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My mistake; it reads "3.6 Flash" and "3.8 Thinking"; I assumed "Thinking" meant "Pro" but maybe it doesn't.

At this point if a Google PM handed me $100 and said "go figure out how to issue a prompt to Gemini 4 Argon" I'm only ~30% sure I could figure out how. The Gemini app in my phone only has 3.8 Pro as the latest model; it does say "Ultra" in the bottom left, but I really don't think I'm on the Ultra plan. How I would sign up for that, on a Workspace account, is beyond our brightest minds.

Its sad watching them try to keep up when they want nothing more than to crumble under the weight of their own bureaucracy, and at this point I'm not sure there's any hope for them.

They might, but I am skeptical of this idea. The story of the past fifteen years in enterprises has been pretty clear: Move things to the cloud, we don't want to manage data centers. There are notable exceptions, but by-and-large that's the trend, and I don't understand why people believe this will suddenly reverse in the era of AI. Running a GPU cluster is substantially harder and more expensive than any other cloud resource. These companies don't even want to run their own Microsoft Exchange servers anymore, and you think they'll want to run an NVL72?

To be clear, I don't like this trend, and I strongly believe most companies are better off owning as much of their technology stack as possible, working with partners to help maintain it rather than making themselves lesser by working with AWS/GCP/Azure; but it is the trend.

Something like Azure Foundry w/ ZDR is more likely. And yeah, its possible that Ant will find competition from those kinds of providers. But Ant will also find revenue from those providers, and its hard to say where things will land in the end.

I don't think the models being a commodity matters much. Consider: Email providers are a commodity. Cloud linux server hosting is a commodity. Checking accounts are a commodity. There's a lot of great businesses, trillion dollar businesses, built on commodities. DeepSeek v7 Pro might open source and be better than any Claude model (unlikely), but JP Morgan and Eli Lilly are not going to go spend $500M on chinese AI tokens routed through OpenCode. They just aren't. The models really matter far, far less than the experience these companies build around the model, and both Anthropic and OpenAI are doing a decent job of making that experience reasonably sticky and compelling.

The M3/MY are 2-2.5T. The Roadster will be lighter, and 1.5-2T is the same weight class as the 911 and Boxter.

You should go look up the weight of a 911, Boxter, or Miata. And then, I suspect, you will not return here to reply to this comment. You'll invent some other reason why EVs can't be "nimble" or whatever, because it can't be your worldview that's wrong, it has to be the world.

There were multiple demos; its possible you only saw one, and then raced to comment about something you had incomplete knowledge about.

Well, you're insane for thinking that. Drunk driving is very unsafe, and population-level statistics don't lie. They're quite conclusive when it comes to grading the safety of drunk driving as being dangerous relative to sober driving, and Tesla's full self driving system as being safe relative to sober driving.

Only from a regulatory perspective. Anyone who drives a Tesla for long enough knows how to defeat the monitoring system, and doing so is extremely safe; significantly safer than a human driver. I would have no concerns summoning it autonomously over 100 miles, once the law allows for that to happen.

Yeah they'll make a few dozen of them and sell them to people like Joe Rogan. There is zero chance that any individual who doesn't have the personal phone number of a Tesla leadership team member will be able to buy one of these.

If you still believe FSD doesn't work, you're operating on outdated intelligence. Frankly, like a boomer saying that email doesn't work.

Teslas already have very strict (annoyingly strict) gating keeping specific functionality off of public roads (Summon). The drag mode thruster-assisted boost will probably leverage that. I also imagine that cameras on the car would stop it from being engaged if it detects humans in its firing area even when not on public roads.

The flying trim will never be sold to general consumers. Even the rear-thruster trim is currently listed at a $250,000 pre-order fee, which is a deposit for a fraction of the full cost of the car. Final cost will be over $1M. Any of the thruster variants will be extremely rare; like, the one guy in your city that has one will show it off at car shows twice a year, McLaren P1, level of rare.

Generally, very true in mental health, but moderately true for all areas of health: The idea “at least we have systems/doctors/drugs to help solve this if/when it becomes a problem” is a massive cognitohazard. You do not want to be on the other side of that fence, and belief in solutions there will slide you toward it quicker. Do everything you can to avoid it while you’re on the other side.

e.g. if you aren’t getting 10,000 steps every day for six months straight, you aren’t doing everything you can. It might not solve your problems, but you don’t know that, because you’ve never tried. If you aren’t quitting nicotine & alcohol, you aren’t doing everything you can. If you aren’t saying hi and being shot down by at least one stranger you find attractive every week, you aren’t doing everything you can.

Decision models have the potential to have an even larger impact on the Real World than LLMs have to this point (which is obviously quite large). But the model itself matters less than the product experiences you build around the model, and its very likely that the incumbent labs are treating the area as something more like "oh yeah I guess we can ship that and then forget about it" rather than investing in what building business processes on decision models looks like. Unlike full language models, I don't think the primary business of Typesafe will be serving Jev at API pricing; it'll look a lot more like putting Jev at the center of a much more expensive suite of software.

There's the potential for an inverse LLM play. In contrast with LLMs, all that seems to matter is the model, and the products the labs build around the models are all really samey and boring; the same left panel list of agents, main view agent conversation, right hand extra context, and we're now in the era of everyone creating the same cutesey furry friend on top of all this tech.

As I recall, they picked Deno over Bun during the whole vibecoding rewrite fiasco from six months ago, and there wasn't a ton of explanation given beyond the general vibe of "ai bad". They might reevaluate that in light of things being generally fine and losing their precious handcoded runtime choice.

Its simple. My company is very willing and able to pay ~$200/engineer/month for the best version of these tools. My company is even willing and able to pay as much as $500/engineer/month, but does not need to at this moment.

My company is not willing to pay $50/engineer/month for a cheaper version that is nearly as good. My company is also not willing to pay any amount for a product produced by China, even if it is hosted in the United States.

I think this is everything right about Apple. There is zero chance the building is not up to code, at least within the context of some exception granted to them by the city. Their extremely numerous number of employees are likely well trained on what to do during a fire, including where the extinguishers are. I can think of no stores I'd rather be in, if there's a fire, than that Apple Store. But, it makes nerds angry; they're more interested in the process than the outcomes.

Wow, the first four comments are negative. Welcome to Hacker News everybody!

Reading comprehension check: I never said product managers would be the new engineers: I said that engineering is becoming product management.

Yes I think that's correct. But I'm worried less about safety and more about fit. Articles like this convince me that there are some engineers who will struggle to adapt to a world of "no one knows what's going on". Engineers have always had the benefit of making abstraction concrete, through the systemization of abstract business requirements into concrete code that the team would build a shared understanding of over time. But that doesn't seem to be where the industry is trending.

PMs have always lived in the world of dealing with hazy abstraction in both directions: Unclear requirements coming in, turned into unclear system capability whom they have to rely on the engineers to parse. This is where Engineers will have to get comfortable living now: Unclear requirements coming in, unclear code coming out. Its clear that many engineers aren't ready for this, and I don't blame us; it SUCKS. If I had ten dollars for every time I've heard a PM say "no one has any idea what's going on" over the past fifteen years, I wouldn't have to work anymore.

Engineers are probably still the role most suited to adapt to this new world, as you say, but I think people are still vastly underestimating how much they will be personally impacted by the changing industry. If you hate your job now, for reasons like those the article communicates, you'll hate it ten times more in a year.

The future of engineering is product management. I don't believe there is any world left for people whose primary responsibility is opening pull requests; and we're seeing the angst against this happen from both directions. Engineers hate it. Leadership feels they don't need them. The truth is somewhere in the hazy middle; we're just in an uncanny valley right now where neither side can take the leap to cross the valley: the agents aren't good enough yet, and the bigger problem is that there's no job title or corpus of experience leadership can look to and say "Yeah that's the person we need in this role".

The labs saw this early, and thus many roles at the labs are "Member of Technical Staff". That's the future for every software team. You're not a software engineer anymore, but you're also not a PM, nor a designer. Think horizontal slices, not vertical: Every human's responsibility is to leverage AI to be an expert on everything necessary to deliver some vertical slice of the business.

I don't feel that all fitness trackers are bad, though some use-cases for them are generally bad (such as sleep tracking). Watches are quite good for exercise tracking, and tracking exercise has legitimate and high quality value (e.g. competitions with yourself to incentivize improvement, strain tracking to ensure you aren't overdoing it, daily targets and goals to incentivize activity). The idea of all-day tracking is probably on the net unhealthy for almost all humans though.

My comment was targeted at rings in-particular, because 1. they encourage that all-day tracking paradigm, which is probably on the net unhealthy for almost all humans, and 2. their sensors are so bad that even if all-day tracking were healthy (it isn't) they physically can't do it to any degree that is meaningful.

There is not a single modern model where, when asked to "Turn the Add To Cart button Blue", would also turn the "Cancel" button next to that add to cart button blue. That's what this site is communicating, literally, and it does not happen. You'd have to rewind to, like, GPT-3 to get behavior like that (actually, even that would probably do it fine. Maybe a 50 million parameter model embedded in a microwave would struggle).

I am not going to be brain-off empathetic to purported lived experiences which no one has lived, when those purported lived experiences are simultaneously and irreducibly denying the lived experiences of the vast majority of LLM users who do not have these problems. And, frankly, no one should be. At some point, if you're having these problems, its time to grow up, be self-sufficient, and figure out why your interactions with these systems are so much worse than the millions of people who are having perfectly agreeable interactions. Or leave the industry.

The biggest reason should be that they're just bad. They gather sus data from their undersized sensors, then derive tons of sus insights to overwhelm you. None of it is accurate enough to base major life decisions on, which is also laughable because even if it were accurate, there are effectively no major life decisions to make based on the data! Here I can save you $500: Go to bed before midnight. Set your alarm at least +8 hours from when you lay down. Sleep in a cool, dark room. Eat nothing 4 hours before bed. Don't exercise 4 hours before bed. Limit alcohol. Engage in at least 30 minutes of strenuous physical activity each day. Done.

I don’t know where you picked up the idea that my intention was to be helpful. I’m only mirroring the intentionality behind whoever created this site; they obviously also had no intention of being helpful, as is the case with many discussions concerning AIs limitations.

But its not even good satire, because its totally unrepresentative of my and most others' lived experience. Its similar to making a joke about a calculator misadding two numbers because a stray beam of solar radiation flipped a bit.

I'm probably between $50-$200/day depending on the day; we also have effectively unlimited budget, though a lot of that is because Azure gives startups $150,000 in credits for 2 years, which we've wired up to a LiteLLM gateway & OpenCode. Without that I think our appetite would be more around $400/month/employee.

A lot of my high costs is because I just throw Sol at everything. If I were more selective and brought in Luna or v4 Flash every once in a while, I think I'd be more like ~$400/month. That's why I'm not aligned with the notion that "tokens are subsidized so that's why people are using so much": its not that I'll have to adjust to using less, its just that I'd need to think before I prompt a bit and be more judicious. I could easily see my raw token counts doubling or tripling in the coming months. I don't think that will change as subsidization subsides; though maybe lab revenue will; intelligence per dollar is getting cheaper every week. Its solely a function of adaptation to process, which takes time.

The productivity gains per token are the single most asymmetrical thing I've ever seen in engineering. The engineers on our team are pretty effective with tokens; easily that 2x-4x output as you're seeing, spending $20-$200/day. Some of our security folks have also started contributing more-and-more code, and they're on the other side: they'll spend hundreds a day running in circles, eventually producing these +/-30k loc pull requests that take ages to get merged and are littered with issues. They weren't writing much code before, so arguably they're more productive by some multiplier greater than 1, but I think the drag on the rest of the team, and potential issues with what they produce, has overall created a net-negative situation. Inversely, some other company functions have produced a few one-off websites for things like sales processes, and those have been a huge win. The asymmetry is wild. There's almost a valley of incoming skill where if you know nothing about code, you'll leverage it well; if you know just a little bit, it makes you super dangerous; if you know a lot, you're the biggest winner. Really difficult situation to navigate.

Your issue, I believe, is that you seem to believe capabilities are measured along one axis. This is natural to believe because it is representative of how the models have evolved up to this point, and thus it is also what many AGI-pilled people believe.

Critically, you did not quote the most important part of my sentence: "useful progress will probably slow down and become more linear starting in Q4"; your omission of those words is why I believe you don't understand what I'm saying; you didn't find it important to make your point, so you omitted it, when actually it is critical to the entire assertion. You can read my third paragraph, if you wish, to understand why it is important, instead of just stopping at the first word you disagree with and hitting the "Submit Comment" button.

Coding is an extremely verifiable and loopable task, like math (in fact, all of the math that these models has done has been through the lens of Lean, which is itself just coding). I am talking about their capabilities in tasks that are more general, the execution of which represent the vast majority of economic value generation in the world.

I'm very bullish on AI, but I still feel that we pretty much plateaued at Opus 4.6 and everything since then has been in the domain of "extremely verifiable and loopable tasks" (math), benchmarkmaxxing, and harness improvements. Which are fine to good things, but I think its very reasonable at this point to start asking questions about when we'll see progress in more general domains. The readability of AI output, for example, has nosedived as they've gotten more intelligent, which makes the frontier models difficult to use even for things like writing emails.

In that sense, the frontier models are going to quickly blaze past any semblance of usefulness to humans, while every once in a while we get a news drop like "GPT-7 solved some crazy math problem" or "it invented some new awesome drug"; meanwhile what most people will use will be smaller, more human-specialized models, maybe distilled from those frontier models, that take much longer to iterate on because they rely on large amounts of human feedback in the domain they're specialized for. In other words, useful progress will probably slow down and become more linear starting in Q4, bounded by the rate at which the humans paying for it say "yes this is a good react website".

(By the way: I earnestly do categorize "inventing a new drug" as non-useful AI progress, counter-intuitively. The drug industry has more ideas for drugs than they know what to do with; "useful progress" is, after the idea is made, validating that it works in humans and doesn't kill the human, and productionizing it. AI will help with this and does, but I have substantial doubt that we'll ever see the drug pipeline speed up to, like, a year from idea to prescription. That would be useful progress, which unfortunately many AI pilled hypermaxers conveniently forget. The invention of a promising new drug, or the solution to an arcane set theory problem, are cherries that, through the diligent labor of humans and AI, may become useful, but progress is rarely made by the lone intellect having an a-ha moment.)