the people who burn Tokens the fastest
why are they worth the most
a strange ranking has been trending at big Silicon Valley firms lately
it's not about who writes better code
not about who ships products faster
instead it compares something that sounds absurd
who burns through AI Tokens the fastest
the faster you burn them
the more the engineer is seen as a master
hey there
hi everyone
I'm Wang Lijie
let me first explain what a Token is
you can think of it as fuel for AI
every time you put AI to work
from reading the instructions to generating the result
the compute it uses is measured in Tokens
the more Tokens you burn
the more work AI does
and the more you pay
a follower asked me
isn't this totally counterintuitive
don't companies chase lower costs and higher efficiency
isn't burning fewer Tokens better
how is it the other way around
burning them faster somehow means you're better
this question hits the core logic of the AI era
picture a scene
there are two excavators on a construction site
one burns two hundred liters of diesel a day
the other burns only twenty liters
looking at fuel use alone
you'd think the fuel-saving one is the better deal
but if the one burning two hundred liters dug a thousand cubic meters of earth
while the one burning twenty liters only dug fifty
then the answer flips
the diesel isn't waste
it's the shadow of output
Tokens are the diesel of AI
burning a lot doesn't mean wasting
it just means you can run the machine at full speed keeping it fully loaded
and most people
simply don't have that ability
let's take a look
from an ordinary programmer to a top AI engineer
how many hurdles lie in between
the first hurdle
is called the basic level
an ordinary engineer gets an AI coding tool and starts writing code
this is the now-popular vibe coding
you don't write it line by line yourself
you tell AI in plain words what feature you want
and let it do the work
sounds great
but in practice
what AI writes breaks the moment you test it
you throw the error back and let it fix it
you test again after the fix and new bugs pop up
after three or four rounds
AI might even break what it had already fixed
so you have to start over
this do-it-and-redo-it pattern
over a month on a subscription plan
burns roughly a hundred dollars of Tokens
a hundred dollars isn't much
but what does it buy
an extremely inefficient worker
ninety percent of the time AI isn't advancing the project
it's all fixing its own mistakes
the second hurdle
is called workflow optimization
smart engineers quickly find the trick
the problem isn't that AI is dumb
it's that the way you assign tasks is too crude
just saying build me a website
AI does it based on its own understanding
and most likely it's not what you wanted
then comes endless revising
but if you first spend an hour laying out the development process
and clearly defining the roles and collaboration rules of multiple agents
then split the website into five modules
and write out each module's function inputs outputs and boundary conditions clearly
and then hand it to AI
the odds of getting it right the first time go way up
this is called workflow design
when the input is precise
AI reworks less
but note this
even though each task needs less rework
you actually push forward more projects in a day
total Token use goes up instead of down
at the same time you start using better models
expensive models make fewer mistakes
and get it right the first time more often
all things considered
you save time
but spend more money
at this stage
it's about two to four hundred dollars a month
there's a subtle shift here
at the first level
the engineer inputs a ton of instructions
AI outputs a pile of half-finished work
the human inputs even more instructions to correct it
high input low useful output
at the second level
the engineer inputs less and less
yet AI's output gets more and more precise
a good workflow is like a precision mold
you put the raw material in
and out comes a standard part
the engineer goes from a handcrafter to a mold designer
a mold designer's value isn't in how many products they made by hand
but in how many products the mold can produce automatically
what really opens up the gap
is the third hurdle
high concurrency
here's an analogy that makes it clear
in the first two levels
you're a boss
with only one employee
you hand it the work
it finishes and you check
then you assign the next one
you can only do one thing at a time
the third level is different
you're upgraded to the company's CEO
you don't have one person under you
but a whole team
some write code
some do design
some write docs
some do testing
and even better
these employees can clone themselves
like Sun Wukong pulling out a hair and blowing on it
conjuring fifty copies of himself
take my actual workflow as an example
making one video needs thirty scenes
the traditional way is the main AI handles them one by one
first generate the image
then generate the audio
then compose the video
you finish one before starting the next
processing thirty scenes in a queue takes several hours
how do you do high concurrency
design a role dedicated to handling a single scene
let it call the image audio and video composition functions itself
then split this role into thirty clones
each takes one scene number
and they all start at once
what used to take hours
gets done in minutes
this is high concurrency
right now one main AI can dispatch up to fifty clones working in parallel
did you notice
by this point it's no longer one person writing code
it's one person managing an AI army
but this still isn't the limit
the fourth hurdle
is called full-chain squeezing
you're no longer a CEO running one product line
but a chairman overseeing ten CEOs at once
each CEO runs their own project with a team
ten lines moving forward at once
and each line has tons of clones collaborating with high concurrency
the most extreme is overnight mode
before bed you give AI a big complex task
laying out the rules the judgment criteria and the exception handling plans clearly
and let it run all night
you wake up in the morning
a project that would take a team a week
is already done
sounds too good to be true
right
but there's a fatal threshold here
if one part of the workflow isn't designed well
or one rule isn't spelled out
at three in the morning AI hits a point it can't decide on and stops to wait for you
you wake up the next morning to find it stopped working at three
wasting the whole night for nothing
I've been through this
once I gave AI a complex video production task
I laid out all the steps
but missed just one
what to do when a certain image fails to generate
and at two in the morning AI generated a bad image
not knowing whether to skip it or retry
it just stopped there waiting for me
I looked the next morning
and only six of thirty scenes were done
the rest of the time was spent waiting for one word from me
to keep AI running nonstop on a complex task for fifteen or twenty hours
without getting stuck or making directional mistakes
deciding on its own when it should
and following the rules when it shouldn't act alone
this demands extreme precision in the workflow
it's like designing a real company
each role's responsibilities the reporting flow
the decision authority the escalation mechanism
all have to be written into the virtual company's charter manuals and even its constitution
miss one rule
and AI might crash right there in the middle of the night
at this level
the engineer's rhythm is completely different from a traditional programmer's
he's not sitting there typing
he's running several AI product lines at once
his time is cut into fragments
while AI is running
he's thinking about how to arrange the next task
whichever line's AI stops
he immediately jumps over to give new instructions or clear the blocker
and keep it running
he barely does any hands-on execution himself
all his energy goes into one thing
keeping AI from stopping
an engineer like this
how much do they burn in a month
take myself as an example
I use the Claude Code subscription plan
two hundred dollars a month
the amount of Tokens it buys
roughly equals four thousand dollars of pay-as-you-go usage
I burn through two of these accounts a month just to fill my working hours
one Claude Code
one Codex
dividing the work
converted to pay-as-you-go
my monthly AI compute runs about eight thousand dollars
you might be curious
the Claude Code Max subscription
the 20X after it
what does it mean
let me tell you
once the monthly quota runs out and you switch to pay-as-you-go
the best models burn about ten to fifteen dollars an hour
ten hours a day is a hundred to a hundred fifty dollars
three to over four thousand dollars a month
so the quota the two hundred dollar plan gives you
is about equal to four thousand dollars of pay-as-you-go
that's the twenty times
this is already a decent level in Silicon Valley
but the engineers who use AI best
need two to four of these accounts
adding up to eight to sixteen thousand dollars of AI compute a month
now looking back at that ranking
it's not strange at all
burning Tokens fast
means three things
one your workflow is well designed
so AI won't get stuck
two you understand high concurrency
and can orchestrate dozens of AI clones at once
three you can keep AI running at full speed while you sleep
these three things are really saying the same thing
you can squeeze AI labor to the absolute limit
notice something
this is completely different from how engineers used to be judged
engineers used to be measured
by how much code they could write themselves
now it's by how much AI labor they can orchestrate
and to what extreme they can squeeze it
what does this resemble
it resembles management ability
the most classic leap in a career
is going from an individual contributor to a manager
it's no longer about how much work you can do yourself
but how big a team you can lead to deliver together
now the same leap is happening again
except what you manage isn't people
it's AI
there's not a single living person on the team
they're all agents
but the core abilities a manager needs haven't changed
breaking down tasks setting processes drawing boundaries handling exceptions and allocating resources
a top AI engineer
is essentially a top AI manager
so that ranking
isn't about who can spend more
but who's better at being AI's boss
but this story has an even deeper meaning
when an engineer uses eight thousand dollars of AI compute a month
to produce what a ten-person team used to make in a month
do the math on those ten people's cost
a Silicon Valley engineer averages around two hundred thousand dollars a year
plus benefits insurance and office space
ten people easily cost over two hundred thousand dollars a month
eight thousand dollars
against two hundred thousand dollars
this twenty-five to one leverage ratio
is what the ranking is really about
this is no longer a tech topic
it's an economic issue an industry-structure issue
and a signal about the future job market
whoever can orchestrate AI labor most efficiently
can create the most output at the lowest cost
we used to call this automation
but old automation replaced repetitive physical labor
assembly line workers tollbooth cashiers
today it's different
automation is starting to replace cognitive labor
writing code doing design writing copy doing research
these brain jobs once seen as iron rice bowls
are being taken over by AI
ever wonder why this wave of automation hits so hard
the last generation of automation needed factories robots and production lines
the upfront investment was huge
only big companies could afford it
this generation is different
one person one laptop one subscription account
is one or even several production lines
there's no minimum investment threshold
no economies-of-scale limit
the spread isn't linear
it's exponential
the key to driving this cognitive automation machine
isn't the ability to write code
it's the ability to design systems define processes and manage teams of agents
if you're a programmer
the skill to practice now isn't learning a new programming language
it's learning how to be a good CEO of an AI team
if you're not a programmer
you should pay even more attention to this
this trend won't stop at programming
any job that requires cognitive ability
will face the same question in the future
who can orchestrate AI more efficiently
is worth more
and those who can't orchestrate AI
are like someone holding a shovel next to an excavator
you can dig too
but you and that machine
aren't even in the same dimension
what do you think
what's the next industry to compete on Token-burning speed
and what level are you at right now
let me know in the comments
I'm Wang Lijie
see you next time