If humanity is over-reliant on frontier labs' models to perform work, the result is a dependence on the actual intelligence of these models -- not on human intelligence. This could be a small reason, on top of many others, why investors are throwing hundreds of billions of dollars a bit "carelessly" to these labs. It's fascinating seeing the models do the "hard work" (the deep, challenging thinking) for you.
The conundrum which tricks me though - is this a net negative or a positive? If humans are less intelligent, but their output is 2-3 times more intelligent (with AI), what's the result? At what point do we, as humans, stop comprehending anything and give all intelligent work to the neural nets?
And if that does happen, could we live in a society where no work, or at least a significantly less amount of work, is needed? To me, it seems like a dystopian net positive.
It might seem far-fetched to ask these, but I think these questions are getting more prevalent by the day.
If there was a way to guarantee that every human would have equal access to external intelligence then it would be hard to argue against it but everyone knows that the US oligopoly will do everything they can to ensure that no one else has the keys to the kingdom.
Just listen to what the SV ownership class says out loud. They openly discuss how China cannot "win the AI arms race" and how China's development is existential. Existential to who? It's impossible to fully subjugate people with agency.
I am going to try to cheer you up. Hear me out. One day, not long from now, I am going to buy a humanoid bot for 40k. This human android will 1) get my groceries, 2) make my elderly parents meals, 3) go to the backyard and plant 1 acre of corn, 4) paint my neighbors house. 5) get the kids from school 6) change my oil.
What will happen? Massive. Deflation. What will you pay for an oil change? Corn? Meals? Everything is about to be free. But tokens will be expensive!! Sure but, you wont do white collar work anymore so it wont matter what tokens cost.
It's not just a dependence on the intelligence of the models, but also their intentions, as programmed by their owners.
A friend of mine asked me if I was optimistic about AI. I told him, it depends on who owns it. If the people own it, I'm optimistic. If the oligarchs own it, I'm pessimistic.
Seems like we're starting to get reliant on the intelligence of these models to keep our outputs less "sloppy". Effectively an IaaS (Intelligence-as-a-Service). With the U.S. putting the suspension on Fable 5, we might be stuck with slop.
With open-weight AI, there might not be an incentive to put large sums of capital towards training / research. There might be a donation fund of some sorts, but it certainly won't reach the level of fundraising that the frontier labs are receiving.
Because of this, I think it might not be possible to have AI *only* open-weight; major players like OpenAI, Anthropic, Google will likely stay for good, with better models than open-source versions.
I think it might look something like Photoshop & GIMP, with Photoshop being a frontier lab, and GIMP being the open-weight model. GIMP is decent for many different image editing workflows, but Photoshop is just better.
I would definitely prefer to have an open-weight model better than frontier labs'. Though I don't think it's possible.
I think the same, but I also think that local AI is actually inevitable, even if not open source models. I wouldn't be surprised to see OpenAI and others release an on-prem product. Whether that's effectively an appliance rack, or some other form, people (large companies) are going to want to run inference locally for data sovereignty & cost controls. Especially if we get to a point where companies want AI integrated into manufacturing and other air-gapped networks.
I do believe that if OpenAI and others release an open-weight model that is better or on par with their frontier variants, it might ruin their primary business model.
That is, of course, unless they develop their own hardware specifically to run this open model. But, that does ruin the point of open models.
When/if gains slow down, I can definitely see branching out into hardware to sell for on-prem inference once the models can be etched into the silicon with hard wired weight chips. I'd guess maybe at least 5+ years away from that though.
I think this is inevitable. Sooner or later, model-specific ASIC's will make economical sense. We're already seeing it happening with Taalas/Cerebras so I think it's sooner than 5 years. And inference is order of magnitude faster which is amazing.
Yeah I think that's a decent analog (Photoshop & GIMP). We're in a sort of "rapid expansion" phase right now, but unless the tech behind "AI" really evolves, better and better models will be harder to come by, with diminishing returns.
Even if the GIMP of LLMs is only 80% as good as the VC-funded stuff, that will still be plenty useful for lots of people.
And I think just having the option to use open source models is a win, even if it turns out to be true they'll never be quite as good as the proprietary ones.
Zoom out. It's a matter of time the trillion valuations will be deemed senseless, only once it will prove inpossible to extract trillions from consumers.
In the meanwhile, and regardless, software optimisations coupled with hardware continuing to scale, we will end up, soon enough, with some open weight that run on a mobile device with greater capabilities than Fable.
This is utopian thinking. The products are way too useful to not subscribe. The argument presupposes the worst-case negative-utility in the long-term scenario (AI companies will create a totalitarian nightmare) and pits it against the radical usefulness that the products are creating right now.
I've used over a trillion tokens in the last few years with zero subscription. In fact I have one with deepseek, spend 13 cents to test some automation. The rest is consumed free of charge to bleed the bleeders.
If/when they ALL enforce paid subscriptions, I will only run inference locally. By then consumer hardware will equal frontier models in term of speed and performance.
> Because of this, I think it might not be possible to have AI only open-weight; major players like OpenAI, Anthropic, Google will likely stay for good, with better models than open-source versions.
There's a more fundamental reason for this: some AI models are large enough that they can plausibly only be reasonably run in a state-of-the-art hyperscale datacenter. Open sourcing such models would be largely pointless. Note that this would be a significantly larger scale than even the largest open models available today, one that precludes even doing inference slowly on a small-scale, cheap makeshift cluster. But it's plausible that Fable is there already.
Which is the nearterm future that we must demand: a stop to the amounts of capital flowing to ASI research. Join me, Anthropic, Google, and OpenAI’s-founding-charter in saying the obvious, y’all; Pause AI, now.
It should be clear by now that there’s a whole universe of work to do with the models we have today, from studying to securing to ‘harness’ing. There are tons of economic benefits to be reaped already, if applied carefully. Doesn’t that sound nicer than rolling the dice with the lives of trillions?
That is fantastic news then, if commercial product products will always be better than open source, and open source products will continue to get better
Agreed. The only "issue" is that commercial products will always be ahead, with less friction for most users. This ultimately results in most people using these over open-weight variants. Users might not even be aware that the open-model variants exist. Similar to Windows / MacOS and Linux.
In a way that's ok, though? I run Linux on my laptop, and in some ways it's better than Windows or macOS, and in other ways it's lacking. But that's fine; the existence of Windows and macOS doesn't mean I can't run Linux, and doesn't mean I have a worse experience.
(Yet; I do worry about future required hardware attestation for basic things, but that's another issue.)
Well. Right now buying hardware to run your own models tops off at about 32gb VRAM at any price point that's not insane. Sure you can get a Mac mini, or a PC equivalent. But the problem is RAM.
More RAM means bigger models, which means smarter models.
Which is why Qwen and Gemma have been so interesting to a lot of us who run our own... Now 32gb VRAM isn't so bad, as these models can be run on that with decent results.
Where this gets interesting is in a couple years, when all the A100, etc, all the Enterprise hardware hits eBay.
> Warns users about how dangerous and powerful Mythos Preview is
> Restricts model to large corporations
> Release information about how Fable / Mythos 5 is stronger than Mythos Preview, give access to every user for a limited time via subscriptions
> Users jailbreak model
> U.S. suspends Fable / Mythos use
Who didn't see this coming?
I wonder what this means for the future of AI models. Either we'll see worse guardrails than what was there for Fable 5 (for me, it was a unusable at times), or the models just stop getting better from here.
I think it's that the guardrails will be more strict, which is unfortunately not good news.
I see many comments saying, "AI can't do X with 80-100% accuracy; therefore our professions are in good hands."
While I don't want to sound overly pessimistic, the models are improving at a rapid rate. If asked ~3 years ago where the state of the models are today, it would sound like sci-fi if answered, "the models are creating full MVP apps in ~30 minutes with one prompt".
The hurdles the models are facing now, like reducing hallucination rates, ensuring compliance, and keeping a clean codebase, do not seem far away from being resolved IMO. Fetching specific information is already partially done with various MCP servers / RAG.
I am, of course, a bit worried about the future of software engineers. If these quirks are resolved, where do their professions fit in the industry? Delegating tasks to the AI model? Unfortunately, this does not require years of expertise, which is a double-edged sword. Reviewing AI's output? Ask it to explain each line not understood.
I think we will see more waves of larger layoffs, similar to how human computers were replaced by digital computers. To some, doing complex mathematical calculations mentally is a fun task / challenge, but it is ultimately significantly slower and more error-prone than calculating with a computer. In the same way, I think hand-crafting code will be seen as a fun "challenge" and AI will be seen as the "modern-day calculator".
> the models are improving at a rapid rate. If asked ~3 years ago where the state of the models are today, it would sound like sci-fi
Absolutely true, many things will continue to improve in significant ways. However, if we look at the modern history of rapid disruptions driven by technology (a side interest of mine), persistent patterns emerge. Similar to avalanches or flash floods, such periods of very rapid disruption are often triggered by one or more significant breakthroughs in certain technologies. Early rates of change tend to be fast and furious but eventually begin to taper as recently unlocked low-hanging fruit is harvested and those racing through newly found terrain encounter all-new significant barriers and points of friction. Early in such periods, extrapolating the recent extraordinary rates of change forward has poor predictive power. Sudden extreme bursts tend to regress back toward the long-term trend line.
Arguably, the current disruption in LLMs can be traced to post ~2010 research slowly building to the 2017 transformer paper and the adjacent work it quickly inspired. So today is, arguably, mid or late-ish in the LLM rapid burst phase. The rate of fundamental, broad-based breakthroughs lifting all LLM applications has clearly slowed with many of the most impactful recent discoveries being in scaling, optimization, tuning and productization toward specific domains. That doesn't mean there can't be another transformer breakthrough tomorrow but, historically, black swans rarely travel in flocks.
> The rate of fundamental, broad-based breakthroughs lifting all LLM applications has clearly slowed with many of the most impactful recent discoveries being in scaling, optimization, tuning and productization toward specific domains.
To me it definitely feels like it's still accelerating, with the most impactful recent discovery being RL training reasoning models (late '24, early '25).
There's an interesting article called "sigmoids won't save you" https://www.astralcodexten.com/p/the-sigmoids-wont-save-you which argues that (unless you have privileged information) you should always assume a process will continue about as long as it’s continued already. (Lindy's Law)
With that in mind the current disruption should last another 10-15 years (assuming it started in '10 or '17.)
This is of course true in general. But the question is not "how with this evolve" but how will we deal with the rapid changes in the industry? I suspect a long term k-shape salary curve, even worse than today, with the lower 80-90pctile salaries bottoming out such that many have to exit the industry to make ends meet. You can laugh and blame them for not saving as much as they should, but that's still a fairly horrifying prospect for most of us.
I think a _lot_ about stock trading a profession vs algorithmic trading. It was brutal - suicides, many pivoting out to doing car dealership-style work. Probably a 1/10 or 1/20 survivor rate every couple years, with almost all of it a very painful five year period.
I would ask for references for the suicide claims, so others can assess the impact themselves. That's a very serious claim to provide without any proof, especially to a group of people who very well be going through the same thing. I am not saying it did not happen, only it's the right thing to do.
That really depends on how you define alive and well. There are still stocks and there are still traders, but the market valuations are obscene and it sure appears that there must be collusion or corruption driving the industry to jam massive IPOs into every index and 401k they can find as fast as possible to fasciliste and exit.
Progress happens in a series of S-curves. While your observation is correct that advances occur initially rapidly then taper off, the next step tends to arrive sooner than the previous, and with greater magnitude [1]. Tim Urban's article from 2015 has a great explanation of this phenomenon [2].
> The rate of fundamental, broad-based breakthroughs lifting all LLM applications has clearly slowed with many of the most impactful recent discoveries being in scaling, optimization, tuning and productization toward specific domains.
What this means is that the disruption across industries not even truly begun, because it's not the generic chatbot models that are going to kill labor, it's all the domain-specific applications that leverage those models to perform work that was performed by humans
Why would it stop with just developer layoffs? When software companies rely on LLM providers to run their business, I’d argue we‘ll see a massive bust of these companies around the world - from on-prem products to SaaS.
Customers may build the software they need entirely in-house or via prompt-engineer consultants, without the need to buy software tools like today. It could be a very very different world.
> Customers may build the software they need entirely in-house or via prompt-engineer consultants, without the need to buy software tools like today. It could be a very very different world.
Already happening. I know of a few places that have gotten such large gains from LLMs that they know have their engineers working on creating homegrown ports of popular services (Docker etc.).
Why would you create a homegrown port of Docker? Docker the container software, or Dockerhub the image repository? This is just confusing. If you didn't want to use Docker there is a perfectly good well tested alternative called Podman with wide adoption.
Not sure about Docker (lol) but stakeholders are definitely more open to "building your own" now. It used to be that to be agile as a business you would seek out already built software and rent it, as it typically was cheaper than building and maintaining your own (I say typically due to stuff like vendor lock-in and such). But these days, and especially in 2026 with the widespread use of agents and harnesses, that formula has started to change. Even though the SOTA models are really good now, it's the harness and the "fluff" around the model that makes it a game changer. The developer is no longer the one writing or even gluing the code together, the harness does that. Pair that with context preserving mechanisms and tools that emerged (automatic context compaction, AGENTS, TOOLS, MCP...) and you can get to a state where you start a new thread in Codex and it knows your systems, your dbs, can smartly explore code it doesn't know and db data patterns etc., it can explain stuff to a new developer (and be correct most of the time and have time to spend on the developer)... all of which SIGNIFICANTLY reduces the risk you take on yourself as a company when you "build your own". What's $10k/year to any half-working semi-profitable company? Nothing. But in 2026, you can build and maintain A TON of software for that, much more than your "average IT needs" company may ever use.
I'm sure the very large (and very small) businesses will keep their absolute need for (or the lack of) inhouse developers, but everything in between will probably get compressed to one or two inhouse architects in direct contact with the stakeholders and the rest will be contractors working with Codex-like automation.
Homegrown ports of calendly or jira seem feasible, and arguably a good business decision. Homegrown versions of docker seem ridiculous as a starting point, even if its possible to do today there is much lower hanging fruit to go after first.
> I know of a few places that have gotten such large gains from LLMs that they know have their engineers working on creating homegrown ports of popular services (Docker etc.).
Sounds like a good way to eventually erase those gains.
This won't happen in most cases because the valuable thing is largely the knowledge encoded in the software, which the buyers of the software don't have and don't want to have since they're focused on their own business.
There's also, of course, the not insignificant value in the software itself actually working, being operated, being updated when necessary, all of that. Again just extra hassle no business will want to shoulder when they can just buy something that does it for them.
I have personally replaced multiple tools that cost me money every month and now cost me $0/mo. They are low stakes but they work and have near zero maintenance (only changes are me adding features or fixing the occasional bug I missed).
Why would I pay someone even $10/mo when I now have a $0/mo solution?
1. There is value in a tool that solves precisely your needs, in the way you want it solved.
I've repeatedly seen enterprise SaaS purchases where the company ends up wrapping/layering on top additional tooling, software, and infra to solve core needs that are absent (or misaligned) from the saas tooling, but required for their specific usage.
I've directly experienced this with: analytics tooling, customer survey tooling, feature flag tooling, and interview tooling.
If your going to dedicate a dev anyways - the numbers can change here.
Is this every SaaS product for every business? Fuck no - but there are products that might be adjacant to your core business where you have both strong preferences & experience, are already spending for customization, and now it makes sense to pull the whole thing in-house.
2. The 50k/m crm is competing with that 500/m crm. which realistically appears to soon be competing with that 50/m.
Even if we stick with your stated observation that end businesses don't benefit from building their own tooling (which is fair and often true, although I'd wager it's not as clear-cut as you imply) - you're dismissing competition that is absolutely willing to undercut the market because they can slip on quality (slop - as you say) but still serve a need to customers who place cost as the primary buying metric.
If the customer is better served by the 500/m crm, why stop there? Why not go for the 100/m crm? The 50/m crm? Why not chug on down to the lowest possible cost competition, which likely will be 2-5 guys with an llm they ask to go copy "[insert crm of choice]", and then bill just slightly over infra costs.
Or the other thing I'm seeing happen in a lot of spaces right now... the "do it all" SaaS companies, that are pumping out into adjacent verticals that previously would have been too expensive to develop. The bill stays the same, but now it's not just a crm, it's the original crm, plus a clone of all the adjacent market leaders... scheduling, billing & invoicing, marketing, SEO, site hosting and design, social engagement, etc...
One SaaS elbowing into other verticals but keeping the bill the same, which I consider functionally equivalent to the competing on price, just wrapped in a different flavor (it won't be 2 dudes in a basement, it'll be 2 dudes on the "crm" squad in a bigger eng dept).
Starter is the "under 10" seats, but they don't have most of the features companies actually want, and the next category is suddenly $266/seat/month. Which is what they really expect enterprises to be paying per seat.
But.... like I said, that leaves a TON of room for getting undercut by a copy-cat company if the only competing metric is "price".
Hell, there is a lot of really good open source software that fits most peoples needs already, that can be self hosted and costs nothing but the running of it. But people still pay for the SaaS product. Because you're not just paying for software. you're paying for support, uptime, compliance etc. These people think that SaaS is dead confuse me.
Nothing corroborated this. Performance on benchmarks has practically leveled off. The big gains have come from architecture (have a secondary LLM review output) or searching the internet. Also prices are going up. Everything points to the likelihood that we're at the top of the curve.
> Nothing corroborated this. Performance on benchmarks has practically leveled off.
[There is plenty of data to support the claim that AI continues to improve, even exponentially.](https://epoch.ai/trends)
As for benchmarks I feel compelled to remind you that as soon as a metric becomes a goal, it ceases to be a useful metric. The models optimise for solving the benchmark and we create new benchmarks to assess broader intelligence. As models converge on 100%, progress obviously slows. That doesn't mean intelligence isn't improving fast. It just means that that benchmark is being well served and we need other benchmarks to assess other forms of intelligence.
I would like to take your bet that we're near the top of the curve. I take the side of Geoffrey Hinton, the Nobel Prize laureate scientist known for his work on artificial neural networks. He believes AI is getting better even faster than he predicted. He estimates that every seven months AI becomes able to handle tasks twice as long.
> [There is plenty of data to support the claim that AI continues to improve, even exponentially.](https://epoch.ai/trends)
This doesn't look at all exponential to me: https://epoch.ai/benchmarks?view=graph&tab=eci. OpenAI models went from 137 ECI to 159 ECI over about a year and a half, and the trends are similar for Anthropic and Google. These things have never been exponential.
> The models optimise for solving the benchmark and we create new benchmarks to assess broader intelligence. As models converge on 100%, progress obviously slows.
...also, progress isn't improving with model releases.
---
We're running out of money. While we don't know how much it cost to train things like Claude, most (all?) industry reports indicate that a significant gain in function (2x) would require an exponential amount of resources (20x). No one's yet been able to convince investors that's worth it.
Also, we're running out of of low hanging fruit: "We find that the level of compute needed to achieve a given level of performance has halved roughly every 8 months, with a 95% confidence interval of 5 to 14 months. This represents extremely rapid progress, outpacing algorithmic progress in many other fields of computing and the 2-year doubling time of Moore’s Law that characterizes improvements in computing hardware (see Figure 2)." (https://epoch.ai/publications/algorithmic-progress-in-langua...). Maybe you think we'll continue along this breakneck pace, but again no investor thinks that, which is why prices are going up (investment is drying up).
Also we're running out of compute. Data center projects are stalling. Some of this is spiking energy prices, some of this is politics, much of this is grid constraints and supply chain problems: https://tech-insider.org/us-ai-data-center-delays-cancellati....
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Finally, and perhaps worst of all, despite unprecedented investment data on the productivity gains is mixed. This is the biggest difference from other technological leaps like electricity, the industrial revolution, literally fire, etc. Those things were immediately, undeniably more productive. AI is not like that. You're not seeing an AI Microsoft, an AI Salesforce, an AI Oracle, an AI SAP, etc. You can argue that their advantages are structural, but there are no successful AI-powered alternative products (no AI Office, no AI ERP, no AI database, etc).
> Performance on benchmarks has practically leveled off
Ehm, no? DeepSWE[1] for example shows that new models like gpt-5.5 continue to show big improvements compared to older models.
> Also prices are going up.
Prices for frontier intelligence have gone up, but prices for the same level of intelligence have gone way down (what you can get for pennies now was SOTA just a couple of years ago). The pareto frontier is still expanding.
Most benchmarks can be trained for as well, so they are over-representative of model's engineering skills.
The entire nature of a benchmark is collapsing some qualitative work (software engineering task, architecture choice, code quality) into a quantitative score which can be optimized for.
That's what the AI comapnies would like, but they can't pay back the 100's of billions they are blowing without 10,000x the price they charge. The investors won't allow it. we're not even in a revenue cycle yet and they are already trying to dump their deep losses on retail by trying to IPO
What is your theory of when AI gets to 100%. PMs and business analysts build all the software? Or just like a 700 or so 1-founder companies in the world and everyone else is without work? The matrix?
See my issue with these comparisons is that it always compare AI to 100%.
When 100% does not exist. Most software out there has issues, bugs, compliance problems, security weaknesses, scaling, redundancy, availability issues...etc. A lot of this is not actually related to how good or bad software engineers are. It's about costs and time to ROI. Greed is an issue too.
So people seem to have this idea that software created by humans is perfect (its not). And that deterministic (human created software with if/then) is alway going to be better than probabilistic (LLMs). Which in a perfect world is the case, but we live in a capitalistic world where this is not the case.
> Or just like a 700 or so 1-founder companies in the world and everyone else is without work?
This. But instead of 700 it's more likely that everyone will be a founder (more or less). It's already scary how easy it is to launch an MVP or produce prototypes with the latest models.
In my own (admittedly limited) experience, 2 employees in my company (that had no programming knowledge or experience) have vibe coded apps that simplify their daily roles. The apps basically automate a flowchart of steps where multiple people need to submit certain pieces of info and as they do, a "project" moves through stages and the employees get notified on Telegram.
The app really is just several simple forms with some if/else logic, but claude code allowed them to get the app up and running and deployed on vercel's free tier, and it's Good Enough™ to save them an hour or so each day lost in messaging and chasing up things.
I don't think anyone would ever have targeted an app for sale to them, and it would have been hard to twist some sort of flow management app and integrate it with Zapier or something to handle external api calls. With claude code they could just tell it what they wanted and solve their very niche issue. That's why I think that even though LLM coding has improved so much you might not see more software for sale - it's easier for people to just...make their own software.
The best part of this workflow - which I see often - is that by having someone build custom software to automate some process they often step back away from the process being their job. That eventually translates into them understanding that some (or sometimes most or all) of that process is not needed. There are so many corporate processes that were implemented and then become the way... and if there are people who identify that process as being their job those people resist attempts to optimize that process.
I have seem several people use AI to write apps to automate a process and along they way finally ask the question 'do we even need this process?'.
Don’t get me wrong, :) that’s pretty cool! I’ve also made highly personalized mini apps for my own personal life. Currently working on an iOS one to log mood and correlate it with HealthKit data since the native health app does a bad job of it.
That said, I meant more like production grade apps that have to serve N>1, which is IME where the hard part LLMs suck at comes in. I saw a tweet somewhere along the lines of “CEOs/execs are so divorced from the last mile effort that they are uniquely susceptible to believing AI can replace engineers end to end”
> It's already scary how easy it is to launch an MVP or produce prototypes with the latest models.
No it isn’t. The things that were hard are now harder. The things that were comparatively easy are now easier. But if you build another piece of vibe-coded crap in a world awash in vibe-coded crap, you will not stand out. Nobody cares about your unpolished, one-shot prototype, so cranking them out faster is not really helpful.
Differentiation is always a problem of effort and care, and this isn’t going to change.
>If asked ~3 years ago where the state of the models are today, it would sound like sci-fi if answered, "the models are creating full MVP apps in ~30 minutes with one prompt".
The first one-shot app was created with ChatGPT in June 2023 - 3 years ago. In my experience, the current result of one-shotting apps is just as bad today as it was back then.
What “full MVP app” are you talking about? I know of none that have been anywhere near production ready. With all due respect, I think you’re portraying fantasy as reality. I would love to be proven wrong.
> The first one-shot app was created with ChatGPT in June 2023 - 3 years ago. In my experience, the current result of one-shotting apps is just as bad today as it was back then.
Hard disagree. Take a good SaaS starter template and do a bunch of harness engineering. You can get an agent to shit out production grade stuff. You might argue that's cheating, but there's nothing stopping you from doing it, and it works. It's only getting better too.
When did you last try this? Here's a prompt for you:
> It's really important to me you make sensible decisions here, and don't bother me with the small stuff. I want a plant-watering app me and my wife can share, that shows who watered which plants in our house. I'll deploy this on my home server with Coolify. The app should be attractive, work both on desktop and mobile. We have a bunch of cases where we have multiples of one plant type. We'll need separate users, but don't go overboard with auth. I want to impress her, so let's lean on the side of more rather than fewer, features, but I don't really wanna run anything that won't just fit in a single container with some persistent storage. We're the only two users who'll ever actually give this a go. Visually attractive is important to me.
I think we’ll see a lot of layoffs and then the tech industry is going to become more vertically, integrated with product, business analysts and developers all combining into one role. It sucks because there goes one of the highest being roles in America right now that actually employed a lot of people.
Ironically, I don’t think tech support is going to be fully replaced by these anytime soon. That’s one place you definitely need to have actual people talking to other people. Lawyers and doctors are gonna be legally protected too because you still need a human to sign off on all those actions though we will probably need far fewer.
Are the models all that much better? To me it seems the tooling surrounding the models got better, but the models themselves are basically interchangeable unless you're following a bunch of flawed overfitted benchmarks.
Also MVP apps are great and all, but I've seen 0 evidence of actually useful software from all this tooling, if anything all the software I'm using has just become more buggy and less reliable over time
I don't know, even if AI allows two engineers to do the work of six, companies will likely just use that efficiency to expand their scope. I think we'll see short-term layoffs and a more stratified engineering field during the transition, but the fundamental need for deep technical expertise isn't going away.
>I don't know, even if AI allows two engineers to do the work of six, companies will likely just use that efficiency to expand their scope.
Not really. It will be a cuttthroat landscape, and the scope wont matter as much anymore. First because everyone else will equally be able to throw LLMs at the scope, but also because the scope has natural limits: your market fit, customer expectations, and (for software/hw products) physical world/manufacturing limitations.
AI usage will directly impact said margins. Moreover, for the scenario you describe, companies need to have the capability to precisely estimate the cost of a given deliverable - not something possible with current tooling + models. You're also underestimating the market trend towards vertical integration: companies are not going to be constrained by a sector or niche. They will expand to capture as much value as they can, because now their capacity to do so is partially decoupled from labor.
It will certainly be a cutthroat landscape for engineers, but companies will be building _more_ capacity, not less. In other words, the demand won't disappear for skilled technical labor, it will just move higher up the value chain.
>You're also underestimating the market trend towards vertical integration: companies are not going to be constrained by a sector or niche. They will expand to capture as much value as they can, because now their capacity to do so is partially decoupled from labor.
They will still totally be, because the capacity to do so was never coupled to labor, it was coupled to domain knowledge, client network, other players dominating the market, and so on...
> even if AI allows two engineers to do the work of six, companies will likely just use that efficiency to expand their scope.
1) they won't, they'll just cut costs
or
2) they will, but unless it's a new scope or one that can absorb growth, they'll just be competing with other companies in the space and taking away business from them
Something tells me that you have some kind of anti-AI agenda and that you are not really looking at things objectively. Maybe it’s your nick “ai_fry_your_brain”, maybe it’s “ If you disagree with me, you're a slopper.”, who knows!
Not all work is the same. Some of it is debugging, some is testing, rubber ducking, etc. LLMs reduce effort on such types of work, and the range of what they can handle keeps growing. If you don't see that yet, that's exactly why you should invest the time and mental effort to learn these AI tools deeply.
But is the model aware of the training? Unless you hook the model up to an MCP server, or something similar, and have it analyze the RL changes, it will not know if it has changed or not. Even if it is real-time RL, it is not aware of the previous state.
I would not mind if it was +50% more expensive... if it was TRULY a competition to f.ex. a Macbook Air. Many more techies would not mind it. But I don't think we ware there yet.
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