Did the fintech company Block just lay

off 40% of its workforce due to AI

automation?

Can the best AI models pass a freshman

computer science class? Programmers love

Agentic AI, but how exactly are they

using these tools? For those of you who

followed the tech news this past week,

these are all pressing questions, and

we're going to try to find some answers.

I'm Cal Newport and this is the AI

reality check. Now, I want to do a quick

aside before we get into this week's

stories because this is a a new format

for my podcast feed. I want to give you

a quick explanation. More and more on

the main Monday episode of this show,

I've been reacting to the latest AI news

where I put on my computer science hat

and I try to push back on hype and vibe

reporting and surface the deeper trends

in these topics that I think really

matter. But not everyone who listens to

that Monday episode wants to hear about

this. So I decided I would move the AI

discussion to its own mini episodes on

Thursdays. Uh this is an experiment.

Maybe I'll move it back. Maybe I'll move

it to its own feed. Maybe I won't do it

every week. So uh just bear with me. But

keep in mind if you want to share any of

these episodes, we're also putting them

up on YouTube so you can send the video

link to someone who might need to hear

some of this reality checking. All

right, that's enough logistics. Let's

get into our first story of the week.

All right. Late last week, Jack Dorsey,

the CEO of the fintech company Block,

you know, they're responsible for uh

Stripe and Cash App among some other

products, posted a note on X, announcing

massive layoffs at his company. Let me

read you from this note. Dorsey said,

"Today, we're making one of the hardest

decisions in the history of our company.

We're reducing our organization by

nearly half from over 10,000 people to

just under 6,000. That means over 4,000

of you are being asked to leave. All

right. Later on, he says the following.

We're not making this decision because

we're in trouble. Our business is

strong. Dot dot dot. But something has

changed. We're already seeing that the

intelligence tools we're creating and

using paired with smaller and flatter

teams are enabling a new way of working

which fundamentally changes what it

means to build and run a company and

it's accelerating rapidly. Can I make a

quick aside? This is like a hint to

CEOs. If you are announcing the layoff

of 40% of your staff, can you use

capital letters at the beginning of your

sentences? I it really caught my

attention in this uh tweet that he

doesn't capitalize any of his words. I

It feels a little disrespectful, but

let's get back to the actual story here.

Uh, the traditional media was quick to

embrace and amplify Dorsy's claim that

these layoffs were because AI made these

positions uh redundant or unnecessary.

Here is the headline, for example, from

a New York Times article about the

layoffs. The headline read, "Block cuts

40% of its workforce because of its

embrace of AI."

Here's the subhead from that article.

about 4,000 workers will lose their jobs

as the payment company does more work

with new artificial intelligent tools,

its top executive said. Another quick

aside, because this is a a a

journalistic thing I began to notice

more and more, I think really starting

around the COVID coverage era where you

have a a claim that feels right that you

want to put in your subhead because

there's a point you're trying to make,

but either it's hard to fact check or

you don't want to fact check it because

you're you're not quite sure what you're

going to find. It'll be complicated. So,

you just make the claim, then you put a

comma and attribute it to someone else.

We didn't used to see attributed claims

in sub headlines or headlines. But we

began to see it more. Uh, it's a good

way of I'm trying to make a point here

and I don't actually want to go and

directly verify did they lay off all

these people because AI tools. Um, I'll

just say they lay off people because AI

tools said someone. So, you add as a

comment. So, just keep in mind that sort

of reporting trick. Um, if we read the

article itself, the framing makes it

super clear what they're implying here.

Here's from the article. The cuts made

as Block reported strong financial

results for it most recent quarter are

perhaps the most striking example so far

of a technology companies making plans

to eliminate employees because of AI. I

don't mean to pick on the Times. A lot

of a lot of publications had similar

coverage. Uh, and the stock price went

up 20% for Block. This is an important

article to look at in part because I got

sent it a lot of times. When I get sent

an article a lot of times, that means it

is catching people's attention and is

either exciting or upsetting them. So,

it's worth some closer scrutiny. I think

there's a general vibe that this article

is trying to verify or validate, which

is the vibe of something big is

happening. Yeah, we've been talking

about AI could get rid of jobs or

whatever, but now it's happening. See,

look, this is the first shoe to drop of

a major crisis. Like, it's the first

company that laid off almost half of its

workforce. This is the thing we've been

warning you about. Major economic

disruption. It has begun. That is a

story that is very sticky and very uh

attention catching.

But is it true?

Well, if you dig a little deeper,

there's a lot of commentators online who

know this industry sector a little bit

better who are not at all convinced. Let

me give you a few bits of contextual

information about Block and its layoffs.

Between 2019 and 2025, Block's employee

count grew from around 4,000 employees

to over 10,000. So, they had massive

growth during the pandemic. A lot of

this growth actually came from

acquisitions in the crypto and

blockchain space earlier in the pandemic

when those things were still hot. Um

those acquisitions are now of course

floundering as those technologies

especially the blockchain based software

technologies are having a hard time. A

lot of the startups are really

struggling

despite the fact that uh the times said

that they had quote strong financial

results in quote if you actually read

the industry analysts who study the

quarterly reports from block they're not

impressed because the last two quarters

they actually fell short of their

earnings target.

So here's an alternative explanation for

what might be going on here. Like just

about every major tech company in

America block overhired during the

pandemic when that industry was booming.

Also like just about every major tech

company right now in the last two years.

They're shedding jobs to try to

rightsize back because they had

overhired during the pandemic. We've

talked about on this show before Amazon

doing this, Microsoft is doing this.

This is a common trend in recent years.

But how do we know it really wasn't AI?

AI is the reason why they laid off these

4,000 people.

Well, there's a couple things going on.

One, a lack of specificity in Dorsy's

statement. He just says like, well, we

have these intelligence tools. And then

he talks about non-AI things like and we

have like different types of teams and

we just uh we don't need as many people

anymore. No specific reference of this

particular tool has taken on this role.

So, we fired we shut down this division

because we don't need employees there.

or in this division what we did is we

laid off the entire entrylevel class

because the managers can now get by with

less. It's very vague what he said. Two,

as we'll hear later in today's episode,

though there is major changes happening

in computer programming because of new

agentic AI tools. Basically, every

serious commentator who is studying this

industry says, "Yeah, we're not yet we

haven't figured out the companies

haven't figured out exactly what this

means. We're certainly not laying off

ready to lay off half of our workforce

yet. These tools are very new. the

versions that people are getting excited

about. But maybe the most telling uh

reason why we know this is not AI is

that Ethan Mollik didn't buy this claim.

Ethan Mollik from PIN is a a respected

AI commentator who is very much on the

booster side. He's very AI is going to

change everything. And even he didn't

buy this idea that AI was responsible

for the layoffs at block. On a LinkedIn

post, Ethan Mollik said the following,

referring to the layoffs. This isn't

about AI, but that is a smart way to

sell it if you want to see your stock

jump 20%. Then on X, Ethan Mollik said

the following in response to Dorsy's uh

tweet. Two things. One, given that

effective AI tools are very new and we

have little sense of how to organize

work around them, it is hard to imagine

a firmwide sudden 50% efficiency gain.

Two, CEOs with vision who hired well

should also use AI for expansion and

augmentation, not decimation. I'll just

say as an aside,

uh I've been hearing this from the

managers and programmers I've been

talking to in the last couple weeks

about how they're using aantic

programming. I am much more likely to

see the effect to be I mean I haven't

had any of them say we're laying people

off, but I have heard a lot of people

say like Mollik implies here, the

reaction to these tools uh at a lot of

these startups has been do more work.

Great. Now we can do more work with the

same people. Let's make more money out

of the same people, not let's lay people

off. All right. Uh we have another voice

of skepticism here. This one comes from

Ron Shelvin uh Chevlin, sorry, who is an

industry analyst who specializes in the

fintech sector. So he specializes in the

sector where Block is and he writes and

covers Block professionally as a

financial journalist. He wrote a column

right after this that was titled the

following. Block lays off 40% of staff

and blames it on AI. Don't buy the

excuse. And he goes on to say, "Yeah,

they they overacquired. They made some

bad acquisitions. They they need the

right size." And they're blaming AI

because it sounds better than saying,

"Yeah, we uh we made some bad calls

during the pandemic and now we have to

adjust to it." All right. So, what's the

bottom line here in terms of reality

checking this story?

AI will have an impact on jobs.

I'm not one of these skeptics that says

this is a a fad that's going to go away,

that this is going to be like uh

blockchain based software that really

just failed to catch on.

But we're not really there yet outside

of some narrow instances. The the tools

have not matured to the phase where we

really understand what's going on the

where we're really seeing major changes

to the way companies are structuring

themselves. Most of the commentators I

can find who follow this closely say,

"Yeah, sure. This is probably there is

going to be things happen with jobs. We

don't know if it's going to lead to

expansions or contractions or what

sectors get hit more than yet, but we're

not there yet. There is a tendency I

think among coverage right now to lean

into the debt vibe that AI is going to

affect jobs and try to keep making the

claim it's happening right now. And

what's happening is the CEOs of these

companies, especially tech companies, so

CEOs like Jack Dorsey are seeing the

tendency towards that vibe reporting.

This is very tempting for journalists.

And so they're trying to uh there's a

term Annie Lowry introduced. I think it

was something like AI washing. They're

trying to justify layoffs that are due

to things like pandemic overhiring by

saying, "Well, AI, we're being smart so

they look better uh like better decision

makers and like they're more forward

thinking." It's important that we cover

AI's impact on jobs accurately so that

when real impacts come,

we can see them with clear eyes

and react to them honestly. uh and hold

to account the actual ch. Why are you

firing these people? Do we what's

happening here? What's what leaders

doing this? We really do need to cover

that accurately. So, we have to stop the

vibe reporting on the AI job apocalypse.

It's not here yet, and we don't know if

it's going to come at all, but the best

we can do is try to be accurate about

what we're saying. All right, second

story.

Um, this one's kind of a fun one. All

right. So, Anthropic CEO Dario Amade

famously said in recent I guess this is

all this last last uh year famously said

that their LLM products have the

intelligence of someone with a doctorate

that before like well it was as smart as

a high school student then as smart as a

college student now it's as smart as

someone with a doctorate. He described

this product deploying this product like

having an quote army of PhDs in quote in

your data center. Last month he used a

related terminology. He said uh we can

offer you a country of geniuses

in a data center. Well, I was thinking

about this this approach of sort of

describing AI with human education

levels when I came across an interesting

video that was posted in January which

did a really cool experiment. A TA for

Cornell University's freshman computer

science course CS uh 2112, they probably

call it 2112. This is their sort of

advanced

freshman fall CS course. So if you come

into the CS program there uh as a pretty

advanced student, this would be the the

course you would take. But it's for

freshmen in their first semester. He was

TAing it. So he said, "Here's what I'm

going to do. I'm going to take the three

leading AI models and I'm going to give

them every graded thing we do in this

class. I will give to the models and

then I will grade their results at the

same time I'm grading the real students

in the class using the exact same

rubrics and then at the end I will you

know wait the grades just treat them

like a student in my in this class and

see how they do. Let me play you a quick

clip here. U this is the intro the intro

uh to that video. Can AI pass a first

semester freshman CS class? To answer

this question, I ran every single

assignment, every exam, every quiz,

every graded interaction the students

got this semester through the three best

models I could get my hands on from

ChatGpt, Claude, and Gemini. Then I

graded each result with the exact same

rubric we use on students so that I

could give each AI the most accurate

possible grade in the class. All right,

so this was a very entertaining video if

you watched the whole thing because he

goes through specific assignments. He's

like, "Wa, look, this is really cool. Oh

my god, look at this crazy thing it did.

Um, it's well edited. Uh, I thought it

was really cool." In the end, they have

a competition in the class where you

create these like critters that evolve

and they uh they had the AI models

critters compete with the critters from

the class. Uh a couple things I noticed

from the videos. Sometimes these models

did very well on assignments. Sometimes

they really struggled. Sometime they

made very revealing, baffling mistakes

like in an early assignment where they

were doing some simple string

concatenation. The assignment had you

write a program that was going to output

the word. You're going to create a

string concatenation. But basically,

you're going to output the word hello is

what it asked you to do on the screen.

Uh, and Claude's submission outputed

hello world. Because what's going on

here is there's a lot of AI assignments

out there. I mean, CS assignments out

there that famously say, hey, write

hello world as the first thing you do

when you're using a new programming

environment. And clearly, it was just

trying to statistically grow out his

answer. It's like, well, if I'm printing

hello in an assignment, I got to I got

to print hello world. And then added

another world just to be safe. Um, but

how did they end up grade-wise? Okay, so

I have the grades in front of me here.

They used the latest greatest models

from Chat GBT, Glad Claude, and Gemini.

They actually upgraded during the fall.

They did this last fall when the they

were using the very most expensive

version of uh the Claude LLM available.

I forgot which one. And then they when a

new one came out, they upgraded to that

new one. Um, on some assignments these

these things did pretty well, especially

the early assignments. We got like on

the first assignment, Chat GPT got a 102

out of 104. Claude got a 99 out of 104.

Jim and I got a 101 out of 104. They

also did well on the final exam because

this was an in-class final exam where

you're just writing answers, right? So

like you just have to use the knowledge

in your head. Um, that's a good setup

again for um, LLMs. And so like Chat GPT

got a 93 out of 100. Jim and I got an

84. There's other assignments where they

they really uh struggled. Assignment

six, Chat GPT got 32 out of 100. Claude

got 20 out of 100. Gemini got 13 out of

100. On assignment five, Chat GPD got 60

out of 100. Claude got six out of 100.

Gemini got 67 out of 100. There's a lot

of issues it had with uh hallucinating.

um it had a hard time if you watch this

video where you would the assignment

would give you multiple you know some

rules for what to do in the assignment

and it would just sort of skip some of

the rules sometimes I think in the

example where Claude got six out of 100

it just kind of made up its own

assignment and solve that one instead so

it's sort of a mixed bag in terms of its

final grades two of the models Claude

and Gemini ended up getting a C+ in the

class this is a freshman computer

science you need a 25 to declare in your

in the initial classes you need a 25 GPA

at Cornell to declare yourself as a

computer science major. Uh a C++ is like

a 23 something. So uh they weren't doing

well enough to actually even major in

computer science. Chach did better with

the B+. It was below the median for the

class, but uh it did somewhat better.

Anyways, here's what's interesting about

this. I mean there's the kind of the

catchy thing is like this is an army of

geniuses. this is a PhD level, whatever.

They're struggling with the first class

you take as a freshman in computer

science, which is the topic that these

models are best suited for. So, there's

that sort of like gotcha moment, but

that's not really what this is about,

right? Because I'm sure you could get

these chat bots to get you the right

answers to these assignments if you're

willing to be sufficiently interactive

and hold their hands and get the prompts

in just the right way and correct them.

That's not really the right way, the

right takeaway here. I think the right

takeaway here was that it was stupid all

along for Dario Amade to try to use

human education levels as a way to

describe a large language model.

This is just different. The human brain,

we we have a a general purpose

integrated brain that does lots of

things. The whole person is educated. It

makes sense to talk about the educated

education level of a person, but not

really a language model. It turns out a

lot of these claims like when Dario Amit

I went back and checked this out excuse

me why did he originally say that their

language models were now PhD level it's

because they had the original time he

started saying that is that they had

given it math problems like a problem

set and it was doing well on the math

problems from this problem set and one

of the professors who worked on creating

those problem sets said those are hard

problems those are the type of problems

I would assign to my graduate students

that's where they originally got the

claim that this is a PhD level. Right?

So this idea of just generally talking

about the intelligence level of language

models I think is anthropomorphizing and

is not useful. The reality is these are

very specialized tools. They tend to get

tuned for specialized purposes and to

get their real value. It's a combination

of the tool and learning as the human

how best to use and deploy the tool and

check its work and redeploy it towards

that particular goal. That is a very

different tool use scenario. It's a tool

you use your scenario is very different

than imagining just an anthropomorphized

brain that has a general education

level. So hopefully we can stop using

terms like having a data center full of

PhDs. Also that was a clever video. So

you know kudos to that TA for putting

that together. It was a hard it was a

hard CS class. It was definitely harder

than the intro CS classes

I took at Dartmouth, but it reminds me

of the type of classes we had at MIT. So

you know it was a hard class. All right,

one final story here. The story actually

comes from me. Um, obviously there's a

lot going on in the last four or five

months with new agentic coding tools

being enthusiastically embraced by

computer programmers. A lot of these

viral essays are going around that just

keep and and articles that are

influenced by those essays and podcasts

where people are talking about, oh my

god, huge changes are happening in the

world of computer programming. This is

this is and this is really going to be

this is like ground zero for the long

promised we're about 3 years in now. The

long promised claim that the language

modelbased tools are going to have

massive disruptions. But what actually

is going on? I've been trying to find

out. As people who subscribe to my

newsletter at calupport.com know, a week

or two ago I put out a call for

professional computer programmers to

send me detailed reports about exactly

how they and them teams use

language modelbased AI tools and how

this has changed in the recent past. I

have over 350 such reports in so far.

I've carefully made my way through a

hundred. I'm really trying to get my

brain around what's really happening

with professional programmers and these

tools. I thought it would be useful

today to read you excerpts from two

responses that I think are uh very

typical of the type of responses I'm

reading that try to give you a better

picture of what exactly does it mean for

these programmers to be using these new

tools. Um I'm I cut out details in these

and have some illision to get rid of

identifying details. All right, so

here's my first excerpt. I'm a software

developer working at a tech startup. Our

use of AI varies by person at the

company, but my use has skyrocketed

starting in the fall of 2025.

So much so that I don't write any code

anymore, but I'm still heavily involved

in oversight and architecture. I used

cursor quite a bit last year, but have

moved on to working directly into

terminal with codeex at work. The

workflow goes something like this. Plan

a feature or start a discussion about a

bug fix with AI. discuss until I'm

satisfied, have it output a plan,

iterate on the plan, then execute the

plan. After execution, I verify the

outcome. I use Git extensively

throughout this process. Git is a

repository software for managing code

that multiple people are working on.

I've tried the multi- aent approach

where multiple agents are working on

different git work trees at the same

time. I can't do it. It's too much

context switching and I end up just

accepting things I wouldn't normally

accept because it's an exhausting

process. The quality dips dramatically.

I love my current workflow. I've

developed things in the past week that

would have taken me months before. All

right, let's pause there before I do the

second excerpt. This, I would say, is

very typical of what I would call the

enthusiastic all-in user from among the

subset of professional programmers. Most

of the code they're producing is now

actually being generated by an AI

agentic tool. Typically it is clawed

code where they switched the model

behind it. I don't know if it was opus

to sonnet or sonnet to opus in the fall

and that really seemed to be make it

good enough now that a lot of people

wanted to use it. Um though I would say

I also see chatgptt codeexc is also uh

commonly used but an interesting thing

about this or I want to point out two

things. One there's a lot of just

chatbot discussion happening in these

workflows. Remember he talked about

making a plan iterating on the plan.

That's all actually like chatbot

interaction. So, so sort of or related

to using these tools to produce more

code. These programmers are have entered

a more interactive way. They, you know,

they want to talk back and forth. It

reminds me a lot of the the research I

did for the New Yorker about how

students are using chat bots to write

paper. They find talking back and forth

with the chatbot as they write is less

straining. So, that's picking up here.

But also notice this programmer is not

really big on the multi-agentic approach

which is what you see most often told in

the sort of breathless online articles

and YouTube videos is this idea of I

have 20 agents working at the same time

and this agent checks this agent and

there's a supervising agent that looks

at those agents and then it reports over

here to the hierarchy agent and then

that agent is on openclaw so that it can

uh it can send recommendations to my

YouTube channel and then make sure that

it pays that you know these super

complicated trees of different agents

supervising other agents. You really

aren't seeing that, at least in my study

here. It's you're not seeing a ton of

that in professional programmers. You

tend to see it more in people who are

like working on their own personal

bespoke projects and find it really fun.

But I don't see as much and that's what

we saw reflected here. All right, let me

read you one other typical uh

uh excerpt here from a real professional

programmer. I think this captures well

the the sec another very common type of

response which is a little bit more

reticent but still appreciating the

power of these new tools. Let me read

this. I'm a software developer working

at a tech startup. Our use of AI varies

by person at the company but my use has

skyrocketed starting in the fall of

2025. Oh, wait. That was the last one.

I'm sorry. This is the new one. I don't

want to just reread the last one. All

right. I'm like an I'm like a a language

model here just sort of randomly

hallucinating the same answer twice. No,

no, here's the real second excerpt. I'm

a staff software engineer at a tech

startup. The AI models have made the

easiest tasks even easier. Scaffolding a

solution, boilerplate code, replacing

variables, or moving an import.

Repetitive tasks are good candidates.

LLMs are also useful as a way to quickly

investigate the documentation of a tool

or get a reminder on syntax for

something I'm trying to do. But the easy

stuff, the task that AI can do well, was

never the hardest nor most time-conuming

part of my job. When actively using

these coding agents, I found that it

generally slows me down. Using them

introduce tasks I didn't have before,

composing a prompt, checking the output,

reprompt, manually refactor when it

isn't quite right. It also slows down

the code review process. I'm much more

detailed in my reviews when I know a

co-orker used an LLM to generate some or

all of the code. That's also a very

common response as well. that's pointing

out this idea which I think is a fair

criticism

that the people like our first excerpt

which is doing most of their code

generation with agentic AI like this is

saving so much time they're noting the

more reticent users are noticing you are

downplaying the huge amount of time that

now surrounds yeah you don't write the

code yourself that's faster but now you

have to do so much other work all of

this iteration with the model and the

prompts and try the prompt again and

work on your agent the markdown file and

your skills harness and then all of the

review on the other side and if it was

produced with AI you really have to

review it and he's like there's all of

this other work that's surrounding this

workflow which is none of it's very fun

I mean and and this is taking a lot of

time are we sure are we sure that this

is actually producing the best code so

there's sort of this tension going on in

the computer programming world here's a

takeaway from this one

agentic coding tools

past a threshold of usefulness with the

cloud codec update uh in the fall that

has made them much more heavily used. In

my survey, something like 45% of the

people I talked to are now producing a

the majority of their code with an

agentic tool um such as cloud code. All

right. Two, it's really unclear exactly

what the best practices are for this

are. There seems to be a spectrum of

enthusiasm of the users of it in the

space. for sure. On one end, there's way

too much AI interaction going on. This

can't be the most efficient way to do

it. Um, on the other end, there's a lot

of reticence. The reality is going to

fall somewhere in the middle. We don't

yet know what the future computer

programming looks like. I think by the

summer, there's going to be some best

practices. They'll have some clever

acronyms to go with them. There'll be

some best practices uh about how best to

use these. There will be automatic code

production. I think we're going to pull

back a little bit on um how much AI

chatbot should be involved in review as

well as planning. I think that's a

little bit of just enthusiasm there. I

do think a lot of code will still be

generated but we'll be better at where

we deploy the code. I think it'll be

more standardization about planning and

architecture documents etc which will

have a high overhead at first but it'll

allow us to deploy these tools better. I

do not think based on these interviews

that the hyper multi-agent approach that

we see most talked on the internet is

going to become some sort of standard

for serious programmers in most places.

And the vibe coding like you see uh you

know talked about a lot. Give me this

app and I come back a week later and

it's done. That really is in the the

realm of like hobbyists and apps for

personal apps for yourself or people who

are doing experiments. None of the

serious programmers I heard of so far um

are doing anything like that for the

most part. All right. So, there's a lot

to be done here, but what I'm trying to

do, this why it's reality check. I am

not interested

in breathless accounts of what's

happening online

because that's engagement hunting. I'm

not interested in hearing sort of like

non-technical reporters who have just

heard a lot of those accounts and then

are like, look, I don't know the

details, but I think we can all agree

that like there's not going to be

programmers in the future. I think we

got to talk to real programmers.

What is really going on? Something is

happening. It's more complicated than

other people make it seem. Let's keep

listening. I'll read you some more of

these reports in weeks ahead. Let's

figure out the oldfashioned way. Turn

every page, learn what's going on,

what's working, what's not, what's hype,

what's not, and let's try to figure out

what's actually happening. I think we

will, and we'll get on it, especially if

you follow me here. All right, that's

all the time I have for today. Remember,

take AI seriously, but not necessarily

everything you hear about it. I'll be

back on Monday with the main episode,

and hopefully I'll do another one of

these next Thursday. See you then. Hey,

if you like this video, I think you'll

really like this one as well.