The AI Daily Brief: Artificial Intelligence News and Analysis - Something Big Is Happening
Episode Date: February 15, 2026An 80-million-view post by Matt Schumer ignited one of the most important AI debates of 2026—are we underestimating how fast AI is transforming work, or overhyping disruption before it reaches the r...eal economy? This episode breaks down the original argument that a shift has already occurred inside tech, the sharp critiques that followed, and what the back-and-forth reveals about risk, mindset, and adaptation. From “tool-shaped objects” to the seen vs. the unseen, the core question isn’t whether AI is powerful—it’s what happens if you’re wrong about the speed and stakes of change Sources:https://x.com/mattshumer_/status/2021256989876109403https://x.com/WillManidis/status/2021655191901155534https://x.com/cboyack/status/2021647373571862952Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. https://www.kpmg.us/AIpodcastsRackspace Technology - Build, test and scale intelligent workloads faster with Rackspace AI Launchpad - http://rackspace.com/ailaunchpadBlitzy - Want to accelerate enterprise software development velocity by 5x? https://blitzy.com/Optimizely Agents in Action - Join the virtual event (with me!) free March 4 - https://www.optimizely.com/insights/agents-in-action/AssemblyAI - The best way to build Voice AI apps - https://www.assemblyai.com/briefLandfallIP - AI to Navigate the Patent Process - https://landfallip.com/Robots & Pencils - Cloud-native AI solutions that power results https://robotsandpencils.com/The Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai
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Today on the AI Daily Brief, something big is happening.
The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
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Now, as you guys know, I'm traveling,
and I had actually pre-planned a long read slash big think episode for this Sunday,
but then this conversation, prompted by an article by Matt Schumer,
absolutely took over our corner of the internet,
and frankly expanded quite a bit beyond it,
in a way that it felt important to add our part to the conversation
and make sure that if you hadn't yet,
you get access to part of this as well.
So we're going to read some excerpts from something big is happening.
A post that appeared on X about a week ago,
and has 80 million views.
Much more than the views, it has sparked an enormous number of response articles and conversations,
a couple of which we will also be excerpting,
and the reason it's so important is that Matt has encapsulated and crystallized
this sentiment which you've been hearing and feeling through this show all year,
which is that a shift has happened with big implications.
Matt starts, think back to February 2020.
If you were paying close attention, you might have noticed a few people talking about a virus
spreading overseas. But most of us weren't paying close attention. The stock market was doing great,
your kids were in school, you were going to restaurants and shaking hands and planning trips.
If someone told you they were stockpiling toilet paper, you would have thought they'd been
spending too much time on a weird corner of the internet. Then, over the course of about three weeks,
the entire world changed. Your office closed, your kids came home, and life rearranged itself
into something you wouldn't have believed if you described it to yourself a month earlier.
I think we're in the this seems overblown phase of something much, much bigger than COVID.
I've spent six years building an AI startup and investing in the space. I live in this world.
And I'm writing this for the people in my life who don't. My family, my friends, the people I care
about who keep asking me, so what's the deal with AI? And getting an answer that doesn't do
justice to what's actually happening. I keep giving them the polite version, the cocktail party
version, because the honest version sounds like I've lost my mind. And for a while, I told myself
that that was a good enough reason to keep what's truly happening to myself. But the gap between
what I've been saying and what is actually happening has gotten far too big. The people I care about
deserve to hear what is coming, even if it sounds crazy.
Here's the thing nobody outside of tech quite understands yet.
The reason so many people in the industry are sounding the alarm right now is because this
already happened to us.
We're not making predictions.
We're telling you what already occurred in our own jobs and warning you that you're next.
For years, AI has been improving steadily, big jumps here and there, but each big jump
was spaced out enough that you could absorb them as they came.
Then in 2025, new techniques for building these models unlocked a much faster pace of
progress. And then it got even faster and then faster again. Each new model wasn't just better than
the last, it was better by a wide margin. And the time between new model releases was shorter. I was
using AI more and more, going back and forth with it less and less, watching it handle things I
used to think required my expertise. Then on February 5th, two major AI labs released new models on
the same day, GBT53 Codex from OpenAI and Opus 4.5 from Anthropic. And something clicked,
not like a light switch, more like the moment you realize the water has been rising around you
and is now at your chest. I am no longer needed for the actual technical work of my job. I describe
what I want built, in plain English, and it just appears. Not a rough draft I need to fix,
the finished thing. I tell the AI what I want, walk away from my computer for four hours, and come back
to find the work done, done well, done better than I would have done it myself, with no corrections
needed. A couple of months ago, I was going back and forth with the AI, guiding it, making edits.
Now I just described the outcome and leave. Let me give you an example so you can understand what this
actually looks like in practice. I'll tell the AI, I want to build this app. Here's what it should do.
Here's roughly what it should look like. Figure out the user flow, the design, all of it. And it does.
It writes tens of thousands of lines of code. Then, and this is the part that would have been
unthinkable a year ago, it opens the app itself. It clicks through the buttons, it tests the
features. It uses the app the way a person would. If it doesn't like how something looks or
feels, it goes back and changes it on its own. It iterates like a developer would, fixing and
refining until it's satisfied. Only once it has decided the app meets its own standards
says it come back to me and say, it's ready for you to test, and when I test it, it's usually
perfect. I'm not exaggerating. This is what my Monday looked like this week. And here's why this
matters to you even if you don't work in tech. The AI labs made a deliberate choice. They
focused on making AI great at writing code first, because building AI requires a lot of code.
If AI can write that code, it can help build the next version of itself, a smarter version
which writes better code, which builds an even smarter version.
Making AI-grade at coding was a strategy that unlocks everything else.
That's why they did it first.
My job started changing before yours not because they were targeting software engineers.
It was just a side effect of where they chose to aim first.
They've now done it, and they're moving on to everything else.
The experience that tech workers have had over the past year,
of watching AI go from helpful tool to does my job better than I do,
is the experience everyone else is about to have.
Law, finance, medicine, accounting, consulting, writing, design, analysis,
customer service. Not in 10 years. The people building these systems say one to five years. Some say
less. And given what I've seen in just the last couple of months, I think less is more likely.
Now, the next section is Matt debunking the idea where people say, but I tried AI and it wasn't that
good. Matt says, I hear this constantly and I understand it because it used to be true. However,
he points out the time when that was true is ancient history. And what's more, the gap, one that
Ethan Mollick has talked about quite a bit, of the default free version that most people have access
to, is significantly behind the top-tier paid versions. Matt makes the analogy, judging AI based
on free-tier chat GPT is like evaluating the state of smartphones by using a flip phone.
Now, I'm skipping a bunch of parts of this because, frankly, you as an audience weren't even
necessarily exactly Matt's target. It's more your friends and family and peers who aren't listening
to the AI Daily Brief every day. Matt tries to put some context around how fast things are moving,
referencing the ongoing meter autonomy study. He talks about the fact that AI is now building the next
AI. Quoting the 5-3 Codex release where they wrote, GBT53 Codex is our first model that was instrumental
in creating itself. He then goes through a number of different professions, including legal,
financial analysis, writing and content, software engineering, medical analysis, and customer
service to share what he thinks the impact on those jobs might be. As he rounds the corner,
Matt has a section called What You Should Actually Do. Matt writes, I'm not writing this to make you feel
helpless. I'm writing this because I think the single biggest advantage you can have right now is
simply being early. Early to understand it, early to use it, early to adopt. His advice then is,
one, start using AI seriously and not just as a search engine, basically get the paid version,
use the best model available, and use it for hard things. Second piece of advice, he says this might
be the most important year of your career and work accordingly. Matt writes, the person who walks into
a meeting and says, I used AI to do this analysis in an hour instead of three days, is going to be the
most valuable person in the room, not eventually, right now. Once everyone figures it out,
the advantage disappears. Next, he says, have no ego about it. The people who will struggle the most
are ones who refuse it as a fad, who feel that AI diminishes their expertise, who assume their field
is special and immune. He has a bunch more, but then his final piece of advice is build the habit
of adapting. He says, this is maybe the most important one. The specific tools don't matter as much
as the muscle of learning new ones quickly. AI is going to keep changing and fast. The
models that exist today will be obsolete in a year. The workflows people build now will need to be
rebuilt. The people who come out of this well won't be the ones who mastered one tool. They'll be the
ones who got comfortable with the pace of change itself. Make a habit of experimenting. Try new things
even when the current thing is working. Get comfortable being a beginner repeatedly. That adaptability
is the closest thing to a durable advantage that exists right now. Matt concludes, I know this isn't a fad.
The technology works, it improves predictably, and the richest institutions in history are committing
trillions to it. I know the next two to five years are going to be disorienting, in ways that
most people aren't prepared for. This is already happening in my world. It's coming to yours. I know the
people who will come out the best are the ones who start engaging now, not with fear, but with
curiosity and a sense of urgency. And I know you deserve to hear this from someone who cares about
you, not from a headline six months from now when it's too late to get ahead of it. We're past
the point where this is an interesting dinner conversation about the future. The future is already here.
It just hasn't knocked on your door yet. It's about to.
So that is the piece.
And if you're not sure why it got so much traction, I think it crystallizes the sensibility
that people have been trying in fits and spurts to articulate for a couple months now since
the Opus 45 and GPT 5.2 models came out, but especially over the course of the early part of
2026, as we've really seen just how big a difference these models used well represent.
Now of course, 80 million people can't look at a thing without getting some serious critiques.
There is a healthy dose of personal invective aimed at Matt.
are dismissals via accusations of slop, basically saying that the ideas aren't legitimate
because they believe Matt used AI to write this 5,000-word tome.
Some people, I think, reasonably, don't love the COVID comparison, either because it feels too
abstract, or it feels too aggressively doom and gloom, or because structurally, a virus that
passes is different than a change that doesn't change back.
One of the most valuable critiques I think is the critique that basically says, other knowledge-work
problems outside of coding aren't as instantly addressable and as easily addressable by AI as
coding is. Isaac Saul writes, one thing I've noticed is that computer code is a really structured
language and software is a defined problem space with a lot of defined patterns. So software people
tend to think everything is a pattern and AI being really good at their job, makes them
overestimate how well it can do everything else. The truth is there is a lot more disorder on predictability
and humanness in so much of our lives and our work that I don't think AI applications will always
or even often be able to account for.
Matt, for instance, lists journalism as a job in trouble thanks to AI.
Not that our industry needs more trouble.
And it's true that AI can read documents fast and do incredible research
and even write clean copy and edit.
It will probably eliminate or reduce the need for some jobs.
But you know what it can't do?
It can't work a source over for years on end.
It can't, doesn't, and won't bear witness to live events.
It reminds me of the famous Goodwill hunting scene
where Robin Williams is chastising Matt Damon
about being such a smart ass but not being able to describe
what the Sistine Chapel smells like.
Damon is the AI.
Isaac concludes,
people think humans are finite numbers of neurons and processes and thoughts and learning.
But I think that is wrong.
We are all constantly changing every day, every second,
thanks to new inputs and new experiences.
So yes, I buy that AI will be able to read documents better than your typical lawyer,
but can it build a relationship with a client?
Or look at a jury and guess what argument might move them to guilty?
Or know when to cross the lines with a judge or when to step back?
I don't really think so.
And those limits to me are so under-discussed in this dialogue.
that it kind of discredits everything else.
Now, I do not agree with the idea that it discredits everything else,
but I do think that there is a lot in this critique that is worthy of consideration.
The particular type of criticism that I have no patience for
is the, well, actually AI isn't all that good.
Call this the Gary Marcus strand of criticism,
the folks who just simply cannot be convinced
that AI is as powerful as people say it is.
Now, one very highfalutant strand of this critique came from Will Minitas.
He wrote another very widely viewed post called Tool Shape Objects.
And for all the people ranting and raving about how good this one is,
I think it basically uses good writing to trick you into thinking it's made a point more profound than it actually has.
I think it actually secretly reveals something about the current state of work outside of AI entirely,
which has some big implications as well.
However, it was read enough that I think it's worth excerpting as well.
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The piece, as I said, is called tool-shaped objects.
Will writes,
In 1711, a toolmaker in Kyoto began forging Kana Blades for Carpenter's Boat.
building the temples at Higashi Honganji, the blades were forged from laminated steel.
The highest-quality white Haguein forged to soft iron and were extraordinary.
300 years later, his descendants still forged them.
A Chiazuru cost somewhere between $303,000.
It takes days to set up.
The dye must be hand-fitted.
The blade back flattened on a series of progressively finer stones.
The chip-breaking matted until light cannot pass between it and the edge.
Only then can you take a shaving.
The shaving curls are transcendent.
It is beautiful.
It is also, in the economic sense, worthless.
The power planer does the same work in a fraction of the time.
The kana exists so that the setup can exist.
I want to talk about a category of object that is shaped like a tool, but distinctly isn't one.
You can hold it, you can use it.
It fits in the hand the way a tool should.
It produces the feeling of work, the friction, the labor, the sense of forward motion.
But it doesn't produce work.
The object is not broken, it is performing its function.
Its function is to feel like a tool.
Now from there, Will does an again high-brow version of the slop critique.
Talking about Matt's piece, he said,
it was written or perhaps more precisely generated by Matt Schumer, the CEO of an LLM startup
that I couldn't immediately parse the function of from its various landing pages. What is interesting
is not that the essay is slop. What is interesting is that people consumed it. They shared it,
they engaged with it. Now, for those keeping track at home, that is a critique of generation,
a critique that it's slop, and an implication that because Will can't understand Matt's
startup, his opinion isn't really interesting. Will argues, they performed the act of reading
and distributing an essay about artificial intelligence that was itself produced by artificial
intelligence, and at no point in this loop did the output matter. The consumption was the product,
the sharing was the output. The essay, much like the AI it discusses, was a tool-shaped object,
and it worked exactly as designed. Will argues ultimately that, quote,
AI is everywhere in consumption and almost nowhere in output. We are spending unprecedented sums
to acquire, configure, deploy, and operate these systems. And the primary product of that
spending is the experience of spending it. Will argues that the current generation of
LLM-driven insanity, the billion-dollar frameworks, the orchestration layers, the agentic workflows, is
the most sophisticated tool-shaped object ever created. You can build an agent that reads your emails,
summarizes the contents, drafts a response, checks the response against the style guide,
routes the response through an approval chain, logs the interaction or reports the result to a dashboard.
You can watch this happen. You can watch the token stream. You can see the chain of thought.
You can monitor the system prompt. You can adjust the temperature. You can swap the model. You can add a
tool. You can add six tools. You can add a tool that calls another agent that calls a third agent that
searches the web and synthesizes the results into a memo that no one will read.
Now, Will caveats all of this and says that maybe at some point in the future, LLMs will be worth something,
but it'll take a long time to diffuse into the real economy.
I think that this essay might be one of the most condescending things I've ever read in my entire life,
and I think a lot of the people who are liking and sharing it are attracted to its clever condescension.
There are really two substantive arguments here.
I think the most revealing paragraph is the one that I just read about the agent that reads your email.
The argument that Will is trying to make is that all of this work,
All of this computation adds up to nothing because who cares about that memo?
The critique embedded in that, however, as much as Will thinks it's about the AI, is about the
nature of knowledge work in general.
In my quote share of the piece, I said tool-shaped objects is less of a rebuke when you realize
most work isn't about producing value, but instead producing work-shaped objects.
And the point that I was making is that it is true that a huge amount of the so-called
work that is done today is not valuable in any real sense.
one only needs to go back and look at the TPS reports from office space to see that this critique has been around for quite some time.
I do not think it follows that LLMs are fake tools because they are used in the service of what is ultimately not all that valuable work.
The second critique comes in the paragraph, but my narrow suggestion is that this diffusion into the real economy will take much, much longer,
and look much different than the current run on South Bay Best Buy for Mac minis would have you believe.
My response to that is, well, yeah, no shit Sherlock.
Jason Franik responded and got it exactly right, I think, when he said,
That was a lot of words to say AI adoption won't happen as fast as some would have you believe.
That in and of itself is slop. It's a nothing statement.
Jacob actually kind of went off a little bit in a separate post.
He writes, Will's essay is tangential slop about how some teams are burning tokens,
simply building productivity systems rather than doing work itself.
Meanwhile, anthropic execs are telling you that 100% of their code is written by LLMs.
That's actual effing work being done.
They're shipping dozens of features a month,
all of which would be impossible without clawed.
As the original essay points out,
software engineering is no longer a profession of writing code
and instead of orchestrating agents.
And writing code is just the start.
AI and robots will be coming for numerous other jobs soon,
so the author suggests that everyone start preparing
for what the world will look like when intelligence is commoditized.
That's it. That's all the original essay says.
And Will literally agrees with it.
He just suggests it will take longer than the original essay predicted.
Without referencing this conversation, Ethan Mollock actually summed up this back and forth.
He wrote,
It's a weird time to post about AI because a lot of people are vastly underestimating what AI can do
and how many large-scale impacts on work are inevitable with today's models,
while a lot of other people underestimate the real-world problems involved in getting value from AI.
I think Ethan is right, but let's look at the implications of being wrong
in each of the ways that Ethan suggests people are wrong.
The implication for being wrong about the speed at which,
which this AI diffuses across the workplace and society, is perhaps over-investment, it's some
extra time preparing when you could have used that time for other things, but ultimately you weren't
wrong about the thing, you were wrong about the timescale. Now let's talk about the implications
of being wrong about fundamentally underestimating what AI can do and not preparing. It could
literally mean on an individual or an organizational level, professional extinction. Not that it
will always be so, and I don't think anyone can purport to know how exactly the lines between
the AI haves and have-nots will shake out. It could be, and I hope it is the case, that there is
plenty of time for everyone to catch up and adapt, that the skeptics of today, if indeed they are
wrong, will have had time to be wrong, and still adapt whatever it is that they do for work to the
new reality without someone who wasn't skeptical and embraced AI out competing them. But I'm not
sure that that's going to be the case. The point, of course, is that the cost of underestimating
AI is a hell of a lot higher than the cost of overestimating it. And so many people are just unwilling
to change their priors. Now, one thing that gives me hope is that there are a lot of folks who are
not AI people who are becoming more palatable messengers. I had a political campaign recently
tell me that their biggest issue with AI is that the people who were building it were such a-holes.
And relative to their constituencies, I am sympathetic. But it turns out that the way that a technology
impacts your life has basically nothing to do with the personality traits of the person who built it.
In any case, as I was saying, there are increasingly groups of people who have credibility
with different audiences and constituencies who are not trying to sell AI products, who are trying
to convince people to look at it differently.
One very notable voice here is Derek Thompson, formerly of the Atlantic, and co-author of
Abundance, who has been pounding the pavement on this over on Twitter.
Recently, for example, he wrote, there are still a lot of journalists and commentators
that I follow who think AI is nothing of much significance, still just a mildly fancy auto-complete
machine that hallucinates half the time and can't even think. If you're in that category,
what is something I could write or show with my reporting and work that might make you change your
mind? I find that attitude and just the time that Derek puts into it, extremely optimistic.
Sequoia partner Pat Grady also does a good job of summing up my feeling about the Something
Big is Happening essay overall. He writes, Something Big is happening is, in fact, a marvelously
useful tool. It has served a real purpose. It has been a wake-up call for some 70 million people.
Those people are now more aware of what is coming and more likely to make the right choice.
Will you let AI wash over you or will you put it to work?
The best time to make that decision is right now.
And lo and behold, the longer the people have been talking about this piece, the better the conversation has gotten.
Connor Boyack wrote a follow-up about the scene and the unseen.
It's called AI isn't coming for your future. Fear is.
He writes, I'm not going to argue that those articles are wrong about everything.
AI is powerful. It is moving fast.
the disruption is real and I take the concern seriously.
But I'm going to tell you that the fear you're feeling right now,
that sinking sense that the rug is being pulled out from under you,
is one of the oldest and most consistently wrong reactions in human history.
It has a name, it has a pattern,
and it has a track record of being spectacularly, almost comically incorrect.
Not once or twice, like every single time.
Connor writes that the single idea, written over 175 years ago,
that is the master key to understanding every AI doomer headline you've ever read is this.
It's from Frederique Bastiat from 1850, when he wrote,
There is only one difference between a bad economist and a good one.
The bad economist confines himself to the visible effect,
the good economist takes into account both the effect that can be seen,
and those effects that must be foreseen.
Connor simplifies this to the scene and the unseen.
He writes, when a new technology arrives, certain effects are immediately visible.
You can see the assembly line worker whose job has been automated.
You can see the copywriter watching Grok produce in seconds what used to take her hours.
You can see the customer service team being replaced by a chatbot built with Claude
in a matter of minutes.
This is the scene.
It's tangible.
It's emotional.
It has a human face.
And it makes for incredible content because fear and loss are among the most powerful drivers
of engagement.
But there is a second category of effects.
The ones Bastiat said emerge only subsequently.
These are the unseen.
The new industries that don't exist yet.
The businesses that become possible only because cost of drop.
The creative work that gets unlocked when drudgery disappears.
the entrepreneur who can now build a loan what used to require a team of 20,
the consumer who now has access to something that was previously unaffordable.
The unseen is, by definition, harder to see.
That's the whole point.
And it's why the bad economist, or the bad forecaster or the panic-scrolling reader,
always gets it wrong.
They stare at the scene, extrapolate doom,
and completely miss the explosion of new opportunity forming just outside their field of vision.
Now, Connor goes on and gives lots and lots of evidence of this throughout history.
He can exit back to AI, talking about how the scene
effects are AI doing many of the tasks he used to spend hours on, while the unseen is being freed up
to do higher order work that he never had time for before. The real risk, he argues, is not AI,
it's mindset. Connor says, the people who will be harmed by AI aren't the ones whose current
jobs get disrupted. Disruption is temporary. People retool, pivot, and find new opportunities
as they always have. The people who will be genuinely harmed are the ones who adopt the fixed pie
mindset, the ones who see only the scene. The good thinker takes into account,
both the effects that can be seen and those that must be foreseen. He sees the jobs disappearing and
asks, what new thing is this making possible? Where is the unseen opportunity forming? What can I do
now that I couldn't do before? That question, what is this making possible? Is the most valuable
question you can ask right now? About AI, about your career, about your life. The knitting machine
didn't ruin England. It made it the wealthiest nation on earth. The power loom didn't destroy the
textile industry. It expanded it beyond anyone's imagination. The computer didn't end employment. It
created the modern economy. AI won't shrink your future if you refuse to let fear shrink your vision.
Now, this is very close to what I have always felt. I said it recently is that one of the
assumptions that AI nervousness rests upon is that there is a fixed amount of work in the world to
be done, and that if AI does a lot of it, humans won't be able to. My argument is that we will
always expand, that there is always more work to be done and more to be created based on that work.
I think the best concern to have with that view is that an optimism about where this all resolves
does not negate or change the fact that there can be utter carnage in the liminal period of transition.
And that is something we need to think about and account for.
Ultimately, though, as we wrap up for today, I agree with Pat that whether you think Matt's wrong,
whether you think he's just trying to hype things or sell you something,
the piece has provoked a conversation that almost everyone who has participated in is richer for.
They are thinking about and engaging with the issues.
They see that folks in the AI industry feel like something has shifted that they need to pay
attention to. Many died-in-the-wool skeptics will give it no heed. But for the majority of people
who don't know exactly how to feel, maybe it creates context to go try something new that they
wouldn't have before. Maybe they go try to vibe code something. Who knows? All I know is that it's
better to have the conversation than not. And so I think overall for the world, this was a very good week.
That's going to do it for today's AI Daily Brief. Appreciate you listening or watching. As always,
until next time, peace.