AI is collapsing futures and most of us

are missing what that really means. We

think collapsing as in destroying.

That's not what what I mean here.

Collapsing as in compressing is what

people are missing because AI is

collapsing multiple different dimensions

of our work lives into a single thread

pointing to the future. And we're we're

missing the deeper implications of that.

The first collapse is horizontal.

Engineer, product manager, marketer,

analyst, designer, opsley. These used to

be distinct career paths with very

distinct skill sets. They're all

converging now very quickly into a

single meta competency orchestrating AI

agents to get work done. If you cannot

do that, none of the rest of the domain

knowledge is going to matter in late

2026. And yes, I don't want to lose the

fact that we still have folks who have

10 years, 15 years experience in these

individual domains in front-end design

in being an operational lead in doing

deep back-end engineering. But you don't

have value there unless you can do the

orchestrating AI agents piece certainly

by late 2026, early 2027. That is how

fast this space is developing and I

don't think most of us are ready for it.

The second collapse is temporal. the

leverage you thought you could build

over the next five years. The way we've

been trained to think about career

ladders as these steady steps wait two,

three years next promotion, that

timeline is compressing into months. The

rate of AI capability improvement nearly

doubled in the last year and it's just

going to keep going faster. Both

collapses point to one conclusion. Now

is what matters. Not your 5-year plan,

not your eventual intention to get up to

speed on AI because the future keeps

arriving faster. Preparation means

engagement. And I'll add one more piece

here that I think is absolutely true

across everyone who engages with AI

productively.

This is an art you learn by doing. You

do not get to learn to ride a horse by

reading a book as a friend of mine

called out. You do not get to learn to

swim by sitting in a deck chair and

watching the ocean. You just got to get

in. And that's very true of AI because

it's an experiential technology. Let me

go a bit deeper onto the differentiation

between knowledge work roles because I

think a lot of times when you see oh the

knowledge work roles are collapsing.

They're all going to be the same. It

feels like a big claim. It feels like

it's overhyped.

Gartner's predicting that close to half

of enterprise applications will

integrate task specific AI agents by the

end of 2026. That's up from less than 5%

in 2025. It's absolutely exploding. It's

an eight-fold increase in just over a

year. 57% of companies uh as of 2025

claim to have AI agents in production.

Now those can have varying degrees of

competency. They the the direction is

nothing but it's exploding. So what this

means is that specific domain AI

expertise is going to be mediated

through these universal skills. The

differentiation is going to be whether

you can apply your marketing skills,

your engineering skills, your finance

skills, whatever it is in an AI

agentshaped way. Think about what a

product manager does today versus two

years ago. The job used to require

synthesizing customer feedback, writing

specs, coordinating with engineering,

managing stakeholders, and now

increasingly the job involves just

prompting models to draft spec and using

AI to analyze customer data. And you're

often now using agents to update

tickets. You're using agents to directly

build in production. Your entire job is

radically different. And that pattern

repeats across every function. Legal

teams using AI to review contracts are

compressing jobs that took weeks into

hours. Finance teams can now use clawed

in Excel to build projections that used

to take days. Customer success teams can

run AI agents that handle 80% of initial

inquiries or 90 or 95. There is going to

be a fundamental turnover of skills

across every one of these jobs families.

What used to be 50 different

specializations is going to converge

into variations on a single theme.

Humans directing AI with good knowledge

and good software-shaped intent toward

an outcome. I've talked about software

shaped intent before. I think it's one

of the biggest skills we're missing when

we direct agents. We need to think in

terms of what agents can deliver within

the technical ecosystem they occupy.

Where is the agent's tool set? Where is

the agent's memory? Where is the agent's

workflow? When I direct the agent to do

something, is it going to look

softwareshaped? As in, is it going to be

an interface that adequately reads and

writes data so that I can solve the

problem? Software is leveraged expressed

in silicut. Fundamentally, if you know

how software works, and so much of

software is just reading and writing

data and presenting it in a way that's

useful, if you start to think in those

terms, you're going to be able to apply

the specific domain knowledge you have

in design, in finance, in customer

success, and you're going to be able to

use AI agents more effectively. Even if

your job isn't building software, this

used to be a product only thing or an

engineering only thing. The idea that we

now work with agents is becoming

universal. And the idea that we have to

think in software terms is coming out of

the technical box. It's coming out of

engineering. It's coming out of product.

It's coming for all of us. And I want to

be clear, your expertise doesn't

disappear here. It just becomes

foundational rather than differentiating

by itself. You need to have great domain

knowledge to direct AI effectively. It's

part of how seniors compete in a world

where everyone has access to the same AI

tools. But you have to be able to

leverage that through AI. And I think

most people think of that still in terms

of their specific domain. We have this

sort of single lane focus. And what I'm

calling out is that we have a giant

bottleneck on skilling. Like all of our

skills are starting to converge around

this one gigantic meta skill of driving

AI agents. The second collapse I want to

talk about, I mentioned temporal

collapse. This is really important and

we keep missing it. career leverage is

compressing into the present moment

because AI is accelerating time.

Consider even just the Sweetbench coding

benchmark. AI systems could solve 4% of

problems in 2023 and they've essentially

solved the entire benchmark 2 years

later. I I don't know exactly what it's

going to be when you see this video, but

it's around 90 95%.

Sweetbench is saturated and the fact

that we saturated it is not even the

most important thing. The doubling time

to get that number up is shrinking. AI

progress is accelerating. Traditional

career planning assumed you had the

time. Learn a skill, apply it for years,

build expertise, get promoted,

eventually learn that expertise and

figure out how to leverage it in

leadership. That timeline gave you a

sense that you could plot out your

growth over time and get some breathing

room. You could be strategic about when

to invest your learning energy. And that

assumption, if you take it at face value

like you could in the 2000s and 2010s,

that's now catastrophically wrong

because you have to assume a career path

where AI is gaining speed ever more

rapidly. And this creates a really tough

dynamic for career planning. I don't

want to sugarcoat that. The skills that

will matter in 2027 are being defined

now by people engaging now. If you wait

until the tech settles down, you're

going to find that the early adopters

have already built the workflows,

established the norms, and captured the

opportunities that you were waiting for.

They'll have two years of compound

learning while you're still figuring out

the basics. So, there is I cannot

promise you a way to settle down. This

is a chaotic period. There is no mature

state to wait for. There is only a

continuously steepening curve and it's

going to reward folks who can climb in

early and go faster. I compare AI to

riding a bike. If you are going slow on

a bike, it's really hard to balance and

you feel like you're never going to

catch up. But experientially, when you

go faster on a bike, the steadiness

increases. The way to balance gets

easier. And kids have so much trouble

learning this. They think if they go

slower, they'll be safer. But they're

actually safer going faster. And that is

what you have to learn with AI. You're

actually safer leaning in and going

faster than you are going slower because

slower forces you to constantly think

about breaking and stopping and slowing

down and figuring out how you can adjust

and work this into your existing

workflow. And I see so many of us acting

like kids on a bike for the first time.

We're just trying to figure out how to

go very slowly. I got to say AI is going

too fast for that. You got to get on the

bike and go as quick as you can because

that's the easiest way to balance. Like

people ask how I keep up. It's because

I'm going pretty fast on the bike and it

feels really steady. The old career

model assumed your expertise appreciated

over time. You would learn something

valuable. It would stay valuable

gradually. It would compound. The new

model is really different. Your

expertise atrophies. It depreciates

unless you continuously update it. And

the depreciation rate is accelerating

because AI progress is going faster. I'm

not trying to argue for panic here. It's

an argument for continuous engagement.

The people who are thriving now are not

the ones who just go to an AI class and

master it once and then coast. They're

the ones who develop the meta skill of

continuously learning and adapting as

the tech evolves. The halflife of any

given piece of specific AI knowledge is

short and it's getting shorter. The

halflife of the learning habit around AI

is getting longer and more durable. If

you doubt the magnitude of what's

happening, follow the money. This is the

biggest capex project in human history.

Big tech's combined AI capital

expenditure was close to half a trillion

dollar in 2025. It's going to be well

over half a trillion in 2026. And in

total, the big five, Amazon, Microsoft,

Google, Meta, Oracle plan to add more or

less at least $2 trillion in AI related

assets in the next four years. This is a

tremendous amount of operational

investment in what these companies

believe is the future. The money is

committed. AI is happening and is going

to define the next era of computing so

thoroughly that we have got to

understand that there is no other way

there. The only way out and through is

AI. And that's what I mean when I say

it's collapsing timelines and

compressing career trajectories. There

is no other way through the career path

that does not include AI. And things get

uncomfortable at that point. Many people

are resisting and you don't have time to

resist. If you tried chat GPT in 2022

and said it hallucinated and you just

left it, you don't have time for that

anymore. You don't have time to say I'll

wait it until it matures. Like that's an

that's like sitting by the bike and say

I'll wait till it gets steadier. It's

not going to get steadier. You don't

have time to say my job is immune. I got

news for you. It's not immune. Anytime

you are touching a computer, you are

touching AI. That's how pervasive it's

going to be in the next year. And to be

clear, for people who are saying, I want

to exit the ride. I want to stop a tech

career. I have respect for that. And I

know folks who have said, I had my

career. I think I'm done. I want to open

a bookshop. I want to go and start doing

carpentry. That's fine. That's great.

That is something that you can choose to

do intentionally. I think that's a much

more productive choice than trying to

stay in the industry that is converging

on AI and trying to resist that. that's

just not going to go well and it's going

to make everybody including you kind of

miserable. And so if you really think AI

is not for you, I think the best thing

you can do is pick that alternate career

path that takes you away from the screen

because if you're going to stay in

fields touched by AI, which is

increasingly everything to do with a

computer, you're going to have to

engage. I want to close by giving you

some encouragement. It is easy to look

at this and to be doom and gloom. It is

easy to say, I did not make the choice

for AI. I would argue none of us did.

The industry as a whole made that choice

and we are all living through this

moment together. We did not choose to

compress timelines. We did not choose to

compress career paths. It's happening

for us. And I have seen over and over

again that when people recognize that

and when they choose to say even though

I didn't get to pick this, I am going to

choose to engage with AI with curiosity.

I'm going to choose to engage AI and

learn to ride the bike. I'm going to

lean in as far as I can lean in even if

I'm not quite sure. that is going to get

you so much farther. It is going to get

you an accelerated rate of learning.

You're going to be less overwhelmed.

Curiosity literally opens up your brain.

And we need openness to this AI world if

we want to be able to shape it in a way

that works for us. And I've seen

numerous dozens and dozens and dozens of

examples where people have chosen that

positive path. They've chosen to lean in

in widely differing fields. healthcare,

tech, finance, engineering, product.

I've even seen folks lean in on like

small town community building in AI. And

without exception, that choice to

positively lean in has taken them

farther. And so, if I can leave you with

anything in the middle of a timeline

that feels like it's increasingly wild

and unpredictable, it's just an

invitation to get on the bike with AI.

You got to go faster. And you've got to

be able to believe that if you lean in

and pry, if you jump in and you say,

"All right, I'm going to I'm going to

try something new. I'm going to try

Claude Code." Whatever it is that's new

to you, right? I'm going to try lovable.

I'm going to try a different way of

working with my chatbot. Great. And then

do the next thing. And then lean in a

little farther. And then lean a little

farther. And you're going to go faster

and faster and faster and faster. And

it's going to feel steadier over time

because you're going to pick up how AI

works across all of these systems in

your unconscious brain. The patterns

will start to solidify and you're

basically learning to work with this new

piece of technology in a way that feels

very stable over time. And so if I can

encourage you with anything is that

going faster is safer and less scarier

with AI than going slower. Cheers.