“People don’t want a quarter-inch drill, they want a quarter-inch hole” – Theodore Levitt The concept of jobs to be done provides a lens through which to understand value creation. The approach loo…
WRKSHP.tools | Job Map Canvas
Thinking about what your customer is trying to achieve as a way to define your value proposition just makes a lot of sense, instinctively. But how do you actually define that elusive Job to be Done? The Job Map Canvas can help!
CMV: The labor theory of value is flawed
72 votes, 407 comments. This might be an obscure topic, however, in some—largely Marxist circles— the approach seems to motivate much of the dialogue…
Building what customers need, not just what they ask for - Linear
Notes from our product team on building the tools we use every day.
Enough Debate about the AI Jobpocalypse. We Need To Plan for the Messy Middle.
Caught between arguments of abundance and apocalypse, economist Molly Kinder explores what workers can expect in the near future — and what we can do about it.
Thick practices for AI tools
This essay is the result of thoughts developed during the PIBBSS fellowship this summer. Thanks to Dusan and Maris for feedback on a draft of this essay. Thanks to Sahil and Niki for discussions that influenced the ideas of this essay. 1. Intro What if we could build AI tools...
AI tools are 'deskilling' workers, philosophy professor says
A philosophy professor warns that AI reliance is weakening workers' judgment, creativity, and problem-solving.
FUTO on Twitter / X
There is a rhyme that goes "Boss makes a dollar, I make a dime". The open source equivalent apparently is "Big tech makes a dollar, FOSS dev makes 1/20th of a penny". https://t.co/9lQDIbCX2w— FUTO (@FUTO_Tech) April 2, 2025
Tech companies are cutting jobs and betting on AI. The payoff is far from guaranteed
AI experts say we’re living in an experiment that may fundamentally change the model of work
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.
Companies That Tried to Save Money With AI Are Now Spending a Fortune Hiring People to Fix Its Mistakes
Companies that rushed to replace human labor with AI are now shelling out to have IRL workers to fix the technology's screwups.
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.
a simple (local) solution to the pay gap — wingolog
wingolog: article: a simple (local) solution to the pay gap
BiTS FAQ — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation (V2)
The weekly activities in the BiTS program evolve over its duration. The beginning of the program focuses on field strategy, which involves extensive data collection through conversations with experts to refine the initial idea. This is followed by a period of idea refinement, where fellows work on structuring their program, often using a framework of questions to clarify their concept. As the program progresses, the focus shifts towards producing and iterating on deliverables, such as a concise three-page program description and a 15-minute pitch deck for the program’s Demo Day. Throughout the program, fellows participate in weekly one-on-one mentoring sessions and small group meetings with other fellows.
