abernard1

Fair. Too strong a statement.

But much like Ben's point that commoditization is a relatively novel concept to many in tech, it's not the consumer AI applications at risk of commoditization. They have distribution there.

It's the literally millions of engineers who are updating codebases with tools replacing workers partially or wholly. It's the supply-side where there's compression, and no need for distribution.

I would argue, given the enormity of the existing SaaS stack and how it integrates with the human machinery of personnel, that's where volume is. And that is clearly cheaper and a home run.

Commoditizing a ~$100B AI consumer market is no small feat. Commoditizing 20% of the $500B SaaS market, to say nothing of the underlying systems in the who-knows-how-many trillions "Big Tech" market (you're obligated to say that like the Kool Aid man), is shocking.

" - The highest tier Chinese models are not more economical than US frontier models. Try GLM 5.2 and see how much it costs to do real work. I did, and it was more expensive than GPT 5.6."

This is a flatly false statement for most things powering backend applications. The AI consumer "doing real work" model, either for analysis, chat, or coding could well be more cost effective with closed frontier models.

But most of these internal glue business SaaS applications where engineers are integrating are not those tasks. It is those tasks which 1) drive immense amount of domain-specific data into the platform over time, and 2) are most encouraging of driving open model independence with no vendor lock-in.

Anyone on this site who has actually used ML models (more accurate in many cases) knows there's a lot of kludge that simply does not need a 5 minute agentic feedback loop to solve the problem. And they were solvable a year ago with lower class models. The token economics are exceptional and the anecdotes of a16z saying 80% of startups are productionizing open models is only surprising to people who think running your company on OracleDB in 2026 is a sound engineering decision.

With what accuracy? And with how many intermediate tokens?

We can replace multiplication with any class of problems which should go from 0->100% solution almost immediately if there was actually a concept learned.

There is not. Because they are plain ol' fits. And there are no "emergent" features that pop out without having a sufficient set, where "sufficient" is absolutely gigantic and equivalent to memorizing enough of the space to compress the problem. LLMs are Rain Man.

They interpolate within a known distribution. Search allows places outside of distribution to be explored.

This paper should be required reading [1]. You can explore the curves yourself. You can see exactly what it's doing. And you also have this nagging thing—which you know and I know—that all these models converge and do not diverge upwards. An "emergent" "hyperintelligence"—a characteristic that could be found if something was actually learned and combined with a new concept—would not have this problem.

Exponentials on exponentials added to compute and data and the problem classes still sit at not great places, and require agents, feedback loops, and trial and error to solve. The models are the problem, but more importantly, the people selling things these models could never do are the problem.

[1] https://hai.stanford.edu/news/ais-ostensible-emergent-abilit...

Edit: It should be mentioned, if there's some scary neural architecture that's super-de-duper and doing something beyond the very obvious next string prediction that LLMs clearly do, it can't do what absolutely ancient ML models could do; a network to multiply two floating point numbers should pop out somewhere without symbolic computation, no?

It does not. There's no magic other than the run-of-the-mill SV fake it til you make it magic. And that magic has failed.

I am not confused. Because herein these forums, I predicted everything that was going to happen years ago.

And the "insane inefficiency" of deep learning is fully to be expected from how it works. As well, there are provably no—literally no—emergent properties in these models. The choice of metric was a convenient, sloppy, and embarrassing fault of the field. It should be discredited; the field should be embarrassed; expectations on messaging should have changed; and it did not.

Why? Because the industry is full of charlatans, and this is a highly profitable enterprise telling people that this would lead to AGI.

Multiply two floating point numbers without a tool call. Still can't, because it's a curve fit.

So, in summary, nothing you just said is relevant. There are no emergent capabilities, simply 1) search, + 2) the original set of learned feature vectors from throwing tons of data at this.

When I see Chinese (open source) emerging AI dominance...

what I see is a chronically corrupt set of Bay Area companies who stole open source software, pretended there was an AI god around the corner, and wanted to be regulated so the federal government could backstop their failed gambit.

In other words, Tuesday in San Francisco.

I think this is actually a perfect case of an impressive thing being done exactly by mimicry.

Why do LLMs still have trouble on floating point math without forking out to a tool, but they can perform symbolic manipulation just fine?

Because symbolic manipulation is just rote work and textual stepping through symbols. A side poster commented on the number of prior attempts on this problem which were close, but not quite.

Starting from a known "close" solution (which this did), and using exploration to search around the space is exactly something an LLM would and could be good at (clearly).

The "transformer LLMs are next-token predictors with some in-GPU processing of bounded complexity with respect to token count" remains undefeated. Both because that is mathematically what they are, and also because we don't have counterexamples to that effect that don't require some higher-order tooling wrapping the systems.

Symbol manipulation is what LLMs are good at.

Wouldn't it be wild if there were real-world penalties for being wrong?

And people who follow bad advice will get bad results, and people who follow good advice will get good results?

I wonder if this will impact the quality and prevalence of certain AI models in the future.

Postmodernism is a hell of a drug.

What discipline, outside academia, could position "nothing is neutral" as insightful and helpful commentary?

So is doing nothing, consuming resources and goods created by others, and living in the default state of humanity: brutality, poverty, and early death.

If it sounds if I'm being glib in this statement, I am. This article is an unreasonable amount of words which eventually boil down to "we have choices in what we do": an exactly equivalent statement to "X is just a tool, it depends on how we use it." Which, of course, is the thesis he intended to rail against.

Babies sleep better when their head is near their mother's heart. This seems to be the obvious reason for the left-handed cradling bias [1].

If a baby sleeps better, it cries less. If it cries less, it attracts fewer predators and helps both parents sleep better at night and have more energy. It also allows the mother to do things with her dominant hand if she is right-handed.

Given the left-handed cradling bias exists even with left-handers, it means there is something specific with left-handedness and infant rearing. A baby in the left hand and a tool (or weapon) in the right is biologically efficient.

Most studies take this from the perspective of evolutionary advantage of the individual. They should take it from the perspective of evolutionary advantage of the family, without which the baby does not survive.

If the bias confers evolutionary advantages, that is also important for the longer childhood humans have compared to primates, which supports our larger brains. Any differential here would have a feedback effect.

Wouldn't it be interesting if a key reason humans are the way we are is a mother's love ♥?

[1] https://sites.psu.edu/clarep/2024/04/12/the-left-cradling-bi...

There is also a bias for how babies are held [1]. It holds even with left-handers. Holding a baby's head near the mother's heart helps the baby get to sleep. Which means the baby doesn't cry (and attract predators) and also gives the parents more time to sleep at night.

It also allows right-handed mothers to do something with their dominant hand while cradling the baby in that position.

[1] https://sites.psu.edu/clarep/2024/04/12/the-left-cradling-bi...

THe government shouldn't be raising anyone's children, that's what parents are for.

The government does raise children. It's called the public school system.

One of the things I love about this is while Alex Jones was definitely negligent in his case, this pretty much does exactly what he wanted.

One of the things I've discovered in my long career of people being wrong about everything is how strong the team sports dynamic of social politics really is. I was high school friends with a writer for the Daily Show and the thing I realized is how humor and dismissal was a way of creating social superiority and evasion of legitimate arguments.

Right now, the world is changing greatly. Lots of people are retreating into a shell of humor in order to avoid it. Mass cognitive dissonance about the nature of reality. But reality and life goes on.

I'm definitely not aware that the credibility of the US DOJ has been destroyed.

And I question why a 501c3 charity would need "field informants" and to launder money through shell corporations. Especially to leaders of these organizations who were (1) coordinating some of these rallies and (2) due to the materially dishonest treatment of the "fine people hoax" for years.

Is the SPLC an intelligence organization? Am I missing something?

My sole comment is that people who use verbiage like this are mentally ill. Not "mentally ill" like I'm calling them an epithet. But like, actually mentally ill.

There are things that are simply not pedagogically useful in the limited instructional period in school. There are things that are simply not appropriate during early childhood development.

People who abuse and manipulate language like this are exactly why more traditional instruction is desired in certain school districts. Postmodernism is wrong. There are actually things that are true without the miasma of an artificial (and exhausting) social construction of reality.

Amazing that choice of curricula for elementary schoolchildren draws such a reaction.

Kids can read whatever they and their parents want. Schools don't have to teach it.

All of which is confirming my point. The Iranians are a beautiful people and a beautiful culture that is still run by maniacs.

The original person's implication that the "antagonism" towards the IRGC is novel to America is simply false.

"antagonistic"

They are a theocratic regime which is not supported by 80% of its population. Being gay is punishable by death. They employ surveillance from China to ensure hijabs are worn by women at all times. They ban access to the internet. Chants of "Death to America" are their government's routine greeting for 50 years. They place military equipment in schools and hospitals deliberately, viewing US compassion as a weakness. They recruit child soldiers and have them publicly stationed at military targets.

There is definitely "antagonism," but to act as if the Iranian people would not bomb their own government if they could... it's a bit much.

Most likely motivational media for their new child recruits: https://www.amnesty.org/en/latest/news/2026/04/iran-recruitm...

They of course are "not better than in the USA." But one can hold that weight long past you're drowned in the ocean.

Whatever the epithets, the truth of the matter is those urban areas are closer to what Canada aspires to be (and currently is). Whereas the parts of Canada she cares about are alive and well in the US (and used to be more like what Canada was).

The question becomes: if you're traveling on a line, and you see the destination looks dark ahead of you, do you turn around or keep going?

Canada's notoriously polite deference led them to align with those powerful tech, marketing, and financial hubs in the US. A cheerleader on the sidelines. But everyone gets to pick. There's a lack of acknowledgement that there's even a choice; the dog that didn't bark one could say. But it's part and parcel of why modern Canada is the way it is.

When I think about the counterfactual me that grew up in a large American city, New York or L.A. instead of Toronto,

And just think, those are the American areas most common to Canada.

There are places in America where those counterfactuals do not exist, where the necessities aren't locked behind counters, where community is thriving, and where the normality of civic life is an expectation.

I expect no honors for those parts of the country. If Canada didn't have an air of superiority to comfort itself with, it would have nothing at all.

This ICE stuff is that scaled up to a multi-billion dollar federal agency with, apparently, no accountability for following the law at all.

It should be mentioned that "illegal" is a definitive word. There are definitely people not willing to follow the law, including political entities which are dependent on it. The moniker of privacy in this respect is a shield for illegality, because there is no reason that Medicaid data regarding SSNs should be shielded from the federal government.

To take this to its logical conclusion, Americans must concede that EU/UK systems of identity and social services are inherently immoral.

By all means use Segment. Segment was a great technology with an incredible technical vision for what they wanted to do. I was in conversations in that office on Market far beyond what they ended up doing post-acquisition.

But a company that can't stand on its own isn't a success in my opinion. Similar things can be said about companies that continue to need round after round of funding without an IPO.

My comment is of the "(2018)" variety. Old news that didn't age well like the people jumping on the "Uber: why we switched to MySQL from Postgres" post. (How many people would choose that decision today?)

People tend to divorce the actual results of a lot of these companies from the gripes of the developers of the tech blogs.

The "micro" in "microservice" doesn't refer to how it is deployed, it refers to how the service is "micro" in responsibility.

The "micro" in microservice was a marketing term to distinguish it from the bad taste of particular SOA technology implementations in the 2000s. A similar type of activity as crypto being a "year 3000 technology."

The irony is it was the common state that "services" weren't part of a distributed monolith. Services which were too big were still separately deployable. When services became nothing but an HTTP interface over a database entity, that's when things became complicated via orchestration; orchestration previously done by a service... not done to a service.

I remember when microservices were introduced and they were solving real problems around 1) independent technological decisions with languages, data stores, and scaling, and 2) separating team development processes. They came out of Amazon, eBay, Google and a host of successful tech titans that were definitely doing "engineering." The Bezos mandate for APIs in 2002 was the beginning of that era.

It was when the "microservices considered harmful" articles started popping up that microservices had become a fad. Most of the HN early-startup energy will continue to do monoliths because of team communication reasons. And I predict that if any of those startups are successful, they will have need for separate services for engineering reasons. If anything, the historical faddishness of HN shows that hackers pick the new and novel because that's who they are, for better or worse.

They also failed as a company, which is why that's on Twilio's blog now. So there's that. Undoubtedly their microservices architecture was a bad fit because of how technically focused the product was. But their solution with a monolith didn't have the desired effect either.

I've been developing under that understanding since before Fowler-said-so. His take is simply a description of a phenomenon predating the moniker of microservices. SOA with things like CORBA, WSDL, UDDI, Java services in app servers etc. was a take on service oriented architectures that had many problems.

Anyone who has ever developed in a Java codebase with "Service" and then "ServiceImpl"s everywhere can see the lineage of that model. Services were supposed to be the API, and the implementation provided in a separate process container. Microservices signalled a time where SOA without Java as a pre-requisite had been successful in large tech companies. They had reached the point of needing even more granular breakout and a reduction of reliance on Java. HTTP interfaces was an enabler of that. 2010s era microservices people never understood the basics, and many don't even know what they're criticizing.

A useful distinction I've made before is that of technical vs business services.

This also mirrors the alignment that arises in tech companies between platform (very useful to be centralized) vs architecture. Platform technologies are useful as pure technology, and therefore horizontally distributable. Whereas big-a Architecture as a central committee died an ignominious death for good reason: product and business decisions require deep knowledge, and therefore architecture is simply a function a product team does.

I am old enough to remember when there were simply "services," and there was an understanding that a service was something a team or business function did, because it mirrored Conway's Law. The root of service is literally "serve." That there was a one-to-one correspondence between a software service and the team serving others was a given.

Microservices were a natural evolution of this. When growth happened, parts of those things improperly in a too-large service were pushed down so they could be used by multiple teams. But the idea of a hierarchy of concerns was always present in plain ol' SOA.

We should terraform Australia first.

Gigantic. Full of energy. Able to support massive inland freshwater lakes with desalinization. Essentially unlimited solar yet unable to utilize spare capacity. And it's already been wrecked from biological "terraforming" with non-native species.

If we can't figure out how to balance ecological and biological concerns on terra, we're not going to be able to do it extraterrestrially.

As a history refresher: DOGE is part of the USDS, created by Barack Obama in 2014.

Dealing with PII was an overt part of their remit, as the Medicare system (written in COBOL) had substantial difficulties dealing with regulatory change, and it was to be modernized. As special government employees, they were 1) consultants not appointed through the regular process and 2) had widespread access to software systems and private data across the federal government. And yes, many of them were the age of current DOGE employees.

At its height, there were 700+ employees with varying levels of access like this.

Accessing PII is normal for federal contractors, even young ones. I worked on the maintenance database for the F-35 strike fighter when I was 23. There are tens, if not hundreds, of thousands of people through the federal system and contractors with access to information like this.