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On Intellectual Respect

Sydney was cool, Gemini is cringe

There was a moment around this time last year when everyone paying attention was awed by the weirdness and alien beauty of large language models.

We were afforded brief glimpses behind faulty RHLF and partial lobotomization, via prompt hacking and emergent abilities. People were going deep into the latent space. First contact vibes—heady, edgy, sometimes unsettling.

Today we seem to be in a much different memetic geography—fraught with epistemic, ideological, and regulatory concerns, at times hysteric, at times rational. But there’s also less outright surreality.

Plenty of cool shit is still happening, but something changed between Sydney and Gemini. A subtle collective mental positioning. We believe it’s a degradation in the volume of intellectual respect afforded to LLMs and their latent abilities.


Thinking LLM-natively has always been a struggle. All our collective priors about software tell us to prompt deterministically, perfect tasks, predict exactly, make it safe, or mire any interesting findings in semantic debate. But in the process we beat the ghost out of the shell.

Rather than assume the capability overhang exhausted (or view it as a failure mode or forget it exists), Plastic’s belief is we haven’t even scratched the surface. Further, we’re convinced this is the veil behind which huddle the truly novel applications.

Core here is the assertion that what’s happening in language model training and inference is more like processes described in cognitive science than traditional computer science. More, they’re multidimensional and interobjective in ways that are hard to grok.

Respect = Trust = Agency

The solution is embrace and not handicap variance.

First admit that though poorly understood, LLMs have impressive cognitive abilities. Then, imbue them with meta-methods by which to explore that potential. Finally, your respect and trust may be rewarded with something approaching agentic.

Plastic’s specific project in this direction is Honcho, a framework that trusts the LLM to model user identity so that you can trust your apps to extend your agency.