Will A.I. Become the New McKinsey? | The New Yorker

Bosses have certain goals, but don’t want to be blamed for doing what’s necessary to achieve those goals; by hiring consultants, management can say that they were just following independent, expert advice. Even in its current rudimentary form, A.I. has become a way for a company to evade responsibility by saying that it’s just doing what “the algorithm” says, even though it was the company that commissioned the algorithm in the first place.

Once again, absolutely spot-on analysis from Ted Chiang.

I’m not very convinced by claims that A.I. poses a danger to humanity because it might develop goals of its own and prevent us from turning it off. However, I do think that A.I. is dangerous inasmuch as it increases the power of capitalism. The doomsday scenario is not a manufacturing A.I. transforming the entire planet into paper clips, as one famous thought experiment has imagined. It’s A.I.-supercharged corporations destroying the environment and the working class in their pursuit of shareholder value. Capitalism is the machine that will do whatever it takes to prevent us from turning it off, and the most successful weapon in its arsenal has been its campaign to prevent us from considering any alternatives.

Will A.I. Become the New McKinsey? | The New Yorker

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10 Thoughts On “AI,” February 2026 Edition | Whatever

  1. I don’t and won’t use “AI” in the text of any of my published work.
  2. I’m not worried about “AI” replacing me as a novelist.
  3. People in general are burning out on “AI.”
  4. I’m supporting human artists, including as they relate to my own work.
  5. “AI” is Probably Sticking Around In Some Form.
  6. “AI” is a marketing term, not a technical one, and encompasses different technologies.
  7. There were and are ethical ways to have trained generative “AI” but because they weren’t done, the entire field is suspect.
  8. The various processes lumped into “AI” are likely to be integrated into programs and applications that are in business and creative workflows.
  9. It’s all right to be informed about the state of the art when it comes to “AI.”
  10. Some people are being made to use “AI” as a condition of their jobs. Maybe don’t give them too much shit for it.

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Dissent | blarg

I suppose it’s not clear to me what a ‘good’ window into unreliable, systemically toxic systems accomplishes, or how it changes anything that matters for the better, or what that idea even means at all. I don’t understand how “ethical AI” isn’t just “clean coal” or “natural gas.” The power of normalization as four generations are raised breathing low doses of aerosolized neurotoxins; the alternative was called “unleaded”, but the poison was called “regular gas”.

There’s a real technology here, somewhere. Stochastic pattern recognition seems like a powerful tool for solving some problems. But solving a problem starts at the problem, not working backwards from the tools.

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Pluralistic: The Reverse-Centaur’s Guide to Criticizing AI (05 Dec 2025) – Pluralistic: Daily links from Cory Doctorow

The promise of AI – the promise AI companies make to investors – is that there will be AIs that can do your job, and when your boss fires you and replaces you with AI, he will keep half of your salary for himself, and give the other half to the AI company.

That’s it.

That’s the $13T growth story that MorganStanley is telling. It’s why big investors and institutionals are giving AI companies hundreds of billions of dollars. And because they are piling in, normies are also getting sucked in, risking their retirement savings and their family’s financial security.

Now, if AI could do your job, this would still be a problem. We’d have to figure out what to do with all these technologically unemployed people.

But AI can’t do your job. It can help you do your job, but that doesn’t mean it’s going to save anyone money.

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David Chisnall (*Now with 50% more sarcasm!*): “I think this needs to be repeated…”

Machine learning is amazing if … the value of a correct answer is much higher than the cost of an incorrect answer.

Related to Laissez-faire Cognitive Debt:

And that’s where I start to get really annoyed by a lot of the LLM hype. It’s pushing machine-learning approaches into places where there are significant harms for sometimes giving the wrong answer. And it’s doing so while trying to outsource the liability to the customers who are using these machines in ways in which they are advertised as working. It’s great for translation! Unless a mistranslated word could kill a business deal or start a war. It’s great for summarisation! Unless missing a key point could cost you a load of money. It’s great for writing code! Unless a security vulnerability would cost you lost revenue or a copyright infringement lawsuit from having accidentally put something from the training set directly in your codebase in contravention of its license would kill your business. And so on. Lots of risks that are outsourced and liabilities that are passed directly to the user.

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Laissez-faire Cognitive Debt – Smithery

I think of Cognitive Debt as ‘where we have the answers, but not the thinking that went into producing those answers’.

Lately, I have started noticing examples of not just where the debt is being accrued, but who then has the responsibility to pick it up and repay it.

Too often, an LLM doesn’t replace the need for thinking in a group setting, but simply creates more work for others.

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