Writing at the end of the world, from Hveragerði, Iceland
Bjarnason: the labour arbitrage theory of dev tool popularity
Baldur Bjarnason argues that development tools win by making developers replaceable. The JVM ran that experiment three times, against Visual Basic's precedent, through EJB's committee, and into Spring's reversal, and the results say his theory is exactly half of a whole.
The two worlds of programming: why developers who make the same observations about LLMs come to opposite conclusions
Writing at the end of the world, from Hveragerði, Iceland
The unintended consequences of large language models as a labor-augmenting technology in science
As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.
The unintended consequences of large language models as a labor-augmenting technology in science
As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.
Book Publishers Are Quietly Using More AI. Staff Are Revolting
Workers at three major publishing houses tell WIRED that LLMs are being used for publicity, cover art, back cover copy, and emails, as some execs push junior staff to champion the tech.
Building what customers need, not just what they ask for - Linear
Notes from our product team on building the tools we use every day.
Systems design 3: LLMs and the semantic revolution
Long ago in the 1990s when I was in high school, my chemistry+physics teacher pulled me aside. "Avery, you know how the Internet works, righ...
Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools
How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final outp
Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools
How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final outp
The developer’s dilemma
When it comes to making money, software developers are an odd bunch. Developers produce code, and code is in high demand. At a glance, it…
Labor market impacts of AI: A new measure
We introduce "observed exposure," combining LLM capability with real Claude usage data, and share early evidence on employment effects.
Making and Tool-Making
Bad tools as the main source of friction for Citybound development - and what I'm going to do about it.
Tools – The Markup
Our tools hold institutions accountable for the way they use technology, pulling back the curtain so readers can see for themselves how technology affects them.
Ink & Switch
An independent research lab exploring the future of tools for thought.
supernote-cli: pen, paper, and a pipe
A recent NYT piece argued we need a mental fitness revolution to combat the cognitive decay caused by algorithmic feeds and generative AI. It's an efficient one-two punch. If you're not brainrotting on short form video content, you're outsourcing all of your thinking to an LLM. The result is a kind of cognitive strip-mining. What's left requires active defense. For me, one way of defending that capacity for deep work is with a pen on e-ink. Whether it's annotating a paper or starting a sketch from scratch, I'm intentionally making room for focused thought. My army of clawed Claudes and Codexes will just have to wait.
