A week or so ago in Davos, Switzerland,

Elon Musk told the World Economic Forum

that we're approaching quote abundance

for all. Ubiquitous AI, ubiquitous

robotics, everything's going to be

great. An explosion in the global

economy, quote, truly beyond all

precedent. He recommended we not save

for retirement. Meanwhile, Dario Amade

predicted half of white collar jobs

would disappear. But apparently that's

good because the abundance is just going

to be everywhere. Look, the abundance

narrative was everywhere at Davos.

echoed through every panel, every

fireside chat, every opra ski

conversation. But I want to suggest to

you that the abundance economy is

probably the wrong frame for most of us

to think about the next few years. And

instead, we should think about the

bottleneck economy. It's much more

practical, much more likely to getting

you employed, and much more likely for

you as a builder or a company leader to

find ways to succeed in the AI economy.

So, let's talk about bottlenecks.

Cognizant released telling research on

AI claiming that it could unlock could

is the keyword and a half trillion

dollars in US labor productivity. But

there was a massive caveat that no one

paid attention to from that research.

The value will only materialize if quote

businesses can implement it effectively.

That is the biggest asterisk I've ever

seen. And most businesses according to

the CEO of Cognizant, Robbie Kumar, have

not yet done the hard work. I think

that's very true. There it is. That's

the gap between the abundance narrative

that sounds so good in Switzerland and

the reality. It's not about capability

of models. It's about implementation.

It's about value capture. The AI already

exists, but the trillion dollar value

that people like to talk about doesn't

just show up and flow automatically.

This is not the fountain of youth. This

is the story everyone is missing when

they debate AGI narratives. The

interesting question is really not

whether AI creates abundance. It does.

The interesting question is where are

the bottlenecks? Because that's where

value concentrates. Of course, AI is

creating an unprecedented abundance of

intelligence. But that just means that

the bottleneck flows downstream and

that's where the leverage lives and

that's where fortunes will be made or

lost in the next decade. Abundance is

super handwavy. I'm not interested in

handwavy. Bottlenecks are specific and

specificity is where strategy happens.

It's where careers happen and it's where

companies happen. So let's talk about

bottlenecks first. A bottleneck is the

binding constraint in a system. It's

it's not a constraint. It is the high

leverage binding constraint. The one

that determines actual throughput in the

system. If you improve anything else,

you've accomplished nothing because you

didn't improve the bottleneck. But if

you improve the bottleneck just a little

bit, everything will move. Look, this is

basic systems thinking and it's also

something that most people ignore. They

optimize for whatever is visible,

whatever is comfortable, whatever

they're already good at. They work

harder instead of differently. They add

capacity where there's already lots of

capacity in the system and they ignore

the choke point because that's been

really painful to view and consider and

address. So many organizations do this.

The history of the corporation

ironically illustrates this perfectly.

Every dominant organizational form

emerged to dissolve a specific

bottleneck. The Dutch East India

companies solved the capital lockup

problem of multi-year oceanic voyages.

Railroads cracked the energy constraint

on overland transport. Banks emerged to

allocate capital across time. Stock

exchanges aggregated capital at scales

that exceeded any private fortune.

Walmart solved the information

bottleneck in retail supply chains just

knowing what was selling where and

getting it there before stockouts. The

pattern is consistent. Whoever solves

the binding constraints captures

disproportionate value. Everybody else

participates in the abundance that's

created. The AI era absolutely has its

own bottlenecks and they're not the ones

most people are watching. First, the

binding constraint on AI capability is

increasingly atoms, not bits. Jensen

Hang told Davos that AI needs more

energy, more land, more power, and more

trade skilled workers. Contemporary

hypers scale data centers consume 100

plus megawws. Training a single frontier

model can require sustained exoflops of

compute for weeks. The electricity

demands are approaching those of small

nations. In some cases, this matters

because physical infrastructure operates

on very different timelines than

software. You can ship a new model in

months if you have the compute, but

building a data center to run it at

scale that takes moving atoms around.

That takes time. Permitting alone can

take years in some cases. Expanding grid

capacity is even harder. Google recently

shared that they are bottlenecking on

the ability to establish connections to

the grid. This is not the only

bottleneck in the system, but it's a

great example of all of the specific

upstream bottlenecks that are

constraining the ability of hyperscalers

to build right now. The result is a

structural wedge between what's

technically possible and what is

deployable today. capability sprints

ahead while infrastructure really plots.

We're seeing this also with the memory

crisis where DRAM prices are just

skyrocketing because there's not enough

memory to go around. A model can exist

in potential, but the physical substrate

to run it at scale is what's required to

deliver value. Who captures value from

this gap? Well, the joke is it's always

Jensen and it's Nvidia. And that's not

entirely wrong, but it's also more than

that. It's whoever can navigate the

physical constraints faster. It's who

can pick the better site. It's who can

get faster permitting. It's who can get

more efficient construction. Who can do

smarter energy sourcing. This is not a

temporary bottleneck. This is

structural. The companies that

understand this are securing power

purchase agreements, advanced memory

purchase agreements, locking up

construction capacity, and building

relationships with utilities years in

advance. The companies that don't are

assuming compute will magically appear.

The chip supply chain is even more

constrained. TSMC and a handful of other

fabs control the production of advanced

semiconductors. Packaging and testing

and high bandwidth memory all have their

own separate bottlenecks. As I've called

out, Nvidia's market position isn't

really about better chips. It's about

having chips at all when everyone else

is capacity constrained. The hardware

advantage compounds because access to

compute determines who gets to train the

next generation of models, who gets a

seat at the table. And yes, the physical

layer creates an opportunity for an

entirely different kind of company. One

we normally don't think of as an AI

business. Someone has to build these

facilities. Someone has to provision the

power. Someone has to manufacture the

cooling systems, install the racks,

connect the fiber. This is what Jensen

is calling highquality jobs because he

can't get enough of them and neither can

any of the other hyperscalers. He says

trade craft jobs in these kinds of

spaces have salaries that have nearly

doubled. And I'm not at all surprised.

The abundance of AI at the application

layer depends on scarcity being resolved

at the physical layer. And that

resolution means people. The geographic

distribution matters too. Data centers

need stable grids, friendly permitting

environments. Access to cooling whether

through climate or water. This means

certain regions effectively become

strategic assets. It means local

politics become unexpectedly relevant to

the trajectory of AI. the infrastructure

to build AI, the AI that we have in our

pocket and assume is global, that

infrastructure lives locally. But that

is not the only bottleneck. In fact,

that might be the most well-known

bottleneck, but there are a bunch of

others that people talk about less

often. The trust deficit is the next

one. When Demuse Hassabi spoke at Davos,

his biggest concern wasn't technical. It

was the loss of meaning and purpose in a

world where productivity is no longer

the priority. End quote. He also worried

that we lack quote institutional

reflection about AI. Look, what he's

really saying is these are coordination

problems and coordination runs on trust

and he's worried about trust. Consider

what happens when anyone can get

sophisticated AI content and generate

whatever they want at the touch of a

bot. Text, images, video, code, all

become cheap to produce. The cost of

generation collapses, but the cost of

trust doesn't get cheaper. If anything,

trust gets harder because the difference

between synthetic and authentic is

becoming indistinguishable. Every piece

of content could be fabricated. Every

credential, you could gain that. Every

piece of information might be generated

to manipulate you. When you can't

distinguish the signal from the noise,

you're overwhelmed as a human. And you

look for someone to trust. Trust is the

infrastructure of coordination. When I

trust that a counterparty will honor a

commitment, I don't need to write every

contingency into legal language. When I

trust that a credential signals

competence, I don't need to administer

all of my own tests. When I trust that

published information is accurate, I

don't need to verify it independently.

Trust reduces transaction costs. It's

the it's the trust in the system that

makes coordination possible. Now,

imagine that trust degrading. You don't

have to imagine it. You see it and you

feel it. Transaction costs tend to rise

across the entire economy. Deals take

longer. Verification layers multiply.

Everything gets harder. Who captures

value here? Whoever can mediate trust.

The institutions that can verify, that

can authenticate, that can certify, the

platforms that develop reputations for

signal in a world of noise, the networks

where track records are visible and

accountability actually exists. We're

kind of looking for trust banks in the

21st century. essential infrastructure

that everyone can rely on, controlling a

scarce resource that must be accumulated

over time and that can be allocated

across different uses. The parallels

between trust and capital are definitely

thought-provoking. Here's another

bottleneck that people aren't talking

about enough. The integration gap.

Cognizance research points to something

specific. The value is conditional on

implementation.

$4.5 trillion dollars sitting there

chained up because organizations can't

figure out how to use AI effectively.

This is the integration bottleneck. AI

has the general capacity but no specific

context. And we know after a couple of

years of implementation at the corporate

level that a general capability is a

tool that works well for individuals and

without specific work on the part of the

company, it just dies at the team level.

it does not go anywhere. And so, yes, a

general AI can write code, but it

doesn't know your code base. A general

AI can draft strategy, but it doesn't

know your competitive dynamics. It can

it can talk about board politics

generally, but it doesn't know your

board. It can talk about the product

strategy of someone in your category,

but it doesn't know you. The gap between

AI can do this, and AI does this

usefully right here is $4.5 trillion.

Bridging it requires context that's

often tacit, right? It embeds practices.

It embeds relationships. It doesn't just

embed documents. The person who's been

at the company for 20 years knows that

things aren't written down anywhere. The

AI doesn't. This knowledge is not

promptable. The interface between

general AI capability and specific

organizational reality is where value

gets lost or captured. And some

companies are going to figure out how to

solve this integration problem and

unlock massive productivity gains by

tying AI into their workflows. Others

are going to deploy AI tools by the side

that sit unused or worse get actively

misused. And they're going to generate

outputs that look deceptively productive

and that do not connect to anything that

matters. The difference isn't the AI or

the tool. The AI is increasingly a

commodity, guys. The difference is the

organizational capacity to integrate.

Who builds that capacity? That's not

obvious. Maybe it's a new category of

consultancy that specializes in AI or

fit. Maybe it's internal roles that

don't exist yet. People whose job it is

to translate between what the business

needs and what AI can do. Maybe it's

software that encodes organizational

context in ways that make AI outputs

more relevant. Whatever the form, this

is a bottleneck. And bottlenecks are

where value concentrates. The

coordination problem is broader than

trust. AI doesn't just dissolve the

challenge of getting humans to agree

magically. It doesn't make them align

magically. It might make coordination

even harder. When anyone can generate

sophisticated arguments for any

position, groups have even more trouble

reaching consensus or alignment. Larry's

warning at Davos was really pointed. If

AI does to white collar workers what

globalization did to blue collar

workers, we need to confront that

reality directly. It's very comforting

for him to say that, isn't it? Sitting

in his little Sharon Davos. But he's

describing a coordination problem. How

do we actually share the gains from AI

in ways that don't trigger social

disruption? That's a question of human

alignment. And really, no one at Davos

has those answers. And everyone wanted

to talk over a cocktail glass about

them. The IMF managing director

certainly had a quotable saying that a

tsunami was hitting the labor market and

40% of jobs globally would be affected

and quote we don't know how to make it

inclusive. Well, honestly, the people

who are the closest to knowing how to

put AI and jobs together aren't the ones

going to Davos. They're the ones who are

actually building workflows where AI and

people work together. They don't get

those invitations. You know what's

really interesting? I've spent the first

part of this video talking about

bottlenecks and most of them seem like

they apply to companies, but everything

above applies to individuals too. The

bottleneck principle is a fractal

principle. You are also a system with

binding constraints. Your output and

your impact and your leverage are

functions of which bottleneck you're

solving and whether you're optimizing

the right constraint. The old individual

bottlenecks are dissolving. Access to

information is abundant. Access to tools

is cheap. Skill acquisition is rapidly

getting easier. It used to take 5 years

to become a proficient programmer or

more. AI compresses or eliminates those

runways. Dario Amade noted at Davos that

his own engineers no longer program from

scratch. They supervise and edit the

work of models. And this is something

that's come out of OpenAI as well. And

we're hearing it over and over again

from other extremely experienced

engineers who are now saying we don't

really touch code. This is disorienting

if your identity was built around skills

that are commoditizing like programming.

But disorientation is not a strategy not

for your career or mine. The question is

where are the new individual

bottlenecks? And honestly I was not

happy with what the Davos guys said.

Again I feel like they asked lots of

questions and didn't have answers.

Hubis's advice to young people was to

become incredibly competent with AI

tools. That's a throwaway line. That's

not a great line. I want to think more

deliberately. Tool fluency is table

stakes. The constraint shifts to what

you do with those tools. Taste and

judgment become really critical. When

generation is really cheap because

people have all those tools, the

curation of what's good is expensive.

Knowing what to make, when to stop,

what's good enough versus what's

actually good. These are capacities that

actually still take a lot of time to

learn. The AI can generate a hundred

options, but knowing which option is

right is still human terrain. The

challenge is that taste develops slowly

while AI devalues output. So if you

spend 3 years developing good taste in

design and AI makes okay design a

commodity before you can capitalize on

your extra 10% or 20% of taste, you end

up losing a race you didn't know you

were running. I feel and hear that

frustration from a lot of early career

folks right now. The window to good

taste is getting narrower and the people

who are surviving and thriving and

developing good taste are narrowing

their focus earlier. It used to be that

when you developed good taste, you were

really broad to start with and then you

discovered how to narrow over time as

you learned. These days the folks I see

who have extraordinary taste are diving

in super deeply on something. So they

are rapidly pushing to the frontier past

the edge of where an AI good enough is

acceptable because we all know yes AI in

many ways has solved front-end design

but if you want extraordinary design

people are still turning to humans who

have extraordinary taste. That kind of

dynamic is going to persist in a lot of

different corners of the economy and

it's going to supply a lot of different

jobs. Here's another one. Problem

finding eclipses problem solving. AI

solves wellsp specified problems with

increasing fluency. But specifying the

right problem and framing it right that

remains very very human. What should we

build? What is wrong here? Have I had

time to think about it? What question if

answered would unlock everything else?

Our education system has largely

optimized for problem solving and the

market is increasingly rewarding problem

finding. If you are good at looking for

problems and good at talking about them

and framing them, you're in good shape.

The analyst who knows which questions to

ask and which problems matter vastly

outpaces the analyst who can answer any

question. The skill increasingly is not

execution, it's direction setting. It's

a management skill. Context and

institutional knowledge are becoming

moes for individuals in the way that

data is becoming a moat for companies.

AI is general usefulness is specific.

The person who understands why the

organization really operates the way it

does, what the stakeholder actually

wants beneath what they're saying. That

tacit knowledge is very hard to

replicate and increasingly valuable. And

this creates a really strange dynamic.

Juniors who would historically have

accumulated context through years of

apprenticeship now face a very

compressed path. Why spend 5 years

learning how the organization works when

AI can help you skip the grunt work? But

the grunt work was also where that

context got absorbed and the implicit

knowledge that made senior people really

valuable often came from thousands of

little exposures that never happen if AI

handles all the tasks. So, how do you

develop institutional knowledge without

that slow accumulation? Honestly, I

think it still takes slow accumulation

and people are trying to speedrun it and

they're going to learn that the hard

way. No one has a better answer yet.

There is no fast forward to 20 years of

deep experience in a domain. I'll give

you one more. Execution and follow

through are emerging as a binding

constraint for many. I know I said that

solving problems was going out of style

and finding problems was in style. Well,

there's an element of follow-th through

that we still see as a bottleneck. AI

can generate a lot of plans. It can

generate a workout plan for me tomorrow,

but I have to show up to the gym.

Turning any of these plans that AI can

generate into reality requires a human

to decide and commit and to persist and

to navigate politics, to hold people

accountable, to keep going when things

get hard. Execution has always been

underrated because it's much less

legible than ideation. People love to

ask, "What about Steve's brilliant mind

when he created the iPhone?" They don't

ask, "What about Steve's relentless

execution to get it done?" And call

Gorilla Glass and make them produce the

glass he knew was right for the iPhone.

A brilliant strategy document is

visible. It might get you a promotion in

some companies, but the grinding work of

implementation. Steve calling Google and

saying, "The yellow in the O on Google

looks terrible on the iPhone. and my

engineers will be at your door to fix

it. That's grinding work of

implementation. That's not a strategy

document. Tolerance for ambiguity

separates those who thrive from those

who freeze. The environment is shifting

really fast, guys. Best practices are

shifting all the time. People are

desperate for stable ground in that

world. And the constraint that you face

is actually your ability to metabolize

change. How much uncertainty can you

hold on to in a rapidly changing world

without freezing while continuing to

execute and follow through on a

longerterm perspective? People who are

able to master that balancing act are in

huge demand. All of this adds up to a

leverage shift. The old model of talent

development was super linear. You

acquired skills, you traded your time

for money, you let it accumulate slowly.

The new model has a really different

shape. Some individuals are discovering

X leverage X leverage through AI

augmentation not because they work

harder but because they've identified

their bottleneck and directly dissolved

it. Maybe a developer was bottlenecked

on boilerplate. Maybe a strategist was

limited by analysis bandwidth. Whatever

it is, they found the constraint and

removed it and unlocked capacity that

was laden. Most of us are not finding

that leverage for ourselves because we

are optimizing against the old preAI

constraints. We're still trying to prove

we have the skills when the skills are

commoditizing. The diagnostic question

for each of us is deeply personal. What

is constraining my output right now?

It's not what I wish was constraining

me, right? It's not what was

constraining me 3 years ago. It's not

the constraint I built my identity

around solving so I can be proud of it.

It's the actual binding constraint

today. Now, for some of us, it is tool

fluency because we haven't genuinely

integrated AI into a workflow. And for

others, it's taste. Maybe it's problem

finding for you. The bottleneck is going

to be specific to you. Solving it

requires first honesty about what's

actually holding you back. I keep going

back to Davos and the abundance

narrative that dominated there. It feels

clangy. It feels out of touch. The

conditional is doing a lot of work in

these predictions. Yes, the capability

might exist. I increasingly don't doubt

that. But the value capture depends on

solving bottlenecks that are

organizational, institutional, physical,

and social, not technical. And that is

hard work. That is hard human work. I

believe the businesses and people that

are going to thrive in the next 10 years

are going to be the ones that correctly

identify where scarcity has run off to,

where it has migrated to, into physical

infrastructure, into trust, into

integration, into coordination, and

build systems and build careers out of

addressing those constraints.

Intelligence is getting cheaper. The

promise of abundance is absolutely real.

AI is going to keep getting smarter.

cognitive output is going to keep

getting easier to produce every single

month. Abundant,

but abundance doesn't create value

directly. Abundance shifts where

scarcity lives. And we haven't been

honest about that. The question isn't

whether to believe in the coming

abundance as an article of faith. No,

no, no, no, no, no. The question is

where are the bottlenecks and are you

positioning yourself and your business

to solve them? That's really the only

question that matters and it doesn't get

enough airtime.