Speaker 1: If artificial general Intelligence, I mean real AGI truly lands
this year here in twenty twenty six, the world isn't
going to slowly adapt. No, it's not going to have
a series of you know, town hall meetings or some
gradual rollout period where we all learn the new buttons. No,
the analysis we're looking at today says the world is
going to flinch.
Speaker 2: That is such a visceral image. Isn't it a flinch?
It's a physical reaction?
Speaker 1: It totally is it stuck with me? I was reading
through this, this incredible analysis on the arrival of AGI,
and it's focusing specifically on the timelines for this year,
and that one word just hung in the air. Flinch.
Speaker 2: Yeah.
Speaker 1: You know, usually when we talk about big tech changes,
we use these really soft, kind of corporate comfortable words.
Speaker 2: Oh yeah, synergy integration exactly.
Speaker 1: We talk about adoption curves, we talk about gradual integration.
But the source where Unpacking the Day just says, forget
all that. If AGI arrives with the speed, the sheer
velocity that the data suggests it might, and it's pointing
right at twenty twenty six as the tipping point, society
doesn't have time to.
Speaker 2: Adapt It just reacts.
Speaker 1: It reacts physically, It flinches, and.
Speaker 2: We need to be really, really specific about what that
reaction means, because when you flinch, it isn't a calculated
decision at all. You don't sit there and weigh the
pros and cons field in your face. It's involuntary. It's
a shock to the nervous system. And the argument here
isn't that we flinch because machines suddenly, you know, wake
up and start demanding voting rights. The sci fi stup,
that's the Hollywood version. The source is arguing we flinch
because of speed, just pure unadulterated velocity, exactly.
Speaker 1: It's the physics of the situation. It's about momentum, isn't it.
Speaker 2: It is our systems. I'm talking about our laws, our
corporate workflows, even just our day to day human expectations.
They all have a speed limit. They have a kind
of metabolic rate and agi it just doesn't.
Speaker 1: Welcome to thrilling threads. It is Sunday, February first, twenty
twenty six, and we are sitting right here in the
thick of it. The source material we're unpacking today isn't
looking at some distant you know, far off future. It's
looking at the calendar right now. It is arguing that
twenty twenty six is the year the rubber meets the road.
And honestly, you just have to look at the headlines
this week. The smoke is already starting to rise.
Speaker 2: It really is. And our mission today is to really
dissect this argument that the disruption of AI it isn't
really about intelligence and the way we usually think about it. Okay,
the source calls it a timing problem.
Speaker 1: A timing problem. I love that phrasing. I want to
drill into that because usually when I talk to people
about AGI, you know, at dinner parties or just with
friends of the industry, they want to get into philosophical stuff.
Speaker 2: Sure, is it conscious? Is it smart? Does it have feelings?
Can't write a better novel than Hemingway. But you're saying,
or this analysis is saying, that those are completely the
wrong questions to be asking right now.
Speaker 1: They're interesting questions, for sure, they're great for a late
night to eight with a glass of wine, but they
aren't the dangerous questions. The danger the actual shock it
comes from misalignment.
Speaker 2: Misalignment, I can.
Speaker 1: Go out the internet that was a massive technological shift right,
probably the biggest of our lifetimes until.
Speaker 2: Now, but it rolled out over decades. You have the
nineties with dial Remember that awful screeching noise.
Speaker 1: Vaguely, I just remember waiting like five minutes for a
single picture to load exactly.
Speaker 2: Then you get broadband in the two thousands, then the
whole mobile revolution with the iPhone. The point is we
had thirty years to adjust our laws, our manners, our businesses.
Speaker 1: We had a buffer zone.
Speaker 2: We had a buffer zone precisely, we had time to
figure out what email etiquette was before we had to
deal with social media algorithms rewiring our brains. It was
a gradual absorption.
Speaker 1: Okay, I see where this is going.
Speaker 2: But technologies that arrive compressed into a few years, that's
a different beast entirely. That stresses everything until it breaks.
If AGI lands in twenty twenty six, and the metrics
suggests it's landing right now, it lands fundamentally misaligned with
everything we've built.
Speaker 1: Misaligned with our incentives, our legal.
Speaker 2: Structures, our workflows, our expectations, everything. And here's the kicker,
And this is the part that I think keeps the
researchers themselves up at night. It doesn't need to be
perfect to break things.
Speaker 1: That's the good enough principle, right, I've heard that.
Speaker 2: Yes, it just needs to be good enough, cheap enough,
and fast enough to be trusted at scale. Once you
hit that trifecta, the disruption happens whether the machine is
conscious or not.
Speaker 1: So it's not about the soul of the machine.
Speaker 2: The machine doesn't need a soul to take over the economy.
It just needs to be faster than the person trying
to regulate it.
Speaker 1: Okay, let's unpack this because we have a lot to
get through. And frankly, some of this is well thrilling
is in the name of the show, but it's also
a little terrifying.
Speaker 2: It's both.
Speaker 1: Here's our roadmap for this discussion. First, we're going to
look at the evidence. Why twenty twenty six. Why isn't
this just another random guess. Then we're going to talk
about the quiet shift, this move from AI that assists
us to AI that acts for us.
Speaker 2: That's a critical distinction, the move from assistant to agent.
It changes everything huge.
Speaker 1: Then we're gonna look at what breaks first, and here's
a spoiler alert for you. It's not jobs, at least
not in the way you think. And finally, we're going
to talk about the human tool specifically. This this fascinating
and slightly depressing concept the source calls learned irrelevance.
Speaker 2: It's a heavy concept, but it's so essential to understand.
I think it changes how you view your own workday.
Speaker 1: I think so too. So let's jump into part one,
the evidence why twenty twenty six. For years, I mean
literally for years, every time I read a report on AGI,
the date was always ten years.
Speaker 2: Away, always just over the horizon.
Speaker 1: It was a fusion energy, always promising, never quite delivering.
But this source says twenty twenty six different. It's different
because progress is now compounding, not just inching forward.
Speaker 2: That's the key word, isn't it compounding. When you look
at the metrics that are laid out in the source material,
it's not just that the charts are going up. Everyone
knows the charts are going up. It's that the slope
of the line is changing.
Speaker 1: It's getting steeper.
Speaker 2: It's getting steeper. We aren't seeing linear growth anymore. We
are seeing exponential growth in reasoning, in memory scaling, and
in reliable planning.
Speaker 1: Okay, can we pause on reasoning? That word gets thrown
around a lot in marketing materials. What does actual reasoning
look like in the models we are seeing now in
early twenty twenty six, compared to the chatbots of say,
twenty twenty three or twenty twenty.
Speaker 2: Four, it's night and day. It really is. Back in
twenty twenty four, reasoning largely meant very sophisticated pattern matching. Okay,
you'd ask a question and the model would predict the
next likely word based on the vast ocean of text
it had read. It was a kind of mimicry that
looked like thought. I used to call it a parrot
with a library card.
Speaker 1: Right. It could write a nice poem, but it couldn't
plan a complex logistics route without just making stuff up hallucinating.
Speaker 2: Exactly Today, the models we're seeing can perform multi step deduction.
They can look at a problem, break it down into
twelve subcomponents, solve each one of those individually, and then
this is the important part, check their own work for errors,
and then synthesize the answer. That's not mimicry, That isn't
just better writing. That is a fundamental shift in cognitive architecture.
It's the difference between memorizing a math textbook and actually
understanding calculus.
Speaker 1: And tool use. That was one that stood out to
me from the source. It used to be that getting
an AI to use a calculator or browse the web
was this hacky, experimental thing.
Speaker 2: Yeah, you had to kind of coax it, you had
to prompt it perfectly.
Speaker 1: Please mister robot, use the search tool now.
Speaker 2: But now it's default, it's baked in, And that shift
from experimental to reliable is what completely changes the adoption curve.
The source highlights are really staggering statistic on this about
enterprise adoption. I saw that in just a couple of years,
we went from about fifty percent of companies kind of
playing with these tools.
Speaker 1: Right, setting up a sandbox, letting the interns mess around
with it exactly.
Speaker 2: That we went from that to nearly four out of
five companies deply integrating them into core business processes.
Speaker 1: Four out of five. That is eighty percent of the
market in two years.
Speaker 2: And we are not talking about employees using chet GPT
to write a more polite email to their boss. No,
this is real work. This is writing production code that
ships to customers. This is analyzing thousands of pages of
legal contracts in minutes. This is supporting R and D teams.
And the source makes this point really clearly, that kind
of acceleration doesn't happen because a tool is fun.
Speaker 1: No capital is ruthless.
Speaker 2: It is Companies don't overhaul their entire workflow for a toy.
They do it for a tool that directly impacts the
bottom line.
Speaker 1: And speaking of bottom lines, that brings it to the
money right the compute signal.
Speaker 2: This is the ultimate financial reality check. If you really
want to know what the future holds, don't look at
the press releases, look at the bank accounts, look at
the CAPPAC spending.
Speaker 1: Where are they putting their billions?
Speaker 2: Exactly? And the cost of training these single frontier models
has just ballooned. We're talking about single training runs costing
hundreds of millions of dollars.
Speaker 1: Just to put that in perspective for everyone. That used
to be the annual budget for entire national AI.
Speaker 2: Programs, yes, for a whole country.
Speaker 1: Now it's the receipt for one experiment exactly.
Speaker 2: And the logic here is brutal, but it's crystal clear.
Nobody spends hundreds of millions of dollars on a single
training run unless they strongly believe that capability is about
to compound.
Speaker 1: Again, You're not doing that for a small bump.
Speaker 2: You don't make that bet for a five percent improvement.
You don't build a billion dollar data center just to
get a chatbot that makes slightly better haikus. You make
that bet because you're expecting a paradigm shift.
Speaker 1: It really feels like the ground is shifting under our feet.
The source also mentions that even the researchers, the people
inside the labs building this stuff, are the ones who
know the code best, they're admitting that the timelines feel unstable.
Speaker 2: That is a very unsettling word for a scientist to use. Unstable.
Scientists like predictability they do.
Speaker 1: It gives me this image of walking on a floor
that's suddenly tilting. You thought you knew the angle, you
thought you had your balance, and then suddenly the slope
gets way steeper without anyone warning you.
Speaker 2: And that instability is coming from the fact that the
benchmarks keep getting rewritten. We set a test for the AI,
you know, say, passing the bar exam or solving the
math data set, thinking it'll take five years.
Speaker 1: To be it takes five months or five.
Speaker 2: Weeks, so we write a harder test that takes five days.
The predictability of the science is breaking down because the
subject of the study is evolving faster than the study itself.
Speaker 1: Wow. Okay, So if twenty twenty six is the year,
and the money, the tech, the adoption rates all suggest
it is, how does it actually hit us? Because the
source argues it won't be some big press conference. We
aren't going to get a Hello Imagi tweet.
Speaker 2: No, And that brings us to the quiet shift we
need to discus. History tells us that the biggest shifts
always arrive quietly and then all at once. Right, and
the source identifies a very specific technical shift that changes
the entire risk profile. It's this move from assisting to acting.
Speaker 1: This is part two the mechanism of shock, and I
think this is the single most important technical takeaway for
our listeners. Old AI, and by old I mean like
two years ago, which is just crazy to say.
Speaker 2: It's insane and eternity.
Speaker 1: In AI time, old AI was passive.
Speaker 2: Right, It waited you typed in a prompt to give
you an answer, and then it just sat there frozen
until you poked it again. It was a completely reactive loop.
Speaker 1: But the new systems, the ones rolling out right now
in twenty twenty six.
Speaker 2: They're active agents. They decide. They don't just answer a question.
They break goals into steps. They choose which tools to use.
If they try a method and it fails, they retry
with a different one. They keep context over long periods.
They don't just assist, they act.
Speaker 1: Let's give a really concrete example of this, because I
want people to feel the difference in their bones.
Speaker 2: Okay, perfect. Let's take booking a corporate trip. The old AI,
you ask find me flights to Tokyo. It gives you
a list of flights.
Speaker 1: That's it, and then the work starts for you.
Speaker 2: Yes, you have to read the list, pick one, go
to the airline's website, type in your credit card, then
go to the hotel booking site. Do that, then open
your calendar and put it all in. The AI was
basically a fancy search engine, right, it was a librarian.
Speaker 1: It handed you the book, but you still had to
read it and do all the work.
Speaker 2: Exactly Now, the agentic AI of twenty twenty six. You
say one thing, get me to Tokyo for the conference
next Tuesday, And what happens The agent checks your calendar
for the dates, It knows your airline loyalty numbers, It
navigates to the airline site on its own, books the
ticket using your corporate card on file, finds a hotel
that matches your stored preference for having a gym, books that.
Then it emails the team in Tokyo to let them
know when you land, and puts the final itinerary on
everyone's calendar.
Speaker 1: And you did nothing but give the initial command.
Speaker 2: Nothing. That sounds incredibly useful.
Speaker 1: It sounds amazing, but also slightly ominous.
Speaker 2: It is incredibly useful, but it creates what the researchers
in this space call speed asymmetry. And this right here
is the core mechanism of the flinch.
Speaker 1: Okay, so define speed asymmetry for us.
Speaker 2: It's simple math. Really, machines iterate in seconds, institutions and
humans iterate in months or years. When you have an
agent that can act, iterate, retry, and deploy a new
strategy in the span of a few seconds, and you
pair that with a human oversight system that requires a
weekly meeting to approve a single decision, you have a fundamental,
dangerous disconnect.
Speaker 1: The source used this great analogy for that. It's like
trying to fact check a fire hose with a clipboard.
Speaker 2: It's a perfect visual, isn't it. You're just standing there
with your little pen and your piece of paper try
and tick boxes, and meanwhile you are being blasted in
the face with gigabytes of executed decisions per second.
Speaker 1: And we are already seeing this play out, aren't we.
The source mantions code shipping.
Speaker 2: Absolutely, we have AI generated code being pushed to production
repositories faster than human teams can possibly review it. Imagine
a software team. The human engineer writes what one hundred
lines of good code a day if they're lucky. Yeah,
the AI agent writes ten thousand lines. Who is checking
the ten thousand lines?
Speaker 1: Nobody?
Speaker 2: Nobody can. They're running automated tests. Sure, but the deep
conceptual understanding, the intuition of a senior developer looking at
it and saying this feels wrong, that's gone.
Speaker 1: And we're seeing automated recommendations in business for pricing, for
marketing being accepted almost blindly, simply because slowing down to
check them feels too expensive.
Speaker 2: That is the good enough danger we talked about, but
now supercharged with agency AGI doesn't need to be flawless
to be destabilizing. It just needs tempo. If the machine
is right ninety percent of the time and it moves
one hundred times faster than you, you eventually stop checking
the ten percent. You just can't keep up. You let
it run so small errors, small biases, they start to
scale up, but you don't know it them until it's
far too late because you've lost the ability.
Speaker 1: To audit the stream. Okay, so we have this high
speed agenic AI and we have these slow legacy human systems.
This leads us right into part three. What breaks first?
Because the common narrative, the one I hear at every
single dinner party, is oh, the robots are coming for
our jobs math unemployment. That's going to be the first sign.
Speaker 2: And that's a valid long term concern, of course.
Speaker 1: Huh.
Speaker 2: But the source argues very convincingly that it is not
the first thing that breaks. The first casualty is coordination.
Speaker 1: Coordination. Explain that.
Speaker 2: Think about how a company is built, how any organization
is structured. It's a hierarchy built on the assumption of
human speed. We have meetings to synchronize, we have approval chains,
we have escalation paths. That entire structure relies on the
assumption that every part of the system moves at roughly
the same pace.
Speaker 1: Right, I email you, You email me back a day later.
We have a meeting next Tuesday to.
Speaker 2: Decide exactly The whole thing chugs along at a human pace.
But now you insert a component, an AI agent that
moves one hundred, maybe a thousand times faster than the
rest of the org chart, the whole structure just starts
to wobble.
Speaker 1: Can't handle the strain.
Speaker 2: It's like putting a Ferrari engine in a bicycle. The
frame can't handle the torque. The management layer completely loses visibility.
How so well, Decisions that used to take days of
meetings and reports now happen in minutes autonymously. Outputs that
used to trickle out over a week arrive all at
once in a giant data dump. The manager isn't firing
people yet. The manager is just sitting there, completely overwhelmed,
realizing they no longer understand how the work is actually
getting done.
Speaker 1: The source brought up a specific example here that I
found really grounding, a tool called overseer os.
Speaker 2: Yes, this is a prime example of that assisting to
acting shift in the real world.
Speaker 1: So for our listeners who haven't seen this, can you
explain what overseer os does.
Speaker 2: It's a system designed to reverse engineer YouTube channels, but
it doesn't just give you a chart of views. It
goes deep breaks down exactly what's working in their videos,
the hooks, the pacing, the topic selection and its surfaces
patterns instantly and practically hands you a full content strategy and.
Speaker 1: The comparison the before versus after that the source laid
out is just nuts.
Speaker 2: It is previously that kind of analysis deeply studying a competitor,
analyzing their audience retention, their thumbnail strategy. That would take
a team of human analysts weeks.
Speaker 1: To compile weeks of work.
Speaker 2: They'd have to watch hours of video, take notes, build spreadsheets,
have multiple meetings to discuss findings. Overseers does a more
thorough job in minutes minutes, So the role of the
analyst didn't immediately disappear, but the oversight of that analysis
lagged catastrophically behind the execution. The human manager receiving that
report can't possibly verify all the data points in the
two minutes it took to generate them.
Speaker 1: They just have to trust it.
Speaker 2: They have to, and that leads directly to this concept
the Source calls the phantom organization.
Speaker 1: It's a haunting term.
Speaker 2: It is. The ORG chart on the wad wall looks
exactly the same. The headcount in the HR system is
the same. Everyone is still showing up to the office
or logging on from home, but the humans are no
longer steering the ship.
Speaker 1: They're just passengers.
Speaker 2: They're just rubber stamping the machine's output because the machine
is moving too fast for them to actually provide meaningful
direction or oversight.
Speaker 1: I feel like we've all been in that one meeting
where you just say yes to something because you didn't
have time to read the fifty page report they sent
an hour before. Right now, I imagine that happening fifty
times a day for every major decision.
Speaker 2: That is the coordination collapse. You lose control, not because
you're lazy or incompetent, but because you are fundamentally outpaced.
Speaker 1: And this transitions perfectly into Part four, the economic and
human toll, because if companies are running on this phantom autopilot,
what does that do to the broader economy? The Source
predicts something it calls a silent crash.
Speaker 2: This is a fascinating idea because it challenges our traditional
view of a recession, usually a crash that means GP
goes down, stock markets tank, everyone.
Speaker 1: Panics, yeah, red arrows everywhere.
Speaker 2: But the source argues, we might see an economic shock
without a recession, so GDP.
Speaker 1: Could actually go up. We could look prospers on.
Speaker 2: Paper, it could skyrocket. Productivity is going through the roof
because of these agents. But the value the profits from
that productivity, the source argues, it concentrates heavily and very
quickly on those who control the speed.
Speaker 1: Speed based monopoli exactly.
Speaker 2: We used to worry about monopolies based on who owns
all the resources like oil. Then we worried about monopolies
based on network effects, like who has all the users
on their social platform. This is a new kind of
monopoly based on pure tempo right. The companies that can
price their products, ship their goods, and negotiate their contracts
faster than their rivals win every single time. It stops
being about acting better. It's about acting before the competition
can even finish processing the data.
Speaker 1: It's like high frequency trading, but for everything.
Speaker 2: For every applied to logistics, applied to marketing applied to
hiring applied to R and D, and the disconnect there.
The social problem is that while productivity is rising, we're
making more stuff faster. Wages for the average person don't
necessarily follow the games flow to the margins, They flow
to the system owners. They flow to the people who
own the AI that's moving at light speed.
Speaker 1: So the economic pie gets bigger, but the slices get
impossibly thinner for the average worker.
Speaker 2: Or disappear entirely, and that leads to the psychological toll.
This is the part of the analysis that I found
most profound. The concept of learned irrelevance.
Speaker 1: This one hit me hard when I read it. I
think a lot of people are feeling the edges of
this in their jobs already, even if they don't have
a name for it.
Speaker 2: It's distinct from a more well known term, learned helplessness.
Learn helplessness is when you feel like you can't do
anything to change your situations. You give up, you stop trying.
Speaker 1: Okay, so learned irrelevance is different.
Speaker 2: It's more subtle and maybe more insidious. You still show
up to work, you still participate in the meetings, you
are still employed, but you realize, deep down in your gut,
that your input, your opinion, your experience carries less and
less weight simply because you are too slow.
Speaker 1: It's the erosion of human intuition.
Speaker 2: Yes, that's our superpower, right. Humans rely on gut feelings,
We make decisions based on incomplete information and years of
pattern matching in our heads. But AI relies on comprehensive data.
And when the machine is consistently right, or at least
consistently faster and good enough, you start to second guess
your own gut.
Speaker 1: You hesitate. You think, do I override the system's recommendation?
Do I trust myself? I mean, the machine analyzed ten
million data points in three seconds, and I.
Speaker 2: Just I have a hunch precisely, And that hesitation creates
decision paralytus. Do I overcare the system? Do I trust myself?
And eventually you just stop fighting it. You defer to
the machine, not because you were fired, not because you
were forced to, but because arguing with a system that
operates at the speed of light just feels pointless.
Speaker 1: It's a quiet form of disengagement. It's not a loud panic.
It's a slow fading away. You become a spectator. In
your own job.
Speaker 2: That is the human lag. We stop knowing what is real,
what is earned, what was a human idea versus a
machine suggestion, And that creates a crisis of meaning that
is much much harder to solve than a crisis of employment.
Speaker 1: And naturally, when we feel this profound loss of control,
we look for a savior. We look to our institutions,
We look to the government. We say, hey, there should
be a law slow this down. Of course, but part
five of our discussion today explains why the source is
so pessimistic that institutions can save us.
Speaker 2: Yeah, it all comes back to the clocks, the fundamental
mismatch of clocks.
Speaker 1: Election speed versus compute speed.
Speaker 2: Think about the speed of a democracy. It is designed
to be slow. That is a feature, not a bug. Right.
We have election cycles every few years. We have public consultations,
we have legal reviews, we have budget cycles. These things
take years or best months.
Speaker 1: In AI, speed.
Speaker 2: Training cycles for new models are measured in weeks. Deployment
cycles for new applications are measured in days or even hours.
By the time a regulator sits down to draft a
rule to regulate a specific AI behavior, that behavior has
already shifted. The technology has changed three times since the
committee was even formed.
Speaker 1: The source used the social media precedent here, and it's
a really chilling comparison.
Speaker 2: It's the perfect historical analogy. Look at social media regulation.
It arrived after our attention spans were rewired. It arrived
after our politics were fundamentally reshaped. It arrived a decade
after the damage was done.
Speaker 1: We are always always fighting.
Speaker 2: The last war, and with AI the timeline is even
more compressed. We don't have a decade of a wild
West social media era before the rules come in. We
might have eighteen months. And there's another trap here. The
source points out the global coordination trap.
Speaker 1: This is the classic prisoner's dilemma of AI. Right.
Speaker 2: It is if one country, say the US or the EU, says, okay,
we are going to tap the brakes. We are going
to slow down development to ensure safety and alignment.
Speaker 1: What happens another country season opportunity and speeds up exactly.
Speaker 2: Nobody wants to fall behind. Nobody wants to be the
nation that missed the Industrial Revolution two point zero because
they were too cautious. So even if policymakers understand the risks,
and many of them do. The global incentive structure forces
everyone to keep their foot on the gas.
Speaker 1: So to sum it all up, we have a technology
that moves at the speed of light, institutions that move
at the speed of bureaucracy, and a global competitive environment
that punishes anyone who tries to slow down.
Speaker 2: It really does feel like a timing collapse.
Speaker 1: That's the summary of the whole situation, isn't it. The
disruption of twenty twenty six isn't about a robot waking
up and saying hello. It's about the collapse of timing.
We're playing an old game, a game of careers and
laws and gradual change, while the rules of the game
are being rewritten in real time by a player who
moves a million times faster.
Speaker 2: The damage comes from that misalignment.
Speaker 1: Yes, between human systems, which are gradual and linear, in
AI systems which are sudden and exponential. When those two
lines on the grapt diverge far enough, the system snaps.
Speaker 2: That is the flinch.
Speaker 1: It's a sobering realization. We started this episode talking about
twenty twenty six as if it's some date in the future.
But if this analysis is right, these mechanisms are already
grinding into gear all around us.
Speaker 2: And as we wrap up, there is one final thought
the source leaves us with, and it's a provocative one.
Speaker 1: It's that we.
Speaker 2: Won't all agree on when the moment happened. We're not
going to get a notification on our phones that says
AGI has now arrived.
Speaker 1: There's no starting gun.
Speaker 2: No, we will only agree afterwards that something fundamental shifted.
It'll be in hindsight.
Speaker 1: The shock doesn't announce itself with a press release.
Speaker 2: It arrives quietly in the code, in the workflows, in
the silence of a manager who doesn't check a report
anymore because they just don't have time. It arrives quietly,
and then it's all at once.
Speaker 1: That is well, it's a lot to process, but that
is why we do this show, to try and look
the future in the eye, even when it's moving faster
than we are.
Speaker 2: Absolutely, awareness is the first step to navigating this kind
of speed.
Speaker 1: So here's my question for you, our listener, as you
sit here with us in February twenty twenty six, looking
at this speed asymmetry that we've been talking about, do
you feel learned irrelevance creeping into your own.
Speaker 2: Work That's the real question, isn't it.
Speaker 1: Are you steering the ship or are you just rubber
stamping the speed? Are you the analyst with the insight
or are you becoming the clipboard holder in front of
the fire hose.
Speaker 2: We really want to know what you think.
Speaker 1: Let us know your stand in the comments. Are we
ready for the flinch? This has been thrilling Threads. Thanks
for listening and good luck out there.
Speaker 2: Stay curious and try to stay fast.