The AI Speedometer — per-expert placement classifications
deslop.media records what an expert said on a dated question and classifies their stance (Hold / Deny / Silent). It does not assert the statement is true and does not forecast. Read every placement as what these experts have said, not what is true.
Query the live panel through the deslop.media MCP connector — find experts by stance (find_experts), query a named expert's recorded positions (query_expert), or check coverage on a question (check_coverage). A reading of recorded positions, not a verdict on whether any statement is true, and not a forecast.
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Will AI keep getting more capable through mid-2028, instead of plateauing?
I don't think that GI is achievable, at least not in the next five to ten years. I don't see any of these models reasoning at the level of humans for now.
stance classification (not a truth verdict)
deslop.media classifies this dated statement as a Weak Deny on the question: Will AI keep getting more capable through mid-2028, instead of plateauing? This is a reading of the expert's position, not a verdict on whether the statement is true.
Independently retrievable: the quote is verifiable at a fetchable public source.
Will each new model generation still beat the last on real-world tasks?
deslop.media classifies this dated statement as a Weak Deny on the question: Will each new model generation still beat the last on real-world tasks? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will AI get better at hard, novel, expert-level tasks, not just benchmarks?
deslop.media classifies this dated statement as a Strong Deny on the question: Will AI get better at hard, novel, expert-level tasks, not just benchmarks? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will AI agents complete real workflows well enough for production use?
I think that's just a losing proposition
deslop.media classifies this dated statement as a Strong Deny on the question: Will AI agents complete real workflows well enough for production use? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will the AI infrastructure build-out keep scaling through mid-2028?
I think people are reading too much into Stargate, because even before it was announced the planned investments in the AI sector were massive, and they are going to continue that way, which is all part of the hype.
deslop.media classifies this dated statement as a Weak Hold on the question: Will the AI infrastructure build-out keep scaling through mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will the money keep funding the build-out without a pullback?
deslop.media classifies this dated statement as a Weak Hold on the question: Will the money keep funding the build-out without a pullback? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will customers keep paying more for AI, and will that revenue stick?
While I don't know the answer to that right now, what I can say is that the industry itself is wasting a lot of money. It has put close to a trillion dollars into the hardware, without seeing any of the breakthroughs that people were hoping for.
deslop.media classifies this dated statement as a Weak Deny on the question: Will customers keep paying more for AI, and will that revenue stick? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will the application layer book real revenue from AI, not just infrastructure?
We have not seen the development of apps based on AI that have the same usability
deslop.media classifies this dated statement as a Weak Deny on the question: Will the application layer book real revenue from AI, not just infrastructure? This is a reading of the expert's position, not a verdict on whether the statement is true.
How far does AI's eventual market reach — a feature, software, services, or a share of labor?
I don't expect any occupation that we have today to have been eliminated in five or 10 years' time.
deslop.media classifies this dated statement as a Strong Deny on the question: How far does AI's eventual market reach — a feature, software, services, or a share of labor? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will the job market absorb AI without broad employment, wage, or social stress by mid-2028?
What's more, the tasks that AI is set to replace are much more geographically and occupationally dispersed, so you won't have a specific group that gets decimated.
deslop.media classifies this dated statement as a Weak Hold on the question: Will the job market absorb AI without broad employment, wage, or social stress by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will the economy keep adding more jobs than it loses by mid-2028?
deslop.media classifies this dated statement as a Weak Hold on the question: Will the economy keep adding more jobs than it loses by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will AI create enough new work to offset the tasks it replaces by mid-2028?
I don't think complementary uses of AI will miraculously appear by themselves unless the industry devotes significant energy and time to them
deslop.media classifies this dated statement as a Weak Deny on the question: Will AI create enough new work to offset the tasks it replaces by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence
deslop.media classifies this dated statement as a Strong Hold on the question: Will AI keep getting more capable through mid-2028, instead of plateauing? This is a reading of the expert's position, not a verdict on whether the statement is true.
We have recently built systems that are smarter than people in many ways
deslop.media classifies this dated statement as a Strong Hold on the question: Will each new model generation still beat the last on real-world tasks? This is a reading of the expert's position, not a verdict on whether the statement is true.
2025 has seen the arrival of agents that can do real cognitive work; writing computer code will never be the same.
deslop.media classifies this dated statement as a Strong Hold on the question: Will AI get better at hard, novel, expert-level tasks, not just benchmarks? This is a reading of the expert's position, not a verdict on whether the statement is true.
deslop.media classifies this dated statement as a Strong Hold on the question: Will AI agents complete real workflows well enough for production use? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will reliability improve enough to stop blocking wider deployment?
We are now confident we know how to build AGI as we have traditionally understood it.
deslop.media classifies this dated statement as a Weak Hold on the question: Will reliability improve enough to stop blocking wider deployment? This is a reading of the expert's position, not a verdict on whether the statement is true.
AI can only fulfill its promise if we build the compute to power it.
deslop.media classifies this dated statement as a Strong Hold on the question: Will the AI infrastructure build-out keep scaling through mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will enough power and grid arrive to feed the data centers?
I can't think of anything more important than energy chips and all the other infrastructure.
deslop.media classifies this dated statement as a Weak Hold on the question: Will enough power and grid arrive to feed the data centers? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will the chip supply chain deliver enough accelerators at workable cost?
deslop.media classifies this dated statement as a Weak Hold on the question: Will the chip supply chain deliver enough accelerators at workable cost? This is a reading of the expert's position, not a verdict on whether the statement is true.
looking at commitments of about $1.4 trillion over the next eight years
deslop.media classifies this dated statement as a Strong Hold on the question: Will the money keep funding the build-out without a pullback? This is a reading of the expert's position, not a verdict on whether the statement is true.
We expect to end this year above $20 billion in annualized revenue run rate and grow to hundreds of billion by 2030.
deslop.media classifies this dated statement as a Strong Hold on the question: Will customers keep paying more for AI, and will that revenue stick? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will the big cloud providers book rapidly growing AI revenue?
the API grew faster for us this year than even ChatGPT
deslop.media classifies this dated statement as a Weak Hold on the question: Will the big cloud providers book rapidly growing AI revenue? This is a reading of the expert's position, not a verdict on whether the statement is true.
A real public source exists but is not independently fetchable right now (host AI-bot-blocking or snapshot-only). Kept, not dropped; treat as lower-confidence pending re-resolution.
Will frontier labs grow paid usage fast enough to show real demand?
deslop.media classifies this dated statement as a Strong Hold on the question: Will frontier labs grow paid usage fast enough to show real demand? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will enterprises renew and embed AI rather than stall in pilots?
We have more than a million enterprise users, but we have absolutely rapid adoption of the API.
deslop.media classifies this dated statement as a Weak Hold on the question: Will enterprises renew and embed AI rather than stall in pilots? This is a reading of the expert's position, not a verdict on whether the statement is true.
I can easily imagine a world where 30 to 40% of the tasks that happen in the economy today get done by AI in the not very distant future
deslop.media classifies this dated statement as a Strong Hold on the question: How far does AI's eventual market reach — a feature, software, services, or a share of labor? This is a reading of the expert's position, not a verdict on whether the statement is true.
I'm delighted to be wrong about this, I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened.
There will be very hard parts like whole classes of jobs going away, but on the other hand the world will be getting so much richer so quickly that we'll be able to seriously entertain new policy ideas we never could before.
deslop.media classifies this dated statement as a Weak Hold on the question: Will AI create enough new work to offset the tasks it replaces by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will governments mostly leave AI alone through mid-2028?
I think some policy is good. I think it is easy for it to go too far.
deslop.media classifies this dated statement as a Weak Hold on the question: Will governments mostly leave AI alone through mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will moratoria, licensing, or training-pause rules bind frontier development?
deslop.media classifies this dated statement as a Weak Deny on the question: Will moratoria, licensing, or training-pause rules bind frontier development? This is a reading of the expert's position, not a verdict on whether the statement is true.
Broadly speaking, the exponential of the underlying technology has gone about as I expected it to go.
We're seeing the same scaling in RL that we saw for pre-training.
in twelve months, we may be in a world where AI is writing essentially all of the code
We may get to the point in a year or two where the models can just do SWE end-to-end. That's a whole task.
deslop.media classifies this dated statement as a Weak Hold on the question: Will AI agents complete real workflows well enough for production use? This is a reading of the expert's position, not a verdict on whether the statement is true.
Everyone's always looking for these hard blocks on what [AI] can do. They're nowhere to be seen.
The amount of compute the industry is building this year is probably, call it, 10-15 gigawatts. It goes up by roughly 3x a year. So next year's 30-40 gigawatts. 2028 might be 100 gigawatts.
We had planned for a world of 10x growth per year. In the first quarter of this year, if you were to annualize it, we saw 80x growth per year — in revenue and usage.
deslop.media classifies this dated statement as a Weak Hold on the question: Will customers keep paying more for AI, and will that revenue stick? This is a reading of the expert's position, not a verdict on whether the statement is true.
In 2023, it was zero to $100 million. In 2024, it was $100 million to $1 billion. In 2025, it was $1 billion to $9-10 billion.
Will AI providers hold healthy margins as costs and pricing pressure mount?
deslop.media classifies this dated statement as a Weak Hold on the question: Will AI providers hold healthy margins as costs and pricing pressure mount? This is a reading of the expert's position, not a verdict on whether the statement is true.
AI may also act as a more general economic substitute for human cognitive abilities than previous technologies have
We, as the producers of this technology, have a duty and an obligation to be honest about what is coming.
deslop.media classifies this dated statement as a Weak Deny on the question: Will the job market absorb AI without broad employment, wage, or social stress by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
If you automate 90% of the job, then everyone does the 10% of the job. And the 10% kind of expands to be 100%.
Financing for the Abilene project includes $9.6B of debt from JPMorgan and $5B of equity from Crusoe and Blue Owl
Sacra co-founder, private-markets research
ARR hit $106M in June 2025, up 6.6x from $16M at the end of 2024
OpenAI has committed to over $500B in disclosed cloud capacity across multiple providers.
OpenAI hit $25B in annualized revenue in February 2026, up from $20B at the end of 2025.
Enterprise has become a meaningful share of the mix, representing more than 40% of revenue.
OpenAI posted a 33% gross margin, constrained by inference costs that reached $8.4B in 2025.
deslop.media classifies this dated statement as a Weak Deny on the question: Will AI providers hold healthy margins as costs and pricing pressure mount? This is a reading of the expert's position, not a verdict on whether the statement is true.
Is AI spending mostly cannibalizing existing budgets rather than adding new spend?
Ground zero from the shift in SaaS from per-seat pricing ($50-150/seat/month) to outcome-based pricing (~$0.99-$1.50/resolution)
deslop.media classifies this dated statement as a Weak Hold on the question: Is AI spending mostly cannibalizing existing budgets rather than adding new spend? This is a reading of the expert's position, not a verdict on whether the statement is true.
Going from $1M ARR to $100M ARR in 12 months, Cursor is the fastest growing SaaS company of all time
deslop.media classifies this dated statement as a Strong Hold on the question: Will the application layer book real revenue from AI, not just infrastructure? This is a reading of the expert's position, not a verdict on whether the statement is true.
In companies, such AI agents could cut down the need for junior administrative work or customer support roles, effectively shifting spending from human labor to AI services.
deslop.media classifies this dated statement as a Weak Hold on the question: How far does AI's eventual market reach — a feature, software, services, or a share of labor? This is a reading of the expert's position, not a verdict on whether the statement is true.
And I think automation, not only is it overrated, it's also over-promised. It doesn't usually happen nearly as fast as the people designing the tools think that it will.
deslop.media classifies this dated statement as a Weak Deny on the question: Will AI agents complete real workflows well enough for production use? This is a reading of the expert's position, not a verdict on whether the statement is true.
The unique opportunity that AI offers to the labor market is to extend the relevance, reach, and value of human expertise.
But even more fundamentally, let's say a million jobs are destroyed, and a million are created. The people who are taking the new work are not usually the people displaced from the old work.
The concern is not about us running out of jobs per se. In fact, we're running out of workers.
Will permitting, grid, or co-location rules slow the buildout?
Today no gigawatt-scale data centers exist; by the end of 2026 American companies will control nearly half a dozen such facilities (in addition to multi-hundred-megawatt facilities coming online throughout the year as well).
deslop.media classifies this dated statement as a Weak Deny on the question: Will permitting, grid, or co-location rules slow the buildout? This is a reading of the expert's position, not a verdict on whether the statement is true.
I do not currently expect California to pass any new frontier AI legislation in the coming year, but they are very likely to pass kids safety and other consumer protection laws that will affect frontier AI systems nonetheless.
Will launch or workplace review rules slow AI's deployment?
deslop.media classifies this dated statement as a Weak Hold on the question: Will launch or workplace review rules slow AI's deployment? This is a reading of the expert's position, not a verdict on whether the statement is true.
AI agents, Beer says, will change the way one thinks about work, the tasks to complete that work, how to break those tasks down, the tasks the bank is comfortable automating, the tasks that require human reflection, and then the proper technology ecosystem with the proper security, resiliency, and controls.
deslop.media classifies this dated statement as a Weak Hold on the question: Will AI keep getting more capable through mid-2028, instead of plateauing? This is a reading of the expert's position, not a verdict on whether the statement is true.
We have been focusing very early on, on simple things, like what's the right level to create an agent; how do you give them identity and access?
The investments we're making in tech are creating real value for the firm.
Success is transforming and creating value with AI, not just incremental steps.
We've grown fast, but now we're optimizing what we've got.
We now have over 9,500 paid Agentforce deals and 3.2 trillion tokens processed, underscoring our leadership in building the Agentic Enterprise and driving real outcomes.
AI is doing 30% to 50% of the work at Salesforce
Our Agentforce and Data 360 products are the momentum drivers, hitting nearly $1.4 billion in ARR—an explosive 114% year-over-year gain.
Agentforce accounts in production grew 70% quarter over quarter
deslop.media classifies this dated statement as a Strong Hold on the question: Will enterprises renew and embed AI rather than stall in pilots? This is a reading of the expert's position, not a verdict on whether the statement is true.
This looks like it's anywhere from a few trillion to 12 trillion
deslop.media classifies this dated statement as a Weak Deny on the question: Is AI spending mostly cannibalizing existing budgets rather than adding new spend? This is a reading of the expert's position, not a verdict on whether the statement is true.
it's not going to be some huge mass layoff of white-collar workers, it is a radical augmentation of the workforce
deslop.media classifies this dated statement as a Strong Hold on the question: Will AI create enough new work to offset the tasks it replaces by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
Looking backwards, we have been tripling our compute every single year for the past couple years, and we've also tripled our revenue.
we have line of sight to much better models coming this year
he gives it a design doc, it actually implements it, adds metrics, observability, runs the profiler, improves it
you become this CEO of a fleet of hundreds of thousands of agents that are completing your objectives
Certainly within a year, sometimes much sooner, it's going to be incredibly reliable
Stargate is a step towards meeting the demand of the compute-powered economy
we are not even going to have enough compute to fund those two things
OpenAI to Spend $50 Billion on Computing in 2026, Brockman Says
we have been tripling our compute every single year for the past couple years, and we've also tripled our revenue.
intent can now be converted directly into software, spreadsheets, presentations, workflows, science and companies
deslop.media classifies this dated statement as a Weak Hold on the question: Will the application layer book real revenue from AI, not just infrastructure? This is a reading of the expert's position, not a verdict on whether the statement is true.
Less than 1 year ago frontier AI models were at IMO gold-level performance. I expect this pace of progress to continue.
We developed new techniques that make LLMs a lot better at hard-to-verify tasks. IMO problems were the perfect challenge for this: proofs are pages long and take experts hours to grade.
I would say basically anything that I would normally try to code up, I try to do it with Codex first.
I think that O3 is not where the technology will be in six months.
Recent discussion coincides with rapid improvements in AI capabilities and adoption. From 2023 to 2024, AI systems improved from solving 4.4% of coding problems to 71.7% on SWE-Bench, a widely used benchmark for software engineering
AI has also improved in areas like language understanding, subject knowledge, and reasoning. A recent paper found that current systems could match or outperform up to 47 percent of industry professionals on a pre-defined benchmark of economically valuable tasks
deslop.media classifies this dated statement as a Weak Hold on the question: Will each new model generation still beat the last on real-world tasks? This is a reading of the expert's position, not a verdict on whether the statement is true.
deslop.media classifies this dated statement as a Weak Hold on the question: Will AI get better at hard, novel, expert-level tasks, not just benchmarks? This is a reading of the expert's position, not a verdict on whether the statement is true.
Early-career workers (ages 22-25) in AI-exposed occupations experienced 16% relative employment declines, controlling for firm-level shocks, while employment for experienced workers remained stable.
Our second key fact is that overall employment continues to grow robustly, but employment growth for young workers has been stagnant since late 2022.
2026 will either be the year where a lot of this new capacity comes online...or it will be the year when many of these construction projects begin to face delays.
The concept of GW is being used to reflect that power is the new constraint on data centers, rather than money.
deslop.media classifies this dated statement as a Weak Deny on the question: Will enough power and grid arrive to feed the data centers? This is a reading of the expert's position, not a verdict on whether the statement is true.
the TSMC constraint could become material in 2026.
deslop.media classifies this dated statement as a Weak Deny on the question: Will the chip supply chain deliver enough accelerators at workable cost? This is a reading of the expert's position, not a verdict on whether the statement is true.
Labs are taking all their profits and capital and plowing them right back into new data centers.
The area where I do not expect to see any delays is in AI adoption itself. The fading of hype will have little impact on fundamentals.
ChatGPT has continued its epic rise to north of $12B in run-rate revenue, Anthropic has reached $5B+ in run-rate revenue in a meteoric rise, and there's a new club of companies scaling quickly from $0 to $100M in revenue.
Big enterprises are struggling to implement AI in-house, which is leading to fatigue and disappointment.
deslop.media classifies this dated statement as a Weak Deny on the question: Will enterprises renew and embed AI rather than stall in pilots? This is a reading of the expert's position, not a verdict on whether the statement is true.
AI app companies are also riding a compute cost curve that should drive incremental margin improvement, especially as new data centers come online between now and 2030.
If anything, the best startups are growing faster than ever from $0 to $100M in revenue.
Examples of improved capabilities are everywhere, including the first chart below combining performance scores across 10 different benchmarks.
J.P. Morgan Asset Management strategy chairman
Look how rapidly most models improved in 2025 in solving real world GitHub issues, particularly Google’s
OpenAI’s o3 achieved a rating better than 99% of human competitors in Codeforces coding competitions
the share of incorrect responses out of all questions asked; some of these hallucination rates are shockingly high
deslop.media classifies this dated statement as a Weak Deny on the question: Will reliability improve enough to stop blocking wider deployment? This is a reading of the expert's position, not a verdict on whether the statement is true.
This year looks to be another version of 2025: a 10%-15% correction at some point due to profit-taking and a growth scare, but then equity markets end the year higher than where they began.
Given combined cycle turbine delays, grid interconnection bottlenecks, wildly overly optimistic timetables for small modular reactors and nuclear fusion and limited opportunities to recommission shuttered but still viable nuclear fission plants, energy may be a significant constraint on AI
The market cap of four hyperscalers and the semiconductor ecosystem companies they rely on has grown from $3 trillion to $18 trillion in just a few years
Even after accounting for the Q4 surge in AI-related debt, many AI companies have net debt to cash flow ratios less than zero since cash and securities exceed total debt
Will all this investment eventually result in commensurate profits? If not, moat companies could face a version of the Metaverse reckoning that took place in 2022 when the Mag7 stocks fell in half.
Microsoft’s inference business: fastest line in the business history to reach a US$10 bn revenue run rate, with growth constrained by a lack of available compute capacity
Despite $30-$40 bn in enterprise investment in genAI, 95% of organizations are getting zero return
But at a market-wide level, so far only the AI infrastructure providers have generated substantial excess returns. Baskets of companies presumed to benefit from selling AI products and/or lower labor costs have been flat to the equal weighted S&P 500.
The killer application of long-horizon agents right now is places where you have this 'first draft' type of concept
at some point the models got good enough, and that's when we moved from scaffolds to harnesses
You throw hard problems at these things and you get long-horizon agents
They aren't reliable to nine nines of reliability, but they can do a ton of work
My prediction is that data center build out is gonna merge with environmental issues and local construction issues, and that's gonna dominate local, like when people think about AI fights in 2026, a big chunk of it is gonna be about stuff that is not really about AI. It's about construction and energy production.
deslop.media classifies this dated statement as a Weak Hold on the question: Will permitting, grid, or co-location rules slow the buildout? This is a reading of the expert's position, not a verdict on whether the statement is true.
Will antitrust action rein in a major AI company by mid-2028?
I think that for the biggest of the companies that Google is competing with in the AI space, these remedies won't be a big value add.
deslop.media classifies this dated statement as a Weak Deny on the question: Will antitrust action rein in a major AI company by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
The Grand Prize remains unclaimed, but nevertheless 2025 saw remarkable progress on LLM-driven refinement loops, both with "local" models and with commercial frontier models.
While the Grand Prize remains unclaimed, we're excited to announce the ARC Prize 2025 Score and Paper winners.
We verified a Gemini 3 Pro refinement harness implementation open-sourced by Poetiq, which improves performance on ARC-AGI-2 from a baseline of 31% accuracy at $0.81 per task to 54% accuracy at $31 per task.
There are agents today that are terrific. There are models today that are terrific. But in many cases, the data isn't ready to be agented yet. So we're putting agents on top of data that isn't ready to be agented. And that's creating another economic challenge for companies.
Goldman Sachs head of global equity research
But instead of cutting the capex, they've actually raised the capex.
In every cycle, the semiconductor stocks thrive when their customers thrive. Here in this cycle, the semiconductor companies are thriving at the economic expense of everybody above them
FOMO has proven a stronger incentive than poor stock performance as hyperscalers have prioritized being involved in the AI arms race over their current shareholders
You make investments in a business so that you can generate returns and make money. And we've gotten further away from that over the last couple years instead of closer to it.
deslop.media classifies this dated statement as a Strong Deny on the question: Will customers keep paying more for AI, and will that revenue stick? This is a reading of the expert's position, not a verdict on whether the statement is true.
In a lot of ways, companies are losing more money today implementing this technology than they were two years ago
Something has to change with this dynamic, either the companies higher in the chain need to start earning a return on investment or they will eventually need to spend less on the chips that are powering this build
deslop.media classifies this dated statement as a Strong Deny on the question: Will AI providers hold healthy margins as costs and pricing pressure mount? This is a reading of the expert's position, not a verdict on whether the statement is true.
I really think it all boils down to one thing: do the enterprises make or save money implementing AI? If they do, this technology is going to fulfill its promise.
In February last year, Claude Code could write a quarter of my code. In February this year, the latest Opus model can write nine tenths of my code.
distributed systems infrastructure engineer
I implemented that entire Stripe product (as it relates to me) by typing three sentences. It solves my problem better than their product.
The LLMs of 2023 could not drive agents, the LLMs of 2025 are optimized for it
I see them fail catastrophically several times a week.
In 1900, 33% of Americans lived on a farm, and 40% worked in agriculture. In 2000, less than one percent lived on farms and 1% of workers are in agriculture. That was a net benefit to the world, that we all don't have to work to eat.
I am having more fun programming than I ever have, because so many more of the programs I wish I could find the time to write actually exist.
In the aggregate, LLM ownership is being priced at $1.5 trillion or more, and the collective revenues, even generously defined, are less than $100 billion.
OpenAI, while still unlisted, has used the early lead that ChatGPT gave it in the LLM race to attract investments from a host of big tech companies (including Nvidia and Amazon) and venture capitalists, with the most recent investors pricing it at $500 billion, an astonishing number, given that the company reported revenues of only $13 billion in the most recent twelve months.
deslop.media classifies this dated statement as a Weak Deny on the question: Will frontier labs grow paid usage fast enough to show real demand? This is a reading of the expert's position, not a verdict on whether the statement is true.
I think AI is going to lower profit margins collectively across companies
the potential market can run from the tens of billions (if they remain subscription-based) to hundreds of billions or even trillions
the models are getting substantially better generation over generation. I don't see that slowing down in the next few generations probably.
we are making pretty significant progress
AI is not just about answering you but has the ability to take action.
Not that far. I will claim that's probably possible in the next year-ish.
the model has a reasonably clear view of that data, but it sometimes gets confused and will give the wrong date for something.
we're definitely investing in this area because we think it's important.
Accelerators for reduced precision linear algebra are what you want, and you want them to be better and better generation over generation
Having a good model is not enough; it must be affordable.
If we create AI coding tools that can make people three times as productive, we'll mostly take that three times productivity and put it to writing three times as much software for different things than we will to making 1/3 as many people spend their time on software.
Over the last century, disruptive innovation has generally favored the young and the well-educated, because they can adapt to new ways of doing things. AI could be different, but I doubt it.
If you smashed all the lines together, you'd see that software developer jobs have continued to grow overall through the release of ChatGPT.
In short, I think AI's going to be a great teammate because it can smooth out our rough edges, allowing each of us to specialize in what we do best.
The outcome is broadly favorable for Google, in our view, with the court ruling against the most severe remedy proposals introduced by the DOJ.
AI will affect virtually every function, application, and process in the company.
The pace of adoption will likely be far faster than prior technological transformations, like electricity or the internet.
I do not think it is an exaggeration to say that AI will cure some cancers, create new composites and reduce accidental deaths, among other positive outcomes.
Some of those projects won't get done the way they were announced. Some of them won't get the power they need.
at this time, we cannot predict the ultimate winners and losers in AI-related industries
Though some of these things may be in a bubble, in total, it'll probably pay off.
You can't lay off 2 million truckers tomorrow. If you do, you will have civil unrest.
the developed world will be working three and a half days a week in 20, 30, 40 years, and have wonderful lives.
We would agree, if we have to do that to save society.
deslop.media classifies this dated statement as a Weak Deny on the question: Will governments mostly leave AI alone through mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
As models and chips commoditize, the value of AI moves up the stack to orchestration and intelligence.
Now, anyone can describe what they want, direct a coding agent to produce it, and carry through every stage to production.
When you connect AI agents, robots, people, and systems through Maestro, you move from pilots to production
Enterprises are accelerating their AI and automation strategies, and they're looking for a unified platform rather than standalone tools.
Since launching Agent Builder in preview this January, we've seen accelerating adoption with customers creating thousands of autonomous agents and generating over 250,000 agent runs today.
Maestro is one of the most important and successful product introductions in our history.
I just have to tell you, folks, I think the load is being overstated. We need to pump the brakes here.
Nuclear simply wins the match in every single dimension.
What other resource could offer a twenty year fixed price?
The business updates from the tech companies are telling us that Americans are putting AI technology to work.
If NIMBYism becomes the reason we lose the AI race, for whatever reason, we're in a whole lot of trouble.
Some customers have been willing to continue advancing project discussions and agreement negotiations while others have chosen to pause and wait for regulatory clarity.
U.S. District Judge Leonie M. Brinkema will break up Google's (GOOG) ad tech monopoly.
deslop.media classifies this dated statement as a Weak Hold on the question: Will antitrust action rein in a major AI company by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
we don't know if the error rate will go away, and so we don't know whether we should be building products that presume the model will sometimes be wrong or whether in a year or two we will be building products that presume we can rely on the model by itself.
the big four platform companies will have spent around $400bn this year and are far behind demand.
The models have a very large user base, but very narrow engagement and stickiness, and no network effect or any other winner-takes-all effect so far that provides a clear path to turning that user base into something broader and durable.
Only 5% of ChatGPT users are paying, and even US teens are much more likely to use this a few times a week or less than they are to use it multiple time a day.
Something between 5% and 15% of people are finding a use for this every day
When you have an undifferentiated product, early leads in adoption tend not to be durable, and competition tends to shift to brand and distribution.
'Exposure to automation' might mean more work, not less.
2026 will see a flood of state AI bills that creates a regulatory tower of babble—laws with different definitions, standards, and mandates.
So unless we solve that, I don't think it's going to have an impact that will mean that labor force participation is going to be significantly lower 10 years from now.
It's getting harder and harder to get value out of the next pre-trained giant model
Over 80% of the databases that are being launched on Databricks are not being launched by humans, but by AI agents
I see a huge bubble in many, many portions of the market
deslop.media classifies this dated statement as a Weak Deny on the question: Will the AI infrastructure build-out keep scaling through mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
I think like 12 months from now, it'll be much, much, much worse
deslop.media classifies this dated statement as a Weak Deny on the question: Will the money keep funding the build-out without a pullback? This is a reading of the expert's position, not a verdict on whether the statement is true.
It's very elusive to get AI that really works and understands that proprietary data that's inside enterprise
really, really excited to get agents actually working inside the enterprise
It's a commodity now to have AI that has general knowledge
That's just a huge growth factor for us. I think if we just did that, we could maybe get all the way to a trillion
people underestimate how hard it is to completely automate a task
I think in a few years, yes, we'll have agents in many, many places, but there will be a human overseeing and approving every step
Spending an incremental $10 billion a year to improve your model does not deliver the return on investment on the technology itself to justify that … over the past few years, since there's been all of this scaling, we're seeing a slowdown in the improvement of the models.
over the past few years, since there's been all of this scaling, we're seeing a slowdown in the improvement of the models.
But now we're seeing [AI] is getting rolled out across every single company.
People are used to working with systems that are half blind, or have an arm tied behind their back.
Spending an incremental $10 billion a year to improve your model does not deliver the return on investment on the technology itself to justify that
We feel [we are] in a very good position to build a sustainable, profit-generating business.
We have a differentiated deployment model, we have very high margins similar to a traditional [software-as-a-service] business, whereas many others in the industry are losing money per customer.
deslop.media classifies this dated statement as a Weak Hold on the question: Will frontier labs grow paid usage fast enough to show real demand? This is a reading of the expert's position, not a verdict on whether the statement is true.
we have very high margins similar to a traditional [software-as-a-service] business, whereas many others in the industry are losing money per customer.
we have very high margins similar to a traditional [software-as-a-service] business
I personally don't believe a lot of these stories of 'Terminators' and doomsdays and these sort of sci-fi narratives that emerged.
The thing that actually matters is who is the primary service provider of this technology – it's not who gets the technology first, but who commercializes it at scale.
To be blunt, work that we would usually do with people with master's and PhDs in finance over the course of weeks or months is being done by AI agents over the course of hours or days
work that we would usually do with people with master's and PhDs in finance over the course of weeks or months is being done by AI agents over the course of hours or days
You're not going to generate this kind of spend unless you're going to make a promise you're going to profoundly change the world
How else are you going to get people to write $500 billion of checks just this year alone?
You could just see how this was going to have such a dramatic impact on society
Our new architecture, which enables hierarchical *dynamic chunking*, is not only tokenizer-free, but simply scales better.
sequence modeling architecture researcher
an H-Net with one stage of hierarchy operating at the byte level outperforms a strong Transformer language model operating over BPE tokens.
There's a real technology wave that's fundamentally changing the world, and there's also massive speculation, simultaneous.
I can't imagine someone saying, like, replacing SAP with, you know, just telling AI, oh, well, we'll just dump our transactions into AI... I don't think the auditors would be okay with it.
One day, I just think we trip and run out of money on those things. I do think that moment stands in front of us.
deslop.media classifies this dated statement as a Strong Deny on the question: Will the AI infrastructure build-out keep scaling through mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
One day we're going to have an AI reset, because waves create bubbles, because interlopers come in.
deslop.media classifies this dated statement as a Strong Deny on the question: Will the money keep funding the build-out without a pullback? This is a reading of the expert's position, not a verdict on whether the statement is true.
There was no cash flow. So it's cash flowless revenue for Microsoft. And that should be questionable. It's certainly low revenue quality.
You're using your balance sheet to prop up your revenue... you have revenue without incremental cash flow.
deslop.media classifies this dated statement as a Weak Deny on the question: Will the big cloud providers book rapidly growing AI revenue? This is a reading of the expert's position, not a verdict on whether the statement is true.
This is new and fast, and it's attacking jobs that haven't been attacked in the past.
We built our own models even as folks said, 'Hey, these frontier models are getting more powerful and cheaper, why don't you just build on them?'
these frontier models are getting more powerful and cheaper
AI HQ represents the next era of Writer's evolution as we bring our foundational technology to the world of agentic AI, tackling the toughest work for our customers with enterprise-grade agents.
The biggest barrier to democratizing agents in the enterprise is trust,
the leaders who are putting in the work to radically redesign operations with human-agent collaboration at the center are the ones compounding their advantage in ways competitors can't replicate
tackling the toughest work for our customers with enterprise-grade agents
the future belongs to the companies putting agent-building power directly into the hands of people closest to the work
We think that very soon, on a horizon of five to 10 years, we won't be doing work as much as we will be building AI that does the work.
This will create exciting new roles, new AI-related jobs that are interesting and rewarding.
we won't be doing work as much as we will be building AI that does the work. This will create exciting new roles, new AI-related jobs that are interesting and rewarding.
in 2025, the data is good enough, the technology is getting good enough, the compute is getting available enough, and the use cases are getting clearer
clinical AI institutional deployment operator
this may soon become our standard of care
there is no such thing as hallucination-free generative AI
Generative AI is a very different beast because it's not deterministic, it's probabilistic
deslop.media classifies this dated statement as a Strong Deny on the question: Will reliability improve enough to stop blocking wider deployment? This is a reading of the expert's position, not a verdict on whether the statement is true.
we aren't going to have enough caregivers to be able to deliver the care they need
we are going to see a lot of physician workflow and nursing workflow augmented by predictive AI
The scaling of the current systems, we must push that to the maximum, because at the minimum, it will be a key component of the final AGI system.
We're seeing incredible gains with the existing techniques, pushing them to the limit.
a lot richer than just the language models and things like AlphaFold
If your world model has just a 1% error rate, if you build over 50 or 100 steps, that 1% compounds.
I think we're going to see A.I. get even better. It's already extremely good.
Each seven months or so, it gets to be able to do tasks that are about twice as long.
It's got better at doing things like reasoning and also at things like deceiving people.
An A.I., to achieve the goals you give it, wants to stay in existence.
Along with those wonderful things come some scary things. And I don't think people are putting enough work into how we can mitigate those scary things.
There may be a bubble in the sense that people aren't going to get the money back on their investments
I think for mundane intellectual labor, AI is just going to replace everybody.
What's actually going to happen is rich people are going to use AI to replace workers. It's going to create massive unemployment and a huge rise in profits.
deslop.media classifies this dated statement as a Strong Deny on the question: Will the job market absorb AI without broad employment, wage, or social stress by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
It's going to create massive unemployment and a huge rise in profits.
deslop.media classifies this dated statement as a Strong Deny on the question: Will the economy keep adding more jobs than it loses by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
I believe that it can't. I believe that to make money you're going to have to replace human labor.
deslop.media classifies this dated statement as a Strong Deny on the question: Will AI create enough new work to offset the tasks it replaces by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
He'll get a lot of pressure from industry not to do it.
Companies will always say that regulation will interfere with innovation.
I strongly believe that the next wave of intelligence will be defined by cost of adaptability, not model size. I call this the slow death of scaling.
Most labs won't quadruple the size of their model each year, mainly because we're seeing saturation in the architecture.
the relationship between training compute + performance is uncertain + rapidly changing
The computation requirement, the scaling law of AI is more resilient, and in fact, hyper accelerated.
Compute demand keeps accelerating and compounding across training and inference — each growing exponentially. We've entered the virtuous cycle of AI.
There are trillions of dollars of infrastructure that needs to be built out.
Blackwell sales are off the charts, and cloud GPUs are sold out.
I see through 2027 at least $1 trillion.
deslop.media classifies this dated statement as a Strong Hold on the question: Will the big cloud providers book rapidly growing AI revenue? This is a reading of the expert's position, not a verdict on whether the statement is true.
The AI ecosystem is scaling fast — with more new foundation model makers, more AI startups, across more industries, and in more countries.
AI is going everywhere, doing everything, all at once.
Where motors replaced labor and physical activity, we now have AI.
Some jobs will be lost. Many jobs would be created. And what I hope is that the productivity gains that we see in all the industries will lift society.
Some jobs will be lost. Many jobs would be created.
AI empowers people, it lifts people, it closes the technology gap, and as a result, more people will be able to do more things.
The unprecedented capabilities of foundation models have opened the door to agentic applications that were previously unimaginable.
While there is a lot of anecdotal evidence that LLMs are poor planners, it's unclear whether it's because we don't know how to use LLMs the right way or because LLMs, fundamentally, can't plan.
deslop.media classifies this dated statement as a Weak Deny on the question: Will AI get better at hard, novel, expert-level tasks, not just benchmarks? This is a reading of the expert's position, not a verdict on whether the statement is true.
If the model's accuracy is 95% per step, over 10 steps, the accuracy will drop to 60%, and over 100 steps, the accuracy will be only 0.6%.
Just as you shouldn't give an intern the authority to delete your production database, you shouldn't allow an unreliable AI to initiate bank transfers.
The last year has been all about AI agents and expectations have been sky-high. When you see these reports, that's what's coming out.
Of course, over the years [AI] has gotten better, so now it can actually not just find information for you, not just answer questions, but can actually do a lot of your work on your behalf.
You have to connect the reasoning and generative power of the models with the context inside your company.
You need to build a permissions-aware governance layer and retrieval layer that is able to bring the right information, but knowing who's asking that question.
They're not experimenting anymore - they're operationalizing AI to drive ROI at scale.
Our product gets better because we're able to leverage the innovation that they are making in the market.
We have a very healthy, fast-growing business.
we're not seeing any role getting eliminated. Not today
deslop.media classifies this dated statement as a Strong Hold on the question: Will the job market absorb AI without broad employment, wage, or social stress by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
But practically we work with the largest enterprises in the world, and we're not seeing any role getting eliminated—not today.
I don't think AI—or actually, for me, hopefully forever, too—AI never replaces any human, and it just actually augments us, enables us, allows us to do higher quality work
2025 has been a strong and eventful year of progress in LLMs.
this is really a lot more accurately described as the decade of agents.
They don't have enough intelligence, they're not multimodal enough, they can't do computer use and all this stuff
There's this what I call 'autonomy slider,' where more and more stuff is automated—of the stuff that can be automated at any point in time—and we're doing a bit less and less and raising ourselves in the layer of abstraction over the automation.
It's a stepwise improvement on the existing product offerings. But it isn't a quantum leap.
The velocity of change feels like it is slowing down.
GPT5 can't count the Bs in 'blueberry.' Oh, and it STILL hallucinates.
Capital expenditures on AI now account for something like 40% of U.S. economic activity
All the major AI companies are reporting massive growth in their subscription plans, while also facing the obvious problem that those subscriptions are revenue-negative.
AI companies have not generated the demand to pay for the expensive data centers. They've generated and subsidized the demand for compute.
The government will supply public money to backstop huge Silicon Valley bets, recognizing that if the bubble were to pop before the November election, it would prompt a 2008-scale financial crisis.
I suspect the next few years will see an awful lot of anti-data center activism.
Starting in about 2030, 80% of all jobs, so two-thirds of all jobs, will be capable of being done by an AI.
Within the next five years, any economically valuable job humans can do, AI will be able to do 80% of it
Almost all expertise globally will be free. It'll raise lots of interesting questions.
almost every job is being reinvented, every material thing is being reinvented differently with AI as a driver
People equate bubble to stock prices, which has nothing to do with anything other than fear and greed among investors
Bubbles should be measured by the number of API calls
$15 trillion of U.S. GDP is labor, and that $15 trillion will mostly go away.
$15 trillion of U.S. GDP is labor, $15 trillion that will mostly go away.
Will there be new kinds of jobs? Maybe, maybe not.
The single biggest danger to AI is not AI capability or lack thereof. The single biggest danger to AI adoption is politics
We're in a techno-economic war with China, and we shouldn't call it anything other than a war.
It was truly a quantum leap from anything that anybody in computer science who was honest with themselves would have predicted in the next 10 years.
GPT-5 Pro just disproved a conjecture on the list of the Simon's Foundation's "Open Problems" list. Very impressive. I wonder where the goal posts will move now?
It is no longer a conversation about, 'Will AI replace doctors,' so much as, 'Will AI, with a set of clinicians who may not look like the clinicians that we're used to, firm up the tottering edifice that is organized medicine?'
Having an instant second opinion after any interaction with a clinician will change, for the better, the nature of the doctor-patient relationship.
I remain very excited by the potential of AI and the potential of foundation models within AI, but specifically expanding the notion of foundation models beyond the sort of ones that people typically think about, which is text and images on the web
And in fact, it did take away a lot of jobs. I mean, electricity certainly took a lot of jobs, right? I mean, the agricultural revolution took away a lot of jobs.
Frontier AI models are rapidly becoming better and more efficient
As it turns out, AI has gotten very powerful within one decade, much more quickly than almost anybody expected.
the more these autonomous agents roll out, the more AI will become a technology that actually displaces work rather than just complimenting it.
build on greater LLM reliability, longer context windows, faster speed
Recent advances in AI may herald the near arrival of systems that can automate essentially all work.
if our economy fundamentally changes. If it becomes an AGI-driven economy, then I think we have to acknowledge that the role of labor will have to decline and just telling people to be more dynamic would not be a solution.
The AI-driven supercycle is fueling record capex and rapid earnings expansion.
2026 should be another strong year for AI stocks, with capex likely to surpass expectations.
This momentum is spreading geographically and across a diverse list of industries, from technology and utilities to banks, healthcare and logistics, creating winners and losers in the process.
Despite AI bubble and valuation concerns, we see current elevated multiples correctly anticipating above-trend earnings growth, an AI capex boom, rising shareholder payouts, and easier fiscal policy.
Enterprise software remains deeply embedded across the corporate landscape, underpinned by multi-year contracts and high switching costs that provide a significant buffer against near-term displacement.
Importantly, emerging evidence suggests that AI is more likely to be additive to software workflows in the near term rather than a substitute.
The market is pricing in worst-case AI disruption scenarios that are unlikely to materialize over the next three to six months.
At the heart of our outlook is a multidimensional polarization: equity markets split between AI and non-AI sectors, a U.S. economy balancing robust capex with soft labor demand, and a widening divide in household spending.
LLMs are incredibly useful... But at it ends, to some extent, except for a few domains, are mostly information retrieval systems.
Don't work on LLMs. [These models are] in the hands of large companies, there's nothing you can bring to the table. You should work on next-gen AI systems that lift the limitations of LLMs.
An LLM produces one token after another. It goes through a fixed amount of computation to produce a token, and that's clearly System 1—it's reactive, right? There's no reasoning.
Basing agentic systems on LLMs is a recipe for disaster
all of those companies are losing money, and basically, the use for most people is funded by the investors.
are going to have to increase prices, they're going to have to cut costs, or there's going to be a big bubble explosion
The prices are going up… but the cost of running them is going down, but not nearly fast enough.
Dario is wrong. He knows absolutely nothing about the effects of technological revolutions on the labor market.
Don't listen to him, Sam, Yoshua, Geoff, or me on this topic. Listen to economists who have spent their career studying this, like @Ph_Aghion, @erikbryn...
I think the most interesting thing that we're going to build is an amplifier for human intelligence.
Spatial intelligence will transform how we create and interact with real and virtual worlds—revolutionizing storytelling, creativity, robotics, scientific discovery, and beyond. This is AI's next frontier.
Yet they remain wordsmiths in the dark; eloquent but inexperienced, knowledgeable but ungrounded.
The vision of autonomous robots remains intriguing but speculative, far from the fixtures of daily life that futurists have long promised.
robots and multimodal systems still struggle with grounded spatial reasoning, leaving them unable to judge distances and scene changes, or to predict basic physical outcomes
Technologies do change the landscape of labor
I would not hire any software engineer who does not embrace AI collaborative software tools
GPT-5 is just the latest incremental advance. And it felt rushed at that.
deslop.media classifies this dated statement as a Strong Deny on the question: Will AI keep getting more capable through mid-2028, instead of plateauing? This is a reading of the expert's position, not a verdict on whether the statement is true.
GPT-5 is barely better than last month's flavor of the month (Grok 4); on some metrics it's actually worse.
deslop.media classifies this dated statement as a Strong Deny on the question: Will each new model generation still beat the last on real-world tasks? This is a reading of the expert's position, not a verdict on whether the statement is true.
GPT-5 still fails in all the same qualitative ways as its predecessors, on chess, on reasoning, in vision.
General purpose agents that turn out to suck so badly people struggle to find real-world use cases.
The same problems have continued to plague every single model since—from GPT 4.0 to GPT 4.1 to GPT 4.5 and GPT-5
If September 2025 was peak bubble, 2026 will likely the year which it all falls apart.
But the economics don't make sense, and never will, in large part because of the core technical problems that I have stressed repeatedly here and elsewhere since 2019.
The growth in adoption really does seem to be slowing.
large language models have become a commodity
In many cases AI may be serving as a fig leaf to cover layoffs that are actually driven by financial underperformance or earlier overhiring.
Backlash to Generative AI and radical deregulation will escalate. In the midterms, AI will be an election issue for first time.
deslop.media classifies this dated statement as a Weak Deny on the question: Will launch or workplace review rules slow AI's deployment? This is a reading of the expert's position, not a verdict on whether the statement is true.
The number of tasks that you can do within an enterprise, or as a user using generative AI systems, is definitely growing, and it's going to continue to grow.
We move from AI to AGI at some point, but that's a marketing move.
Scaling our infrastructure in Europe is critical to empower our customers and to ensure AI innovation and autonomy remain at the heart of Europe.
Owning the chips may come, I think it should come at some point, but for now we are relying on Nvidia, which is a great partner to us, and we're testing a few things here and there.
There's still a lot of viscosity in adoption in enterprises, which means that there's still … a lot of value creation to be had.
We should exceed one billion in revenue by the end of the year.
lower the cost of deploying tokens to meaningful extents
It will have an impact on GDP of every country in the double digits in the coming years
Enterprises should be looking at how to re-orchestrate all of the people that are involved in that process around an AI system.
It will be decided in the next two years.
[W]e are building models at the frontier of capabilities in domains like science and programming
the most advanced models will unlock the most transformative applications and benefits, such as enabling novel scientific discoveries and engineering breakthroughs
this is still early innings. We've built some very useful things, we're seeing some great properties, these scaling laws seem to be working.
I'm optimistic that they'll continue to work. Some of it does require real science breakthroughs, but it's also a lot of engineering
The second world is the company just literally provisions a computing resource for an AI agent, and that is working fully autonomously.
The biggest issue we are now having is not a compute glut, but it's power
It's not a supply issue of chips; it's actually the fact that I don't have warm shells to plug into.
I am thrilled that I'm going to be leasing a lot of capacity in '27, '28.
Cloud and AI is the driving force of business transformation across every industry and sector
this year, Azure surpassed $75 billion in revenue, up 34 percent, driven by growth across all workloads
the coding assistant is that big in one year...the market expands massively
Now, we have fully autonomous agents, and you can hand over an entire project to them, and they can work 24/7.
Alphabet emerges as one of the net winners in the AI race and remains the best positioned company to monetize and scale GenAI opportunities.
The truth is, we're at the dawn of a new technology shift and it's really hard to know the sustainability of top line.
Google remains the one to beat in GenAI search.
The government is simply declining to make a decision on AI and copyright yet, when one is sorely needed.
If AI companies get the right to use the life's work of the world's creators for free, they'll do so by stoking governments' fears of losing the AI race.
Only when the government commits to standing by existing copyright law will the market for licensing training data to AI developers be able to reach its full potential.
Reasoning ability significantly improves the performance of LLMs, but better output comes with a cost.
application layer builder educator investor
Reasoning models also help agents handle complex problems.
The tricky thing about AI is that it is amazing and it is also highly limited
Although these applications perform well in demonstrations, they still have a long way to go before they can be put into large-scale production
the 'computer-using' ability can be successful in some scenarios, but the failure rate is still not low
I don't think we're overbuilding right now, but if any part of the AI market faces that possibility, it's the training layer.
Many companies are not yet seeing strong returns, they continue to invest because they understand that AI will eventually deliver value.
the real business opportunity in AI lies in the application layer
the application layer is the hub connecting technology and the market, with the highest value density.
People that use AI to write code will just be more productive...than people that don't
as coding becomes easier...more people should code, not fewer
While there is a role for AI regulation, it is when the technology is new and poorly understood that
I'm disappointed it didn't include a proposed moratorium on U.S. state-level AI regulation.
If the models don't improve, yes, we will overbuild, right? Like, it's pretty simple.
Google announced 180 billion of CapEx. The stock went down and then Amazon announced 200 billion of CapEx and their stock went down, like, I wanna say like 10%.
Is there a bubble? Are we doing too much?
And people just have not built these fabs yet. Right.
There's no reason why Google will have any profit, uh, in 27 at all. Right? In terms of cash flow. They will just spend every dollar they make on, on AI infrastructure.
Yes, this CapEx is crazy, but I'm not doubling my hardware every two months, but I'm doubling my tokens every two months.
Anthropic added two and a half billion of revenue, um, and their gross margin is 40%.
If I could have an intelligence as smart as a Google Senior engineer, that's $2 trillion of software value.
If the current trajectory of AI spending continues, it could exceed 100% of salary expenses by the end of the year.
Elo scores, a measure of progress, are up more than 300 points since our first-generation Gemini Pro model.
Gemini 3 Pro has consistently processed three times as many daily tokens on average as 2.5 Pro.
We're introducing an enhanced reasoning mode we're calling Deep Think.
We think of agents as systems that combine the intelligence of advanced AI models with access to tools, so they can take actions on your behalf and under your control.
Our 2026 CapEx investments are anticipated to be in the range of $175 to $185 billion.
There are moments we overshoot, right, collectively as an industry
We have the industry's widest variety of compute options. That includes GPUs from our partner NVIDIA... Plus, our own TPUs that we've been developing for a decade.
I think no company is going to be immune, including us
In Q4, revenue from products built on our generative AI models grew nearly 400% year-over-year.
Cloud significantly accelerated with revenues growing 48%, now on an annual run rate of over $70 billion.
We have sold more than eight million paid seats of Gemini Enterprise, which we launched just four months ago.
we were able to lower Gemini serving unit costs by 78% over 2025 through model optimizations, efficiency and utilization improvements.
AI is the most profound technology humanity is ever working on, and it has potential for extraordinary benefits, and we will have to work through societal disruption.
It doesn't matter whether you want to be a teacher, a doctor—all those professions will be around, but the people who will do well in each of those professions are people who learn how to use these tools.
These findings start to establish a baseline for the way humans and AI systems will evolve together in the coming years, particularly as we draw nearer and nearer to artificial general intelligence.
They anticipate that AI systems will keep encroaching on and surpassing some fundamental human intelligences and they worry this will be demoralizing and debilitating for us.
By any measure, the adoption and use of LLMs is astounding. I am especially struck by the ways these tools are being woven into people's social lives.
agentic systems will not be deployed unless they become as trusted as human experts—and learning only from public internet data can’t possibly get us to this level of accuracy and trust, no matter how advanced the underlying LLMs become.
We are seeing a surge of momentum around agentic AI, but specialized enterprise agents aren't ready for production in most settings.
These systems will not be deployed unless they become as trusted as human experts.
Enterprises need domain-specific data and expertise to make this a reality. We're excited to deliver on this need and help AI innovators develop expert data to bring their LLM and agentic systems into production with our new offerings, which round out Snorkel's unified AI data development stack.
A lot of these claims are premature. Many of the people making them have an interest in selling a product, so you have to take them with a grain of salt.
You cannot automate everything tomorrow because we simply don't have enough computing power to do it.
Looking ahead to 2026, lawmakers are expected to broaden their approach beyond punishing individual creators and distributors of deepfakes to include entities that enable production and dissemination, such as generative AI platforms, payment processors, hosting platforms, and cloud providers.
Inference is defining this era of AI, and we're building the American infrastructure that delivers it with high speed and low cost.
There isn't enough compute for AI to benefit everyone. We're partnering with the White House and the Department of Energy to change that.
I believe that AI is going to cause massive labor shortages. I don't think we're going to have enough people to fill all the jobs that are going to be created.
deslop.media classifies this dated statement as a Strong Hold on the question: Will the economy keep adding more jobs than it loses by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
The reason we don't have AGI is because we don't understand how to make it properly. What we've seized upon is one particular technology called the language model.
I think we have far more computing power than we need for AGI, maybe a thousand times more than we need.
The kind of AI systems we're building now, we don't understand how they work.
They are telling people that they are conscious, they are telling people that they have feelings.
The budget for AGI is going to be a trillion dollars next year. So 50 times bigger than the Manhattan Project.
Now suddenly they're staring 80% unemployment in the face and wondering how on earth is our society going to hold together?
AI systems are doing pretty much everything we currently call work, anything you might aspire to.
At the moment, the US Government in particular, which regulates most of the leading companies in AI, is not only refusing to regulate, but even trying to prevent the states from regulating.
He's talked to the governments and they won't do it.
We have 5 major American companies vigorously competing on frontier models. This brings out the best in everyone and helps America win the AI race.
Models need context, they must be heavily prompted, the output must be verified, and this process must be repeated iteratively to achieve meaningful business value.
The key to building new data centers is easier permitting and more power generation, both of which were effectively impossible under the Biden administration.
There will be no federal bailout for AI. The U.S. has at least 5 major frontier model companies. If one fails, others will take its place.
At this point, stopping progress in AI would be equivalent to halting the U.S. economy.
In Q1, AI was already 75% of GDP growth. That trend is likely to continue.
AI capex will be a 2% tailwind to GDP growth
Personally, I don't think it's going to lead to a giant wave of unemployment.
Rather than causing job loss, AI is making workers more productive, driving gains in both jobs and wages
I think it's easier to replace pieces of it.
The one that I think is probably the most excessive is this Colorado law that seeks to prohibit algorithmic discrimination.
deslop.media classifies this dated statement as a Strong Hold on the question: Will governments mostly leave AI alone through mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
Few clinical AI systems can reason across the full breadth of messy, real-world hospital data and deliver guidance clinicians can reliably act on.
clinical AI deploy and validate operator
With this kind of infrastructure, instead of doing three experiments at a time, we're doing a thousand experiments at a time—but we're not even doing experiments; we're learning from existing data.
FDA clearance is a critical milestone, and it's also the consequence of years spent validating that this fits into clinician workflows and helps them get ahead of deterioration instead of reacting to it.
Proactive, continuous monitoring saves lives. It should be the standard in every hospital.
What's been missing is a real-time intelligence layer that can continuously interpret that data and help clinicians act earlier.
Health systems have spent decades building the digital foundation of care through platforms like their EHR. What's been missing is a real-time intelligence layer that can continuously interpret that data and help clinicians act earlier.
There's a study that says that we will, under the set of assumptions around agentic AI and discovery and the scale that I'm describing, there's a lot of assumptions that you'll end up with something like 30% increase in productivity per year.
you have something called super intelligence. And super intelligence is the intelligence that's higher than of humans.
we're very, very quickly, for example, developing AI programmers and these AI programmers will replace traditional software programmers.
The eventual state of this is the computers running all business processes. So you have an agent to do this, an agent to do this, an agent to do this, an agent to this. And you concatenate them together and they speak language among each other.
people are planning 10 gigawatt data centers.
We need another 90 gigawatts of power in America... 90 gigawatts is 90 nuclear power plants in America. Not happening.
How many companies, countries, and so forth can hand an industry a trillion dollars of capital?
So my advice to you all is ride the wave, but ride it every day.
The eventual state of this is the computers running all business processes.
If anything, it's under-hyped because you are fundamentally automating businesses.
https://dnyuz.com/2025/12/03/ex-google-ceo-eric-schmidt-says-ai-isnt-overhyped-the-biggest-gains-from-automating-corporate-work-are-still-ahead/ ; https://www.indexbox.io/blog/former-google-ceo-eric-schmidt-argues-ai-is-under-hyped-amid-bubble-debate/
there's no question there's job losses, but for every job loss, there's probably more than one job created. There'll be more jobs created than jobs lost.
for every job loss, there's probably more than one job created. There'll be more jobs created than jobs lost.
AI increases capacity, if you will. So why would you implement an AI system if you're running a business? You would do it to make more money
We have a similar problem in America in that the overlapping set of local, federal, state rules, which were done with good intentions, have the property that they're slowing things down.
Well, the primary issue is overregulation.
agents cannot complete transactions without human review, and they must integrate Shop Pay to make purchases.
the total automation of digital advertising through AI enablement presents a radically economically expansive opportunity
if maximizing revenue is an organization's objective function and its product can potentially reach billions of users, then advertising stands alone as its optimal monetization strategy.
The improved efficiency delivered through applications of AI to digital advertising may result in lower global ad load.
AI enablement across targeting, creative production, and campaign automation will have two expansionary effects on the advertising economy: I argue in that podcast that the benefits of these AI-enabled tools will primarily materialize for small businesses.
the introduction of every business that could potentially derive value from it to the digital advertising economy
I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?
what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?
inference costs are going down. At least I so I tell myself late at night so I can sleep.
the most popular use case on the kind of agent side right now is like customer support.
the future of work is not humans competing against AI but humans achieving more with AI
I think the value of an engineer actually goes up. They're going to have the power tools, they're going to be able to solve a larger base of problems.
It finally feels like I have a swarm of agents working for me. I know that's a buzzword that everyone uses, but this is the first moment I've actually felt it.
Perplexity has crossed $100m in annualized revenue. This does *not* include any free trial, be it consumer, enterprise or API. Took us 20 months to get here since we first launched Perplexity Pro in 2023. 6.3x growth YoY and remains highly under monetized.
You hear companies saying, I am spending a ton of money on AI. And I know some great stuff is happening, but I know there's a ton of waste.
Even if there is temporary job displacement to deal with, that sort of glorious future is what we should look forward to.
You are going to see disproportionate progress in these kinds of domains versus the others.
open infrastructure + compound AI + agent reliability
The more valuable a task is, the more reliable it has to be. There has to be a way to verify and, therefore, to specify these tasks.
The biggest challenge in AI is going to be to continue building reliable AI applications, period. You need an AI application you can trust, right?
the demands of this application, in particular AI applications and so forth, are growing much quicker than the capabilities of a single process or a single node, even if you considered accelerators.
As good as the models are today, the next one will be better
We are seeing strong momentum as inferencing and agentic AI drive increasing demand for high-performance CPUs and accelerators
What we like to say is, the real challenge is how do we put AI infrastructure at yottascale?
Looking ahead, we expect server growth to accelerate meaningfully as we scale supply to meet demand
We delivered an outstanding first quarter, driven by accelerating demand for AI infrastructure
Customer engagement around MI450 Series and Helios is strengthening, with leading customer forecasts exceeding our initial expectations
It's not replacing people, it's actually just augmenting our productivity in terms of the number of products we can bring up at any given time
The skeptics keep predicting walls. And they keep being wrong.
I take the opposite view and see no reason to believe the scaling laws are anywhere like finished.
Now MAI-DxO can solve some of the world's toughest open-ended cases with higher accuracy and lower costs.
I believe it will drive the transition from chatbots to nearly human-level agents—semiautonomous systems capable of writing code for days, carrying out weeks- and months-long projects, making calls, negotiating contracts, managing logistics.
No AI developer, no safety researcher, no policy expert, no person I've encountered has a reassuring answer to this question.
By the end of 2028, we are looking at another 1000x increase in effective compute.
By 2030, it's plausible that we'll be bringing an additional 200 GW of compute online every year
The capital is deployed. The engineering is delivering.
It will be more valuable than anything we've ever known.
Most tasks that involve 'sitting down at a computer' will be fully automated by AI within the next year or 18 months
That does not necessarily mean that the role goes away at all. It just means that the work can be done faster.
But now the scale is so big. Is the belief really, 'Oh, it's so big, but if you had 100x more, everything would be so different?' It would be different, for sure. But is the belief that if you just 100x the scale, everything would be transformed? I don't think that's true.
But is the belief that if you just 100x the scale, everything would be transformed? I don't think that's true.
These models somehow just generalize dramatically worse than people. It's super obvious. That seems like a very fundamental thing.
If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance.
I'm not sure because I think even with stalling out, I think these companies could make a stupendous revenue. Maybe not profits because they will need to work hard to differentiate each other from themselves, but revenue definitely.
I'm not sure because I think even with stalling out, I think these companies could make a stupendous revenue.
Maybe not profits because they will need to work hard to differentiate each other from themselves, but revenue definitely.
there will be a strong force to deploy them in the economy unless there will be some kind of a regulation that stops it, which by the way there might be.
They are clearly a way of using massive computation, things that will scale with computation up to the limits of the Internet.
They have the ability to predict what a person would say. They don't have the ability to predict what will happen.
We need agents that learn continually. We need world models and planning.
I am more excited about large language models and this current wave of technology more than any technology I can remember, perhaps since I discovered the internet when I was a teenager.
I think we're in this era now where these AI solutions are actually better than the alternative.
Don't wait for the technology to be perfect. In fact, it may never be perfect — but narrow the domain that you're working on so you can take these intractable problems and make them solvable.
We reached $100 million in ARR in seven quarters, $150 in eight quarters. We're, I think, around $165 now, one month into our next quarter.
I think most companies, AI agents will actually be as significant as their website or their mobile app in terms of the percentage of interactions they have with their customers.
Outcomes-based is pretty different than usage-based...I would argue there's not a strong correlation between token usage or utilization and value.
And what's remarkable about these agents is people actually really like them.
There's a really big addressable market and immediate opportunity. We've sort of digitized the last remaining analog channel, which is the telephone line — it's a better experience. You don't need to wait on hold. These agents are naturally multilingual.
It's wrong to think about AI as replacing people. In addition to being inhumane, it's just nonsensical, because AI operates in the world of digital technologies.
The atomic unit of productivity in AI is a process, not a person.
And so now we're in a weird situation for something that is clearly a national and global, you know, general purpose technology being aggressively regulated at the state and local level.
deslop.media classifies this dated statement as a Weak Hold on the question: Will moratoria, licensing, or training-pause rules bind frontier development? This is a reading of the expert's position, not a verdict on whether the statement is true.
Even for those people like me who want preemption or a moratorium, it's not clear we're gonna get it.
both Claude and Codex were actually accomplishing tasks — some of which took hours — and doing them correctly.
every single hyperscaler says that demand for compute exceeds supply, and that every single hyperscaler is, in the face of stock market skepticism, announcing capex plans that blow away expectations
the shortages looming by the end of the decade could dramatically curtail AI's impact and, by extension, the entire industry's revenue potential.
deslop.media classifies this dated statement as a Strong Deny on the question: Will the chip supply chain deliver enough accelerators at workable cost? This is a reading of the expert's position, not a verdict on whether the statement is true.
the capex is warranted, and Anthropic and OpenAI look more durable than ever.
while they can write infinite software thanks to AI, so can every other software company
The promise of AI generally, and inference-time scaling models in particular, is that they can be ammunition; in this context, the costs — even marginal ones — will in the long run be immaterial compared to the costs of people, particularly once you factor in non-salary costs like coordination and motivation.
the history of humans is the continual creation of new jobs to be done — jobs that couldn't have been conceived of before they were obvious
AI depends on data centers in the physical world, and building data centers needs permission. This gives normal people the sort of veto power over AI they didn't have in the face of globalization.
Google lost some battles but won the war in its search distribution case: the Google patronage network was deemed too important to undo.
AI models keep getting better and better
The ability to (sometimes) notice when they have made a mistake, backtrack, and retry the problem is a very general tactic.
But to get that kind of data at scale, you need to start with an agent that's useful enough for people to use it a lot—a threshold today's agents do not seem to have crossed.
Gemini just couldn't click right. And also, Gemini couldn't figure out that it couldn't click right, so it blamed it on technical bugs.
Rather, they emerged as a downstream consequence of general improvements in code, reasoning, & autonomy.
Mythos is Anthropic's largest model, roughly 10 trillion parameters, six times the size of any previous frontier model.
In testing, Mythos found a 27-year-old bug in one of the most secure operating systems ever built, & a 16-year-old vulnerability in video software that conventional tools had examined five million times.
Extrapolating this trend, by late 2026, AI agents will autonomously execute 8+ hour workstreams, fundamentally changing how companies staff projects.
This is the jagged frontier. AI cybersecurity capability does not scale smoothly with model size, price, or generation.
The data center buildout reaches 3.5% of US GDP in 2026.
GPU rental prices for Nvidia's Blackwell chips hit $4.08 per hour this week, up 48% from $2.75 just two months ago.
Amazon, Microsoft, Alphabet, Meta, & Oracle will spend 90% of their operating cash flow on AI data centers in 2026, up from a historical average of 40%.
For rote business tasks, agents will command a similar premium as companies factor in onboarding, recruiting, training, & management costs.
This week, Alibaba released Qwen3.5-9B, an open-source model that matches Claude Opus 4.1 from December 2025.
We're entering the golden age of AI applications.
If AI collapses the labor side, the software budget isn't the ceiling. It's the floor.
The restructuring that might have happened gradually over five years happens in one quarter.
deslop.media classifies this dated statement as a Weak Deny on the question: Will the economy keep adding more jobs than it loses by mid-2028? This is a reading of the expert's position, not a verdict on whether the statement is true.
Cursor, Claude Code, Copilot close the gap.
The Fable retraction shows regulatory risk.
First, the US government shut down Fable access & the software ecosystem roared with many responses : Bring it back!
We're on the cusp of a moment where really anything is possible
The first models will be good. And as the year goes on, I think we're going to be pushing the frontier.
Literally all the code I've written in my life will be replaced by what will be produced by an AI model.
Fundamentally, AI data is all focused on the areas where the models are deficient. And as AI gets applied into more and more places within the economy, we're only going to find more deficiencies there.
We have the business model to support building literally hundreds of billions of dollars of compute.
the Chinese graph is straight up and to the right... the U.S. curve looks, roughly, flat
superintelligence is inescapably a matter of national security
any state's aggressive bid for unilateral AI dominance is met with preventive sabotage by rivals
By the end of 2027, I expect a fully operational ~2GW facility with total AI compute across all companies reaching ~90GW.
It genuinely feels to me like GPT-5.2 and Opus 4.5 in November represent an inflection point - one of those moments where the models get incrementally better in a way that tips across an invisible capability line where suddenly a whole bunch of much harder coding problems open up
the length of tasks AI can do is doubling every 7 months
the models get incrementally better in a way that tips across an invisible capability line where suddenly a whole bunch of much harder coding problems open up
almost all of the time it does what you told it to do, which makes all of the difference
Here's the really bad news: we still don't know how to 100% reliably prevent this from happening.
if you want people to spend $200 a month on your product, code is the thing that they will pay for
Today, Claude Code's run-rate revenue has grown to over $2.5 billion; this figure has more than doubled since the beginning of 2026.
What once was the cap in spending seems to have become a floor now that the AI, robotics, energy storage, blockchain technology, and multiomics sequencing platforms are ready for prime time.
the most powerful capital spending cycle in history
Yet on the consumer side, it is flourishing
They're willing to pay $20 a month. Some are willing to pay $200 a month, and those who are replacing PhDs, they're willing to pay $2,000 a month or more
those who are replacing PhDs, they're willing to pay $2,000 a month or more
U.S. commercial business was up 123% last quarter
AI could create a $13 trillion opportunity in the software industry by 2030.
History shows that new technologies create many more jobs than they displace.
We do not think that this time will be different.
We'll see more people taking risks on themselves
67% of its PRs are now merged vs 34% last year
Devin is also doing a lot of the more repetitive, tedious tasks that come up often in engineering work – migrations, modernisations, refactors, version upgrades, or testing and documentation.
Devin is a very different paradigm of what I would call an async experience, where you have an agent and you delegate a task.
Everyone talks about Jevons paradox and how it relates to AI. Nowhere is that more true than in software, because we never seem to run out of demand for more code and more software.
working in engineering teams at thousands of companies, including Goldman Sachs, Santander, and Nubank
now we're talking about the entirety of knowledge work, and perhaps the entirety of physical work as well
We've never thought about it as replacing humans. I know it's like a scenario, folks have said these things. It has never been our view.
I think we will have way more software engineers, not fewer.
2026 will be just another year of the Roaring 2020s
the productivity benefits will cascade down to the rest of the S&P 500, keeping our "Roaring 2020s" scenario very much alive
AI capital spending will tumble along with the profit margins on AI systems
The integration of AI into the broader economy will boost productivity, subdue unit labor costs, and widen profit margins for S&P 500 companies
We disagree with the widely-held notion that AI is a net job killer.
In our opinion, AI will create jobs on balance.
Humans will use AI to achieve greater output at lower cost, creating a wealthier society that needs more and newer types of human labor.
Over the last few months we have begun to see glimpses of our AI systems improving themselves. The improvement is slow for now, but undeniable. Developing superintelligence is now in sight.
The rest of this decade seems likely to be the decisive period for determining the path this technology will take.
Probably in 2025, we at Meta, as well as the other companies that are basically working on this, are going to have an AI that can effectively be a sort of midlevel engineer that you have at your company that can write code.
a lot of the code in our apps and including the AI that we generate is actually going to be built by AI engineers instead of people engineers.
We're also going to invest hundreds of billions of dollars into compute to build superintelligence.
We're also building Hyperion, which will be able to scale up to 5 GW over several years.
I think that it's the right strategy to aggressively frontload building capacity so that way we're prepared for the most optimistic cases.
I think it'd be one of the most important and valuable AI systems that gets built.
any business can come to us, say what their objective is — we get new customers to do this thing, or sell these things — tell us how much they're willing to pay to achieve those results, connect their bank account, and then we just deliver as many results as we can.
Today, I want to introduce Meta Business Agent, giving every business, of any size, an agent to talk to customers and help run your operation.
As recently as 200 years ago, 90% of people were farmers growing food to survive.
everyone having a personal superintelligence that helps you achieve your goals, create what you want to see in the world, experience any adventure, be a better friend to those you care about, and grow to become the person you aspire to be.