@tomcrespii
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Career focus on collaboration tools and org chg mgmt. See LinkedIn bio: https://nitter.cf/t.co/MLLG5sWYN2 …and see @trcres for personal interests.
Joined November 2009
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Tom Crespi retweeted
I kept wondering why smart people write things nobody understands.
Then a Harvard professor gave it a name that stopped me cold: the moment you understand something deeply, you lose the ability to remember not knowing it.
I watched his writing talk at 1am and ended up rewinding the same ten minutes three times.
The professor name is Steven Pinker.
He's spent 40 years studying how language works inside the human brain, and he opened with a question I'd never had the guts to ask out loud. Why is so much writing terrible? Not just school writing. Work emails. Government forms. Half the stuff experts put online.
His answer had nothing to do with effort. Or intelligence. Or how much someone knows.
He called it the Curse of Knowledge.
Here's what it means:
The second you truly understand something, you forget what it felt like to not understand it. The gaps in a beginner's head become invisible to you, because in your head those gaps got filled years ago. You're standing on ground you can't even see anymore, and you assume everyone else is standing there too.
He gave one example I can't shake. A brilliant scientist got on stage at a big event in front of 400 people and lost the entire room in under a minute. He dove straight into words only his lab used. Never said what problem he was solving. Never said why anyone should care. And the worst part? He had no clue it was happening. He thought he was being clear.
That's the curse. You can't feel it from the inside.
Then Pinker said the line that rearranged how I think about writing.
Bad writing isn't a character flaw. It's a failure of imagination. You cannot get inside your reader's head by trying harder or caring more. Trying harder just makes you explain it more confidently in the same language they already don't understand.
So how do you actually fix it?
You find one real person. Not a crowd. One human being who is smart but lives outside your little world. You hand them your writing and you watch their face while they read it. Not the page. Their face. The exact second their eyes go flat and confused is the exact spot your writing broke.
Pinker did this with his own mother. Not because she wasn't sharp. She was. But she wasn't a language scientist, so the moment he drifted into his private vocabulary, she'd get lost, and he'd know instantly.
I sat with that for a while, because I've been doing the opposite my whole life. When someone didn't get what I wrote, I assumed they weren't paying attention. Turns out I was the one who couldn't see the hole. I'd filled it so long ago I forgot it was ever there.
That's the trap. The better you know your subject, the worse you get at teaching it, and you can't feel yourself getting worse. It feels like clarity the entire time you're losing people.
Which means the smartest person in the room is often the hardest one to learn from. Not because they're hiding anything. Because they genuinely can't remember what it's like to not already know.
The cure isn't a bigger brain. It's borrowing someone else's for one uncomfortable minute.
Find one person outside your world. Show them what you wrote. Watch their face, not the page. Everything they don't understand is the part you were too close to see.
Tom Crespi retweeted
Good post on how to think about process redesign in an enterprise with AI Agents.
Unfortunately for most workflows, there’s no “easy” button. The best way to get real efficiency gains is you have to actually reengineer the process to take advantage of what agents are actually good at and can do differently and faster than people.
One of the challenges of course is that the most important workflows actually space many functions at once, so someone needs to be able to go in and retool how work happens.
“Nobody in that chain is empowered to walk into finance, for example, and say that their 14 step process should actually only be 5 steps. So nobody does it. Instead, you 'apply AI' on what you currently have, and you end up with faster sh*t.”
This is the road ahead for AI diffusion in enterprises. This also presents the clear opportunity for companies - both at the applied AI layer (regardless of the article’s title) and teams or companies that can go and implement transformation into the enterprises. And all roads lead to FDEs one way or another.
In 1999 a baseball player saved $3,000 a year starting at age 22 and never saved another dollar after age 37.
He retired at 70 with $703,000.
His coworker saved nothing for the first 15 years. Then saved $3,000 every single year until retirement. Same amount per year. Twice as many years. Disciplined. Patient.
He retired with $356,000.
Half as much. For saving more than twice as long.
Jonathan Gruber spends an entire lecture on the reason in MIT's introductory economics course. A dollar saved today compounds on itself. It earns interest on the interest. The earlier money goes in, the more time it has to multiply before you touch it.
That gap between $703,000 and $356,000 is not about discipline. It is not about sacrifice. It is about arithmetic and a calendar.
Which means every savings account has two numbers. The amount. And the when.
Max Scherzer signed a $210 million baseball contract. That was not $210 million. It was $15 million a year for 14 years, spread past his playing career. In present value terms at 4.7% interest, it was worth $166 million. Still a lot of money. But it dropped him from the second most valuable pitcher contract in history to the fourth.
He negotiated the number. Nobody negotiated the date.
A $290 million lottery jackpot paid over 20 years at 7% interest is worth $164 million today. The advertised number is not the real number. The real number is always smaller. How much smaller depends entirely on when you receive it and what you could have earned in between.
The people who understand this do not earn more money. They earn the same money at better times.
Four ways to make money. Wages arrive now and stop when the work stops. Capital arrives later and multiplies while it waits. The difference between them is not effort. It is which side of the interest rate you are sitting on.
Jonathan Gruber taught this at MIT for 25 years. His students helped design legislation covering 330 million people. The ones who went to finance started at $250,000.
They all learned it in the same lecture.
bookmark this and watch later - after this lecture every salary offer you receive will feel like a number waiting to be placed on a timeline
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👇👀
Hoy contratar a Tony Robbins cuesta 1 millón de dólares por un solo día.
Esto es una cinta de 21 minutos grabada en su propia casa hace más de 30 años.
Ahí explica exactamente cómo lograr que cualquiera diga que sí.
El mismo material por el que ahora cobran fortuna… completamente gratis.
Es una grabación rara e sin filtros, de la época en que todavía no cobraba a multimillonarios solo por estar en la misma habitación.
Cuando alguien dice que no, da dos excusas:
“No tengo tiempo” o “No tengo dinero”
Ninguna es verdad.
La verdadera razón es que todavía no cree que valga la pena.
No es un problema de dinero. Es un problema de estado.
Tony enseña algo que llama “atacar y confesar”
En lugar de discutir la objeción, confiesas la tuya propia:
“Yo tuve la oportunidad de ir hace seis meses y no fui hasta hace dos.
No puedo ni imaginar el tiempo que perdí”
La sala se queda en silencio. Nadie discute.
Después mete a la persona en el “tren del sí”
Cada pequeño sí se suma al siguiente.
Hasta que decir que no al final se siente más difícil que decir que sí.
Cuando llega el momento de firmar, la persona ya ha aceptado cinco veces sin darse cuenta.
Solo 21 minutos.
Ahí aprendes las dos únicas jugadas por las que la gente paga 1 millón al día:
cómo leer el estado de cualquiera… y cómo moverlo.
La mayoría se pasa años adivinando en ventas.
Él lo escribió en un flipchart de su sala de estar en menos de media hora.
Un asiento en esa habitación costaba $125 dólares.
Hoy cuesta $1 millón de dólares al día estar frente a él.
La cinta está gratis ahora mismo.
Y la respuesta está en este video.
Tom Crespi retweeted
One reason why we’re going to get uneven diffusion rates of agents is because different workflows in the enterprise are more or less aligned to continuous, uninterrupted computer work.
A big reason why agentic coding growth has gone completely vertical is because it’s a type of work where economic value is directly correlated with the output of solely digital information, and where the task size can in theory be unbounded in a single session. This means that as AI agents can do larger amounts of work at a time, and as model capability improves on this task type, they can directly be deployed at larger workloads near instantaneously.
Lots of work doesn’t natively have this property in how today’s workflows are designed. A sales rep needs a feedback loop with the customer before they can do additional work for a specific account, a lawyer needs to talk to the client, and a doctor needs to interact with a patient. So large agentic work will likely not look the same as it has in coding, at least by default.
Instead, in many of these domains, the agents will actually need to tie into a workflow that is reengineered for AI.
Matan Grinberg at Factory had a good way of thinking about this on the Training Data podcast: if everyone called in sick tomorrow, token usage would plummet because no one would be there to prompt them. This shows how early we are in agents running in the background for most processes; whereas this should be the majority of volume of agentic work in the future.
In legal, it will be about processing every contract that comes in, in sales it will be agents that roam through customer records and figure out signals of when to better do outreach, in life sciences research it will be about swarms of agents reading through every test or all research.
To do that these business processes have to be wired up to support agents, as opposed to the vast amounts of manual work that we do today.
This is the big opportunity right now, but it’s going to diffuse differently from something like agentic coding. Lots of change management, lots of data cleanup, lots of workflow reengineering, and more.
Tom Crespi retweeted
Replying to @realDonaldTrump
The 6 boys who broke into the burning house and saved all the occupants AND an elderly couple in their 90's deserve it also!!!
nitter.cf/Newsforce/status/20753…
🇺🇸A group of teenage boys in Lodi, Wisconsin, helped save an elderly couple and their family from a burning home on the 4th of July after spotting the fire from across the street.
The 19- and 20-year-olds broke into the home, found 5 or 6 people inside—including a 91-year-old man celebrating his birthday—and got everyone out safely before firefighters arrived.
Fire officials said the group’s quick action likely prevented a tragedy, as the home was heavily damaged by the blaze, which was reportedly caused by heavy fireworks use in the area.
Tom Crespi retweeted
I’ve never understood people flying across the world for vacation just to spend five days sitting next to a pool.
Google just dropped a 1-hour course on agentic engineering from scratch:
00:00 – How to build your first AI agent
08:24 – Build agent memory (short, persistent, long)
28:34 – Agentic loops, long-running AI agents
40:04 – How to build MCP (MCP vs API)
1:00:22 – Multi-agentic systems
This 1-hour watch will replace 10 paid agentic courses on the internet.
Watch it today, then read how to build a self-improving agentic system in the article below.
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Tom Crespi retweeted
Great post on some of the dynamics to think through for the future competitive advantage in world when AI models are shared amongst firms and packing so much for the intelligence of that industry.
This is going to become a core question for companies and the economy broadly over the next decade and beyond. If AI is trained on the best datasets in every single industry - like law, finance, healthcare, or life sciences - then how do you compete and differentiate in the future?
This is a great open question that I don’t think is perfectly knowable right now because of how fast AI progress is happening. But ultimately it stands to reason that if intelligence is abundant and broadly available to anyone in a field, then the companies that effectively use it the best and against a set of data and knowledge that grows in value over time, will be in a strong position.
There’s a huge reinforcing loop between the intelligence from models, a company’s own data, the connection of that data and AI in their workflows, and how employees ultimately interact with that system to create value. There’s no obvious point where this will become uniform across all companies in a vertical because each company will approach this in a different way, just as they already do with their talent and workflows. If anything, there will be compounding returns to those that do this best that accelerate their advantage over time.
Overall, super interesting question to see how this plays out over time.
Tom Crespi retweeted
Anthropic engineer:
"You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day."
In 45 minutes he shows exactly how to do it from scratch, step by step.
Most people are still doing all of this by hand.
Watch the session, then save the guide below.
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Tom Crespi retweeted
China's AI strategy: 1. Government invests in Deepseek, subsidizing labs to open source everything. 2. Building nuclear and data centers everywhere fast to lower inference cost, regulating it like electricity. 3. Making sure people learn AI fast, especially government officials must know how to use AI. US's AI strategy: stock price going up and to the right. Export control. Takes forever to build a data center because of permits and regulation. It's time to change.
😱 Insight from IT Leaders: Companies plan to *add* headcount as a result of AI adoption.
At Box, we just surveyed 1,640 IT leaders across the US, Japan, and Europe about agentic AI adoption. Many standout findings, but a big one was that the companies that adopted AI the most are planning to grow headcount the most.
Obviously lots of ways you can read that data and variables mixed in, but it’s actually quite intuitive that the companies that become most productive want to (and are able to) reinvest back into the business to keep getting the gains going.
The narrative of jobs being wiped out assumes that companies will take a fixed approach to what they want to be able for work on. What’s happening in practice is it’s causing companies to want to light up more engineering projects, sell to more customers, automate more processes to give time back, and more. That all leads to more work to be done by people.
Tom Crespi retweeted
Anthropic just accidentally made every AI course on the internet worthless.
A free 24-minute video. No signup. No paywall.
Taught by the people who literally wrote the code Claude runs on.
I watched it twice.
The part at 8:12 alone is worth more than any $300 course I've bought.
Most people will scroll past this. The ones who don't will have an unfair advantage for the next 2 years.
Bookmark before it disappears 👇
Tom Crespi retweeted
This is a fantastic post about why jobs aren’t going away in the way some predict. We are constantly making the mistake of confusing task completion with AI with being able to eliminate the whole job.
Even as we can automate one or many tasks within a job, the definition of the job almost inevitably just expands to do vastly more of those tasks, do them at a higher quality, or move on to the type of task that hasn’t been automated yet.
And as a result of being able to do more of the tasks or at a higher quality level, the job becomes valuable in a new way. And in many cases for now an entirely new audience as well.
This will be true for coding, legal work, sales, or marketing. The small business or non-tech company that wants to now take on larger software projects finally can, and they’ll hire to do so. The small business that couldn’t afford a full marketing agency can hire or contract out to a marketer that can do as much as an agency did before now with agents. And so on.
Don’t fall into the trap of confusing tasks with jobs.
We’ve automated every single thing we can @every with AI agents.
And yet there’s way more human work to do than ever. We’ve gone from 4 -> 30 human employees since GPT-3.
I wrote a report on the structural reasons: how AI makes expert competence cheap, why that drives up demand for experts, and why the dynamic only intensifies as we approach AGI.
After Automation: every.to/p/after-automation
Tom Crespi retweeted
Replying to @karpathy
According to OpenAI's own data and a Harvard NBER study, coding queries account for only about 4% of ChatGPT messages, while non-work queries make up over 73%.
For non-coding use cases, even $200/month subscribers have experienced stagnation or regression from 2025 through today, precisely because the entire AI industry has mistakenly treated coding as the sole standard for true intelligence.
This perhaps reveals a real choice: should AI development serve more people, or more profit?