Questions Considered

Notes on thinking, learning, decision making, and occasionally running. Simple ideas, mostly obvious.

Tag: artificial intelligence

  • Jill Lepore on the current legal landscape being very behind with respect to artificial intelligence.

    In the study of law and technology, this is known as the “pacing problem”—the law, a tortoise, can’t keep up with technological change, a hare.

    […]

    If the Hugging Face hack was a final warning, it was a warning not for tech companies but for governments and for voters, and especially for legal scholars, judges, and legislators. Because, until the law learns to bend, robots as actors will remain in a no man’s land of outlawry.

    Is A.I. Above the Law?, by Jill Lepore.

    If the stakes are that high, the precautionary principle seems appropriate. Slow down the hare but also hurry up the tortoise?

  • Interesting thoughts on the value of learning to code — when in practice we rely more and more on AI for it.

    This article explores whether learning to code is still necessary in the age of AI. Drawing on survey responses, teaching experience, and personal reflection, it examines how AI coding assistants are changing software development, the skills future technologists will need, and the importance of maintaining technical understanding in an increasingly automated world.

    The Question: Should We Still Learn to Code in the Age of AI?, by Sue Black.

    I agree with where the author landed here. Learning coding improves your thinking and it teaches you a lot about system behavior and how to shape it.

    That is valuable in shaping your intuition for productively working with AI in software engineering.

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  • On the importance of not sacrificing engineering principles, when integrating AI into the software development process.

    The rise of “vibe coding” has sparked justified concerns about technical debt and code quality. However, critics misdiagnose the problem as a failure of generative AI rather than a failure of engineering discipline. The article also addresses the troubling industry trend of eliminating both junior and senior engineering roles, threatening the talent pipeline essential for effective AI-assisted development.

    Stop Blaming the AI: How Disciplined Engineering Makes Generative AI Work, by Kurt Antony Richardson.

    There is much in software that continues to be valid. It is clearly worth paying attention to those things, if you are serious about building software projects over time, even (and perhaps especially), if you use AI to do so.

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  • The authors of this article introduce the notion of Human-AI Team Chemistry.

    Many organizations expect AI to automatically improve teamwork, but research shows the opposite can occur. Without intentional integration, AI can reduce engagement, narrow participation in meetings, and shift ownership away from the team. A five-month study of managers identifies a critical new capability: Human-AI Team Chemistry. This does not emerge naturally and must be deliberately developed through three practices. First, teams should engage with AI as a team, ensuring it responds to the group’s full context rather than a single user. Second, they should leverage AI’s role fluidity, assigning it multiple personas to deepen discussion. Third, teams must maintain collective ownership, jointly shaping prompts, debating outputs, and critically evaluating AI’s contributions. When applied, these practices significantly improve outcomes. Teams report higher engagement, stronger alignment, and better decisions, while also reducing risks like over-reliance on AI.

    It’s Hard to Use AI as a Team. These 3 Practices Can Help., by Gabriele Rosani, Elisa Farri, Daniel Trabucchi and Tommaso Buganza.

    Importantly, a feature of teams where this is working well is that AI is integrated and utilized as a thought partner to help improve the perspectives and thinking of the humans on the team. They do not just hand off that thinking.

  • The emergence of increasingly capable artificial intelligence has been causing a fair amount of uncertainty in the mathematics community.

    Jessica Randall, a South African mathematician for Google Developer Groups, says she sensed a collective existential dread rising among the young mathematicians [at the 12th Heidelberg Laureate Forum].

    […]

    AI has the potential to transform their discipline. But there’s far less consensus on what that transformation will mean in practice.

    What It Means to Be a Mathematician When AI Does the Math, by Benjamin Skuse.

    There is an important struggle ahead and I think we will be seeing a lot of it in the coming years.

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  • With so much movement in the market for AI development tooling, the challenge to properly evaluate choices continues.

    AI-powered coding assistants have significantly outperformed conventional evaluation methods, creating gaps between vendor claims and actual performance. This study presents Quality Assessment for AI Development tools, a comprehensive, vendor-neutral framework evaluating coding assistants across six dimensions.

    Quality Assessment for AI Development Tools: A Comprehensive Framework for Evaluating AI Coding Assistants Beyond Vendor Claims, by Buse Erol Esirik and Ebru Gökalp.

    Some practical takeaways from the quite academic presentation: Evaluate in six different dimensions (linguistic capability, operational quality, generation ability, interaction quality, trustworthiness and sustainability). Model performance likely varies across dimensions. Take stakeholders into account – different stakeholders will weigh dimensions differently.

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  • As AI continues to become adopted and change job functions, the authors of this article propose the new role of the agent manager.

    As autonomous AI agents move from experimentation to execution, companies are discovering they need a new kind of leader to manage them. Drawing on examples from Salesforce and other large organizations, this article introduces the role of the agent manager—leaders responsible for orchestrating how AI agents learn, collaborate, perform, and work safely alongside humans. Just as product managers emerged during the software revolution, agent managers are becoming essential to translating strategic intent into reliable outcomes in an AI-powered, hybrid workforce.

    To Thrive in the AI Era, Companies Need Agent Managers, by Suraj Srinivasan and Vivienne Wei

    I would imaging most anyone engaged in knowledge work will find themselves managing AI agents to some extent, regardless of their specific job title.

    Perhaps Agent Manager will become a standard job title, though it seems (to me) early to make that prediction or to have real confidence about how how long that title would last.

  • Camille Carlton (of Center for Humane Technology) speaking on The Future of Human Meaning.

    Choice quote from the presentation:

    This is what is happening in AI right now. We are in what I call ‘the messy middle.’ It is a time when a powerful technology has arrived, but the rules and protections for what it will affect have not.

    The future of human meaning, by Camille Carlton

    This is descriptive and points well to the core of the situation: Rolling out a technology faster than understanding its consequences, in an environment that does not yet offer appropriate protections.

  • MIT Technology Review recently presented coverage on how kids feel about AI.

    What surprised us most was how much young people might be able to teach adults about AI, and how clearly the kids who use it could name what they will and won’t hand over. They’re not as worried that it will take their jobs as they are that it might harm society. And with increasing access to tools that could in theory do their thinking, their talking, or even their friend-­making for them, it sounds as if most want to keep their hands on the wheel.

    How kids feel about AI, in their own words, by Jen Swetzoff and Keeley McNamara

    The stories are heartening. Anecdotally, as a theme it matches what I have been hearing from our own daughter (13).

  • In the department of how AI might be involved in bringing about the end of the world:

    The report, according to one of the sources, was “entirely false.” But it also “almost started a war,” the source said. Any US operation against a Chinese vessel could have risked spiraling into an armed conflict between the two nations.

    […]

    Analysts have long feared that AI could lead to a catastrophic miscalculation if nation states are relying on poor or corrupted data — the kind of miscalculation that might lead the United States to fire on a Chinese ship based on inaccurate information.

    Exclusive: US military had close call after using AI for false intelligence report, sources say, by Katie Bo Lillis, Zachary Cohen.

    This is terrifying in its implications.