In the way
This sums up my experience of companies and products trying to inject AI in to the products I use to communicate with other people. It’s always just in the way, making stupid suggestions.
We hoped for a bicycle for the mind; we got a Lazy Boy recliner for the mind.
Nicky Case on how Douglas Engelbart’s vision for human-computer augmentation has taken a turn from creation to consumption.
When you create a Human+AI team, the hard part isn’t the “AI”. It isn’t even the “Human”.
It’s the “+”.
This sums up my experience of companies and products trying to inject AI in to the products I use to communicate with other people. It’s always just in the way, making stupid suggestions.
The human desire to connect with others is very profound, and the desire of technology companies to interject themselves even more into that desire—either by communicating on behalf of humans, or by pretending to be human—works in the opposite direction. These technologies don’t seem to be encouraging connection as much as commoditizing it.
I understand that OpenAI/Microsoft can’t build ChatGPT within our legal framework. Well they could but it would be prohibitively expensive (it already is now without paying the people who did the work). But I missed the part where that is our problem as a society.
This!
I am tired of talking about these things as tech issues. They are not. They are social and political.
In some ways, the fervor around AI is reminiscent of blockchain hype, which has steadily cooled since its 2021 peak. In almost all cases, blockchain technology serves no purpose but to make software slower, more difficult to fix, and a bigger target for scammers. AI isn’t nearly as frivolous—it has several novel use cases—but many are rightly wary of the resemblance. And there are concerns to be had; AI bears the deceptive appearance of a free lunch and, predictably, has non-obvious downsides that some founders and VCs will insist on learning the hard way.
This is a good level-headed overview of how generative language model tools work.
If something can be reduced to patterns, however elaborate they may be, AI can probably mimic it. That’s what AI does. That’s the whole story.
There’s very practical advice on deciding where and when these tools make sense:
The sweet spot for AI is a context where its choices are limited, transparent, and safe. We should be giving it an API, not an output box.
Thorough (and grim) research from Chris.
Seminal technology.
Game of Thrones spoilers ahoy.
Revisiting Spielberg’s films after a decade and a half.
We’ve taught machines to hallucinate so let’s be honest about their hallucinations.