AI has all the same markers of a the dot com bubble and eventually venture capital will dry up and many AI companies will go bust with a few remaining that make something useful with an unmet niche.

A lot of the predictions come from interviews and presentations with top tech executives. Their job is to increase the perceived value of their product, not to offer an objective assessment.

I've gotten a lot of value out of reading the views of experienced engineers; overall they like the tech, but they do not think it is a sentient alien that will delete our jobs.

I have also gotten a lot of value out of Cembalest's recent "eyes on the market", which looks at the economic side of this AI push.

Both of these agents launched mid-2025.

> So, this is how I’m thinking about AI in 2026. Enough of the predictions. I’m done reacting to hypotheticals propped up by vibes. The impacts of the technologies that already exist are already more than enough to concern us for now…

SPOT ON, let us all take inspiration. "The impacts of the technologies that already exist are already more than enough to concern us for now"!

I believe that Codex and the likes took off (in comparison to e.g. "AI" browsers) because the bottleneck there was not reasoning about code, it was about typing and processing walls of text. for a human, the interface of e.g. Google Calendar is ± intuitive. for a LLM, any graphical experience is an absolute hellscape from performance standpoint.

CLI tools, which LLMs love to use, output text and only text, not images, not audio, not videos. LLMs excel at text, hence they are confined to what text can do. yes, multimodal is a thing, but you lose a lot of information and/or context window space + speed.

LLMs are a flawed technology for general, true agents. 99% of the time, outside code, you need eyes and ears. we have only created a self-writing paper yet.

We may not have gotten fully-autonomous employees, but human employees using AI are doing way more than they could before, both in depth and scale.

Claude Code is basically a full-time "employee" on my (profitable) open source projects, but it's still a tool I use to do all the work. Claude Code is basically a full-time "employee" at my job, but it's still a tool I use to do all the work. My workload has shifted to high-level design decisions instead of writing the code, which is kind of exactly what would have happened if AI "joined the workforce" and I had a bunch of new hires under me.

I do recognize this article is largely targeted at non-dev workforces though, where it _largely_ holds up but most of my friends outside of the tech world have either gotten new jobs thanks to increased capability through AI or have severely integrated AI into whatever workflows they're doing at work (again, as a tool) and are excelling compared to employees who don't utilize AI.

So well put.

LLMs are useful for a great many things. It's just that being the best new product of the recent years, maybe even defining a decade doesn't cut it. It has to be the century-defining, world-ending, FOMO-inducing massive thing to put Skynet to shame and justify investments in trillion dollars. It's either AI joining the workforce soon, or Nvidia and OpenAI aren't that valuable.

I guess it manages to maximize shareholder value, and make AI feel like a disappointment.

After posting that, I came across numerous papers which critique Frey & Osborne’s approach, who are some of the forefathers for the AI job losses figures we see banded around commonly these days. One such paper is here but i can dig out others: https://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_...

It has made me very cautious around bold statements on AI - and I was already at the cautious end.

Agents as LLMs calling tools in a loop to perform tasks that can be handled by typing commands into a computer absolutely did.

Claude Code turns out to be misnamed: it's useful for way more than just writing code, once you figure out how to give it access to tools for other purposes.

I think the browser agents (like the horribly named "ChatGPT Agent" - way to burn a key namespace on a tech demo!) have acted as a distraction from this. Clicking links is still pretty hard. Running Bash commands on the other hand is practically a solved problem.

Very few people do

so neither Altman, the many CEOs industry wide, Engineering Managers, Software Engineers, “Forward Deployed Engineers” have to actually inspect

their demos show good looking output

its just the people in support roles that have to be like “wait a minute, this is very inconsistent”

all while everyone is doing their best not to get replaced

its clanker discrimination and mixed with clanker incompetence

'Sequences' is self explanatory. 'Static' is interesting, it means that the future state of the sequence is not dependent on your current action. As opposed to dynamical, which means the next state is a function of your current action/prediction, and your next action is a function of that resulting state and so on and so on.

An example of a dynamical system is easy, the stock market. If you sell 100 dollars of nvidia stock right now, it would have no effect on the market, but if you sold 50 billion dollars of nvidia stock, the market will definitely react, at least a little. You current action can change the future state of the system.

An example of a static system the way LLMs are trained on sentence pairs where you could have the sentence pair "i went to the bank. Swimming in the river was alot of fun" ans you give the LLM the first sentence and ask it to tell you what type of bank the sentect is referring to. If the LLM incorrectly says financial bank, the second sentence doesnt magically change to talk about a cashing a check, it stays the exact same. It stays the same whther the LLM guesses right or wrong. You just tell it whether its right or wrong

If your not scared of equations, a static system is y = f(x), and a dynamical system is a_1 = g(s_1), s_2 = h(a_1) and so on.

Static systems are modelled by supervised/self-supervised learning, dynamical systems are modelled by reinforcement learning. LLMs at their core are trained in a self supervised way (the RLHF is afterwards, not on the core of the foundation model) so they cant model a situation where their actions can have consequences that they have to plan for. They dont model the idea of planning at all. Thats why they can write pretty well. And can even pretend to have furhter capabilities, but they fall short of actually being able to do things because that requires planning and understanding the dynamical nature of the real world

Training an LLM or any modality of foundation model in a dynamical way would be the biggest improvement in AI since the attention mechanism

But this is perhaps not the case. By pesimistic estimates half of the people work in bs jobs that have no real value to society, and every capitalist is focused on rent extraction now. If the economy can operate under such conditions, it doesn't really need more productivity growth, it is already demand-limited.

Humans are good at this because they are truly multi-modal and can interact through many different channels to gather additional context to do the requisite task at hand. Given incomplete requirements or specs, they can talk to co-workers, look up old documents from a previous release, send a Slack or Teams message, setup a Zoom meeting with stakeholders, call customers, research competitors, buy a competitors product and try it out while taking notes of where it falls short, make a physical site visit to see the context in which the software is to be used and environmental considerations for operation.

Point is that humans doing work have all sorts of ways to gather and compile more context before acting or while they are acting that an LLM does not and in some cases cannot have without the assistance of a human. This process in the real world can unfurl over days or weeks or in response to new inputs and our expectation of how LLMs work doesn't align with this.

LLMs can sort of do this, but more often than not, the failure of LLMs is that we are still very bad at providing proper and sufficient context to the LLM and the LLMs are not very good at requesting more context or reacting to new context, changing plans, changing directions, etc. We also have different expectations of LLMs and we don't expect the LLM to ask "Can you provide a layout and photo of where the machine will be set up and the typical operating conditions?" and then wait a few days for us to gather that context for it before continuing.

I don't know what else LLMs need to do? get on the payroll? People are using them heavily. You can't even google things easily without triggering an LLM response.

I think the current millenial and older generation is too used to the pre-LLM way of things, so the resistance will be there for a long time to come. but kids doing homeworks with LLMs will rely on them heavily once they're in the work force.

I don't know how people are not as fascinated and excited about this. I keep watching older scifi content, and LLMs are now doing for us what "futuristic computer persona" did in older scifi.

Easy example: You no longer need copywriters because of LLMs. You had spell/grammar checkers before, but they didn't "understand" context and recommend different phrasing, and check for things like continuity and rambling on.

My AI usage exploded in 2025, but a lot of this stuff still requires a decent amount of technical know-how to set up and operate effectively. It also costs money, or requires hardware that many don't possess.

Like using AI to fill in a form. Instead of a proper autonomous agent (which was the project's goal).

Now that AI is "mainstream" and big bubble big money big career/promotion options for management I expect much more of this behaviour going into 2026.

Man dude, don't automate toil add an API to the website.It's supposed to have one!

There are plenty of jobs that have already been pretty much replaced by AI: certain forms of journalism, low-end photoshop work, logo generation, copywriting. What does the OP need to see in order to believe that AI has "joined the workforce"?

If you want to focus on what AI agents are actually capable of today, the last person I'd pay any attention to is Marcus, who has been wrong about nearly everything related to AI for years, and does nothing but double down.

There's a whole lot of bullshit jobs and work that will get increasingly and opaquely automated by AI. You won't see jobs go away unless or until organizations deliberately set out to reduce staff. People will use AI throughout the course of their days to get a couple of "hours" of tasks done in a few minutes, here and there, throughout the week. I've already seen reports and projects and writing that clearly comes from AI in my own workplace. Right now, very few people know how to recognize and assess the difference between human and AI output, and even fewer how to calibrate work assignments.

Spreadsheet AIs are fantastic, reports and charting have just hit their stride, and a whole lot of people are going to appear to be very productive without putting a whole lot of effort into it. And then one day, when sufficiently knowledgable and aware people make it into management, all sorts of jobs are going to go quietly away, until everything is automated, because it doesn't make sense to pay a human 6 figures what an AI can do for 3 figures in a year.

I'd love to see every manager in the world start charting the Pareto curves for their workplaces, in alongside actual hours worked per employee - work output is going to be very wonky, and the lazy, clever, and ambitious people are all going to be using AI very heavily.

Similar to this guy: https://news.ycombinator.com/item?id=11850241

https://www.reddit.com/r/BestofRedditorUpdates/comments/tm8m...

Part of the problem is that people don't know how to measure work effectively to begin with, let alone in the context of AI chatbots that can effectively do better work than anyone a significant portion of the adult population of the planet.

The teams that fully embrace it, use the tools openly and transparently, and are able to effectively contrast good and poor use of the tools, will take off.

yes, 100%

I think that way too often, discussions of the current state of tech get derailed by talking about predictions of future improvements.

hypothetical thought experiment:

I set a New Year's resolution for myself of drinking less alcohol.

on New Year's Eve, I get pulled over for driving drunk.

the officer wants to give me a sobriety test. I respond that I have projected my alcohol consumption will have decreased 80% YoY by Q2 2026.

the officer is going to smile and nod...and then insist on giving me the sobriety test.

compare this with a non-hypothetical anecdote:

I was talking with a friend about the environmental impacts of AI, and mentioned the methane turbines in Memphis [0] that are being used to power Elon Musk's MechaHitler slash CSAM generator.

the friend says "oh, but they're working on building nuclear power plants for AI datacenters".

and that's technically true...but it misses the broader point.

if someone lives downwind of that data center, and they have a kid who develops asthma, you can try to tell them "oh in 5 years it'll be nuclear powered". and your prediction might be correct...but their kid still has asthma.

0: https://time.com/7308925/elon-musk-memphis-ai-data-center/

Sources? What, but, you are not a journalist, you are not suppose to challenge what I say, I’m a CEO! No I’m not just using media to create artificial hype to pull investors and make money on bullshit that is never gonna work! How can you say that! It’s a real thing, trust me bro!

```

    To find out more about why 2025 failed to become the Year of the AI Agent, I recommend reading  my full New Yorker piece .

so essentially, just go and read the new-yorker piece here: https://archive.ph/VQ1fT

I think Carmack is right, LLM's are not the route to AGI.

1. Punch cards -> Assembly languages

2. Assembly languages -> Compiled languages

3. Compiled languages -> Interpreted languages

4. Interpreted languages -> Agentic LLM prompting

I've tried the latest and greatest agentic CLI and toolings with the public SOTA models.

I think this is a productivity jump equivalent to maybe punch cards -> compiled languages, and that's it. Something like a 40% increase, but nowhere close to exponential.

If my manager said to me tomorrow: "I have to either get rid of one of your coworkers or your use of AI tools, which is it?"

I would, without any hesitation, ask that he fire one of my coworkers. Gemini / Claude is way more useful to me than any particular coworker.

And now I'm preparing for my post-software career because that coworker is going to be me in a few years.

Obviously I hope that I'm wrong, but I don't think I am.

It’s also kind of stupid to hand wave away, programming. Programmers are where all the early adopters of software are. He’s merely conflating an adoption curve with capabilities. Programmers, I’m sure, were also the first to use Google and smartphones. “It doesn’t work for me” is missing the critical word “yet” at the end, and really, is it saying much that forecasts about adoption in the metric, “years until when Cal Newport’s arbitrary criteria of what agent and adoption means meets some threshold only inside Cal Newport’s head” is hard to do?

There are 700m active weeklies for ChatGPT. It has joined the workforce! It just isn’t being paid the salaries.