Craig Tashman

Founder & CEO of @LiquidTextCorp and GA Tech PhD in human computer interaction.

[Craig Tashman @CraigTashman]

@khapgayemere I wish I could give everyone a discount, but we need money to feed the engineers!! 💸💸💸☺️ Thx again!

[Craig Tashman @CraigTashman]

@LagosVillager What's wrong?

[Garry Tan @garrytan]

Memorial Day is a reminder that freedom isn't the default state of the world. People fought and died for it.

I am thankful America is where you can build, create, and push the frontier forward.

Thank you to the men and women who served and gave their lives. 🇺🇸

[Craig Tashman @CraigTashman]

@PhilIPnPolicy @LiquidTextCorp Then email me, Craig at LiquidText dot net

[Craig Tashman @CraigTashman]

I know this, I can explain this, I can almost give a sermon on it… yet I somehow need to be reminded of it almost every day

[Brent Beshore @BrentBeshore]

The modern person has been told to build a self through achievement, and the Bay Area has intensified that message more than almost any place on earth.

I learned that too and can relate to much of this.

In this world, success does not bring rest. The person without wealth fears they have missed their chance. The person with wealth fears that without the company, the status, or the chase, they may no longer know who they are.

Both are asking the same question: “Am I enough?”

But work was never meant to bear that burden. We have to explore our "enough" outside of our profession. We must ask the questions about meaning that achievement has no answers.

And when we find it, work can become work again, not salvation. Money can become stewardship, not identity. Ambition can become service, not self-justification.

The Valley is experiencing, in a sudden and compressed form, what every human heart experiences eventually -- the discovery that the thing we were told would save us cannot bear the weight of our soul.

The answer is not to despise work, wealth, technology, or ambition. The answer is to dethrone them. Once they are no longer gods, they become the gifts that they actually are.

[Google DeepMind @GoogleDeepMind]

We’re reimagining a 50-year-old interface - the mouse pointer - with AI. 🖱️

These experimental demos show how people can intuitively direct Gemini on their screens using motion, speech, and natural shorthand to get things done 🧵

[Craig Tashman @CraigTashman]

@PatBlanchfield @LiquidTextCorp We had a brief maintenance window over the weekend (a few hours) and that may have been what you encountered? Anyway thanks I’m glad it’s back!

[Craig Tashman @CraigTashman]

Every drug has side effects and every government intervention in people’s lives has a cost. Always ask:
- Who will be screwed by this?
- What are the second order effects?
- When the other party gets power and weaponizes this, what will that look like?
- What secondary powers will the government assert to enforce this?
- What slippery slopes will be created by this?
- What will the eventual backlash against this look like?

Only when those are clear and settled, talk about whether the benefit is worth the side effects.

https://t.co/QBCEc3SBXM

[Massimo @Rainmaker1973]

Scientists have created one of the most detailed 3D reconstructions of a human cell (eukaryotic cell) ever produced.

This groundbreaking model, often termed a "Cellular Landscape Cross-Section Through a Eukaryotic Cell," combines data from X-ray tomography, nuclear magnetic resonance (NMR), and cryo-electron microscopy to map molecular structures in extreme detail.

[Ihtesham Ali @ihtesham2005]

A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work.

His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing.

In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen.

Here's the framework that has been quoted by every serious scientist for the last 40 years.

His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired.

He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow.

The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one.

The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed.

The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else.

The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices.

He finished the lecture with a line I have never been able to shake.

He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day.

The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword.

Hamming died in 1998. He gave his final lecture a few weeks before. He was 82.

The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.

[Brent Beshore @BrentBeshore]

"The interviewer, @DouthatNYT, broke down crying during the conversation. And when he did, Sasse laughed. Not unkindly, but the way a man laughs when the heaviness and the lightness of something are both true at the same time and he’s decided not to pretend otherwise."

[Craig Tashman @CraigTashman]

What could go wrong?

https://t.co/HOf6T4rJzK

[Craig Tashman @CraigTashman]

So basically we can’t trust LLMs anymore?

[Brian Roemmele @BrianRoemmele]

https://t.co/QfzZpeY3oN

[Craig Tashman @CraigTashman]

Silly to pay people for commodity thought work, pay people for humanness itself

[Alex Imas @alexolegimas]

New essay on the economics of structural change and the post-commodity future of work.

1. Almost any question about the impact of advanced AI on the economy needs to start at the same place: what is still scarce? Answer that, and the analysis becomes pretty straightforward. This essay explores what becomes scarce if AI really can replicate most of what humans do in production, and what this mean for the future of jobs.

2. My conjecture, working through the economics: labor reallocates across sectors, and the sector it reallocates to has properties that keep labor a meaningful share of the economy. Ultimately this is about the structure of demand itself. For this, we have to go back to Girard, Augustine and Rousseau: once people's base needs are met, their preferences shift to comparative motives (e.g., status, exclusivity, social desirability). This motive is inherently non-satiated.

4. The key paper is Comin, Lashkari, and Mestieri (Econometrica 2021). As people get richer, they don't buy proportionally more of everything. They shift spending toward sectors with higher income elasticity. They estimate income effects account for 75%+ of observed structural change.

5. The ironic consequence: the sector that gets automated becomes a smaller share of the economy, not a larger one. Agriculture got massively more productive and its share of employment collapsed. Manufacturing too. The "stagnant" sectors absorb the spending and the jobs.

6. So the question is: which sectors have high income elasticity in a post-AGI world? I argue it's what I call the relational sector. Categories where the human isn't just an input into production, it is part of the value.

7. Why does the relational sector have high income elasticity? Because human desire has a mimetic, relational dimension. We don't just want things for their intrinsic properties. We want what others want, and we want it more when others can't have it. Girard, Rousseau, Augustine, and Hobbes all saw this.

8. In work with Kristóf Madarász, we showed this experimentally: WTP roughly doubles when a random subset of others is excluded from the good. And in new work with Graelin Mandel, AI involvement kills the premium. Human-made art gains 44% from exclusivity; AI-made art only 21%.

9. This all comes together for the core argument. The sector that absorbs spending as AI makes commodity production cheap is one where human provenance is part of the value, and demand for it grows faster than income. Exactly the profile that keeps labor meaningful.

10. To be clear about the claim: I'm NOT saying aggregate labor share must rise. It may fall. The claim is about sectoral composition, i.e., where expenditure and employment go once commodities get cheap, and the fact that the sector that will absorb reallocated labor maps to a substantial component of human preferences and desire.

11. If you're interested in the formal model, a linked companion technical note works out all the economics.

Read the essay here: https://t.co/NcjVgn2o8g

[Craig Tashman @CraigTashman]

@gilrdb @zotero DM will be hard, but the form on the website will route to the right people. The team will be back online on Monday. I’m sorry about this, hopefully we can find out why you’re seeing an issue here quickly!