fork into eg thinking-high-sports-mode versus thinking-high-eco-mode for example. Or if there were two slightly different instantiations of pro models, one with cheaper inference due to whatever hyperparameter versus full on expensive inference. There are infinite ways to implement this. Zero ways to be proven by the end user.
Unfortunate that he starts with the thinking argument because it will be nitpicked to death, while bullshit and computing freedom arguments are much stronger and to me personally irrefutably true.
For those who will take “bullshit” as an argument of taste I strongly suggest taking a look at the referenced work and ultimately Frankfurt’s, to see that this is actually a pretty technical one. It is not merely the systems’ own disregard to truth but also its making the user care about the truthiness less, in the name of rhetoric and information ergonomics. It is akin to the sophists, except in this case chatbots couldn’t be non-sophists even they “wanted” to because they can only mimic relevance, and the political goal they seem to “care” about is merely making other use them more - for the time being.
Computing freedom argument likewise feels deceptively about taste but I believe harsh material consequences are yet to be experienced widely. For example I was experiencing a regression I can swear to be deliberate on gemini-3 coding capabilities after an initial launch boost, but I realized if someone went “citation needed” there is absolutely no way for me to prove this. It is not even a matter of having versioning information or output non-determinism, it could even degrade its own performance deterministically based on input - benchmark tests vs a tech reporter’s account vs its own slop from a week past from a nobody-like-me’s account - there is absolutely no way for me to know it nor make it known. It is a right I waived away the moment I clicked “AI can be wrong” TOS. Regardless of how much money I invest I can’t even buy a guarantee on the degree of average aggregate wrongness it will keep performing at, or even knowledge thereof, while being fully accountable for the consequences. Regression to depending on closed-everything mainframes is not a computing model I want to be in yet cannot seem to escape due to competitive or organizational pressures.
> An objective and grounded ethical framework that applies to all agents should be a top priority.
I mean leaving aside the problem of computability, representability, comparability of values, or the fact that agency exists in opposition (virus vs human, gazelle vs lion) and even a higher order framework to resolve those oppositions is a form of another agency in itself with its own implicit privileged vantage point, why does it sound to me that focusing on agency in itself is just another way of pushing protestant work ethic? What happens to non-teleological, non-productive existence for example?
The critique of anthropocentrism often risks smuggling in misanthropy whether intended or not; humans will still exist, their claims will count, and they cannot be reduced to mere agency - unless you are their line manager. Anyone who wants to shave that down has to present stronger arguments than centricity. In addition to proving that they can be anything other than anthropocentric - even if done through machines as their extensions - any person who claims to have access to the seat of objectivity sounds like a medieval templar shouting "deus vult" on their favorite proposition.
If the motivation structure is there I don’t see an inherent reason for people to refuse cultivating themselves. Going with the gym analogy lay people did not need gyms when physical work was the norm, cultivation was readily accomplished.
If anything there is a competing motivational structure in which people are incentivized not to think but to consume, react, emote etc. Information processing skills of the individual being deliberately eroded/hijacked/bypassed is not a AI thing. The most obvious example is ads. Thinkers are simply not good for business.
> AI is not the problem, laziness and negligence is.
As much as I agree with you that this is wrong, there is a danger in putting the onus just on the human. Whether due to competition or top down expectations, humans are and will be pressured to use AI tools alongside their work and produce more. Whereas the original idea was for AI to assist the human, as the expected velocity and consumption pressure increases humans are more and more turning into a mere accountability laundering scheme for machine output. When we blame just the human, we are doing exactly what this scheme wants us to do.
Therefore we must also criticize all the systemic factors that puts pressure on reversal of AI‘s assistance into AI’s domination of human activity.
So AI (not as a technology but as a product when shoved down the throats) is the problem.
It might take 50 years to awaken to the abuse of power going on here.
Forget individual videos for a second and look at youtube-the-experience as a whole. The recommendation stream is the single most important "generative AI" going on ever, using the sense of authenticity, curiosity and salience that comes from the individual videos themselves, but stitching them together in a very particular way. All the while the experience of being recommended videos being almost completely invisible. Of course this is psychologically "satisfying" to the users - in the shortest term - because they keep coming back, to the point of addiction. (Especially as features like shorts creep in).
Allowing the well of "interesting, warm, authentic audio & videos having the secondary gains of working on your psychological needs" being tainted with the question of generated content is a game changer because it breaks the wall of authenticity for the entire app. It brings the whole youtube-the-experience into question, it reduces its psychological stand-in function for human voice & likeness, band-aiding the hyper-individualized lonely person's suffering based content consumption habits. I know this is a bit dramatic, and for sure videos can be genuinely informative, but let's be honest, neither that is the entirety of your stream, nor that is the experience for the vast majority of the users. It will get worse as long as there is a mathematical headroom of making more money out of making it worse, that's what the shareholder duty is about.
When gen-AI came about I was naively happy about the fake "authenticity" wall of the recommended streams breaking down thanks to the garbage of generated sophistry overtaking and grossing out the users. Kind of like super delicious looking cakes turning out to be made of kitchen sponges turning people off of cakes all together. I was wrong to think AI oligopoly would let the opportunity of having a chokehold on the entire "content" business, and here we are. (Also this voluntary tagging will give them the perfect live training set, on top of what they have.)
Once the tech is good enough to generate video streams on the fly, so that all you need is a single livestream, that you won't even have a recommendation engine of videos and instead a team of virtual personas doing everything you could ever desire on screen, it is game over. It might already be game over.
To get out of this the single most important legislative maneuver is being able to accept and enforce the facts that a) recommendation is speech b) recommendation is also gen-AI, and should be subject to same level of regulatory scrutiny. I don't care if it generates pixels or characters at a time, or slaps together the most "interesting" subset of videos/posts/users/reels/shorts out of the vast sea of the collective content-consciousness, they are just one level of abstraction apart but functionally one and the same: look at me; look at my ads; come back to me; keep looking at me.
YC sets the prime examples. It is never product at the expense of who the team is and in what proven way they have worked together and plan to execute at scale.