There's no "end" per se, but shifting dynamics. I personally think we are currently seeing a "slow", but noteworthy-in-hindsight, shift in interest and development of alternative options and platforms from the big tech monopolies. From things like Bluesky, Framework laptops, minimalist cell phones, and even smaller local language models and other types of useful local ML models.
I don't think it's going to be anything like 50% or even 30% of users using non-flagship hardware or software products. But it could still be significant. And I think the more important thing isn't going to be market share as much as proof of viability. More successful examples will beget more.
It's about planting seeds from which future digital ecosystems can grow -- that have interoperability, functionality, and openness built into their foundations.
I believe that what drove you to make this post and the way I feel is not unique and are part of a larger swell in similar sentiments.
You throw in other factors too like the mass tech layoffs and the continued doubling down of tech barons on their cravings to intermingle with the surveillance state and military industrial complex... I just can't see how the future doesn't have more people disillusioned with the current state of the tech industry.
I think big tech will continue to overplay their hand and the mess that comes after will be an opportunity to give people what they want and show alternatives to what's already been done that we know won't work out.
Yup, it is. It's my bread and butter too. So much so I decided to just do it for myself and start my own consulting company.
Being a solutions engineer at the right companies means you get to be one of the few people with full end-to-end visibility of the entire lifecycle of both a client and the technology adoption, deployment, optimization, maintenance, etc. process. And you'll get to see it dozens or hundreds of times for a variety of clients across industries. Again though, totally depends on the company.
In my experience, it just entirely depends on the company. Different companies will use the same title and they can have wildly different mixtures of pre vs post sales involvement. My career has all been customer/client facing technical roles. Titles range from:
- support engineer
- solutions engineer
- sales engineer
- applied engineer
- forward deployed engineer
- solutions / sales architect
- field engineer
And that's leaving out titles that avoid calling someone an engineer who is still entirely technical, has to code, has to deploy, etc. but deals with clients.
I will say though that roles that want pre-sales focused engineers typically are pretty picky about people who have the sales-facing experience. So it shouldn't be too hard to avoid those roles if you're wanting a role focused almost entirely on post-sales.
(I say that, but I do know that if a company lacks pre-sales dedicated engs then other engs definitely can get roped into it. I know a guy with a PhD in ChemEng that basically is the director of research at his company and has had to wear a "sales eng" hat quite a bit in his role.)
We'll see. The first two things that they said would move from "emerging tech" to "currently exists" by April 2026 are:
- "Someone you know has an AI boyfriend"
- "Generalist agent AIs that can function as a personal secretary"
I'd be curious how many people know someone that is sincerely in a relationship with an AI.
And also I'd love to know anyone that has honestly replaced their human assistant / secretary with an AI agent. I have an assistant, they're much more valuable beyond rote input-output tasks... Also I encourage my assistant to use LLMs when they can be useful like for supplementing research tasks.
Fundamentally though, I just don't think any AI agents I've seen can legitimately function as a personal secretary.
Also they said by April 2026:
> 22,000 Reliable Agent copies thinking at 13x human speed
And when moving from "Dec 2025" to "Apr 2026" they switch "Unreliable Agent" to "Reliable Agent". So again, we'll see. I'm very doubtful given the whole OpenClaw mess. Nothing about that says "two months away from reliable".
There are plenty of companies that sell an AI assistant that answers the phone as a service, they just aren't named OpenAI or Anthropic. They'll let callers book an appointment onto your calendar, even!
No, there are companies that sell voice activated phone trees, but no one is getting results out of unstructured, arbitrary phone call answering with actions taken by an LLM.
I'm sure there are research demos in big companies, I'm sure some AI bro has done this with the Twilio API, but no one is seriously doing this.
All it takes is one "can you take this to the post office", the simplest, of requests, and you're in a dead end of at best refusal, but more likely role-play.
Agreed that “unstructured arbitrary phone calls + arbitrary actions” is where things go to die.
What does work in production (at least for SMB/customer-support style calls) is making the problem less magical:
1) narrow domain + explicit capabilities (book/reschedule/cancel, take a message, basic FAQs)
2) strict tool whitelist + typed schemas + confirmations for side effects
3) robust out-of-scope detection + graceful handoff (“I can’t do that, but I can X/Y/Z”)
4) real logs + eval/test harnesses so regressions get caught
Once you do that, you can get genuinely useful outcomes without the role-play traps you’re describing.
We’ve been building this at eboo.ai (voice agents for businesses). If you’re curious, happy to share the guardrails/eval setup we’ve found most effective.
It's important to remember though (this is besides the point for what you're saying) that job displacement of things like secretaries from AI do not require it to be a near perfect replacement. There are many other factors (for example if it's much cheaper and can do part of the work it can dramatically shrink demand as people can shift to an imperfect replacement in AI)
I think they immediately corrected their median timelines for takeoff to 2028 upon releasing the article (I believe there was a math mistake or something initially), so all those dates can probably be bumped back a few months. Regardless, the trend seems fairly on track.
People have been in love with machines for a long time. It's just that the machines didn't talk back so we didn't grant them the "partner" status. Wait for car+LLM and you'll have a killer combo.
I have not see any reporting or evidence at all that Anthropic or OpenAI is able to make money on inference yet.
> Turns out there was a lot of low-hanging fruit in terms of inference optimization that hadn't been plucked yet.
That does not mean the frontier labs are pricing their APIs to cover their costs yet.
It can both be true that it has gotten cheaper for them to provide inference and that they still are subsidizing inference costs.
In fact, I'd argue that's way more likely given that has been precisely the goto strategy for highly-competitive startups for awhile now. Price low to pump adoption and dominate the market, worry about raising prices for financial sustainability later, burn through investor money until then.
What no one outside of these frontier labs knows right now is how big the gap is between current pricing and eventual pricing.
It's quite clear that these companies do make money on each marginal token. They've said this directly and analysts agree [1]. It's less clear that the margins are high enough to pay off the up-front cost of training each model.
It’s not clear at all because model training upfront costs and how you depreciate them are big unknowns, even for deprecated models. See my last comment for a bit more detail.
They are obviously losing money on training. I think they are selling inference for less than what it costs to serve these tokens.
That really matters. If they are making a margin on inference they could conceivably break even no matter how expensive training is, provided they sign up enough paying customers.
If they lose money on every paying customer then building great products that customers want to pay for them will just make their financial situation worse.
> They've said this directly and analysts agree [1]
chasing down a few sources in that article leads to articles like this at the root of claims[1], which is entirely based on information "according to a person with knowledge of the company’s financials", which doesn't exactly fill me with confidence.
"according to a person with knowledge of the company’s financials" is how professional journalists tell you that someone who they judge to be credible has leaked information to them.
But there are companies which are only serving open weight models via APIs (ie. they are not doing any training), so they must be profitable? here's one list of providers from OpenRouter serving LLama 3.3 70B: https://openrouter.ai/meta-llama/llama-3.3-70b-instruct/prov...
It's also true that their inference costs are being heavily subsidized. For example, if you calculate Oracles debt into OpenAIs revenue, they would be incredibly far underwater on inference.
Sue, but if they stop training new models, the current models will be useless in a few years as our knowledge base evolves. They need to continually train new models to have a useful product.
They are for sure subsidising costs on all you can prompt packages (20-100-200$ /mo). They do that for data gathering mostly, and at a smaller degree for user retention.
> evidence at all that Anthropic or OpenAI is able to make money on inference yet.
You can infer that from what 3rd party inference providers are charging. The largest open models atm are dsv3 (~650B params) and kimi2.5 (1.2T params). They are being served at 2-2.5-3$ /Mtok. That's sonnet / gpt-mini / gemini3-flash price range. You can make some educates guesses that they get some leeway for model size at the 10-15$/ Mtok prices for their top tier models. So if they are inside some sane model sizes, they are likely making money off of token based APIs.
> They are being served at 2-2.5-3$ /Mtok. That's sonnet / gpt-mini / gemini3-flash price range.
The interesting number is usually input tokens, not output, because there's much more of the former in any long-running session (like say coding agents) since all outputs become inputs for the next iteration, and you also have tool calls adding a lot of additional input tokens etc.
It doesn't change your conclusion much though. Kimi K2.5 has almost the same input token pricing as Gemini 3 Flash.
Ive been thinking about our company, one of big global conglomerates that went for copilot. Suddenly I was just enrolled.. together with at least 1500 others. I guess the amount of money for our business copilot plans x 1500 is not a huge amount of money, but I am at least pretty convinced that only a small part of users use even 10% of their quota. Even teams located around me, I only know of 1 person that seems to use it actively.
> I have not see any reporting or evidence at all that Anthropic or OpenAI is able to make money on inference yet.
Anthropic planning an IPO this year is a broad meta-indicator that internally they believe they'll be able to reach break-even sometime next year on delivering a competitive model. Of course, their belief could turn out to be wrong but it doesn't make much sense to do an IPO if you don't think you're close. Assuming you have a choice with other options to raise private capital (which still seems true), it would be better to defer an IPO until you expect quarterly numbers to reach break-even or at least close to it.
Despite the willingness of private investment to fund hugely negative AI spend, the recently growing twitchiness of public markets around AI ecosystem stocks indicates they're already worried prices have exceeded near-term value. It doesn't seem like they're in a mood to fund oceans of dotcom-like red ink for long.
>Despite the willingness of private investment to fund hugely negative AI spend
VC firms, even ones the size of Softbank, also literally just don't have enough capital to fund the planned next-generation gigawatt-scale data centers.
Yea, the more things change the more they stay the same. This latest AI hype cycle seems to be no different. Which I think will become more widely accepted over the next couple of years as creating deployable, production-ready, maintainable, sellable, profitable software remains difficult for all the reasons besides the hands-to-keyboard writing of code.
I did transcription for a while in 2021. It is absurdly hard. Especially as these days humans only get the difficult jobs that AI has already taken a stab at.
The hardest one I did was for a sports network where it was a motorcross motorbike event where most of what you could hear was the roar of the bikes. There were two commentators I had to transcribe over the top of that mess and they were using the slang insider nicknames for all the riders, not their published names, so I had to sit and Google forums to find the names of the riders while I was listening. I'm not even sure how these local models would even be able to handle that insanity at all because they almost certainly lack enough domain knowledge.
I was skepitcal upon hearing the figure but various sources do indeed back it up and [0] is a pretty interesting paper (old but still relevant human transcibers haven't changed in accuracy).
I think it's actually hard to verify how correct a transcription is, at scale. Curious where those error rate numbers come from, because they should test it on people actually doing their job.
You missed a giant factor: domain knowledge. Transcribing something outside of your knowledge realm is very hard. I posted above about transcribing the commentary of a motorbike race where the commentators only used the slang names of the riders.
I don't think it's going to be anything like 50% or even 30% of users using non-flagship hardware or software products. But it could still be significant. And I think the more important thing isn't going to be market share as much as proof of viability. More successful examples will beget more.
It's about planting seeds from which future digital ecosystems can grow -- that have interoperability, functionality, and openness built into their foundations.
I believe that what drove you to make this post and the way I feel is not unique and are part of a larger swell in similar sentiments.
You throw in other factors too like the mass tech layoffs and the continued doubling down of tech barons on their cravings to intermingle with the surveillance state and military industrial complex... I just can't see how the future doesn't have more people disillusioned with the current state of the tech industry.
I think big tech will continue to overplay their hand and the mess that comes after will be an opportunity to give people what they want and show alternatives to what's already been done that we know won't work out.
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