I kind of grew up immersed in emerging technology. I was playing with 3D printers when they only existed in makerspaces and attended yearly conferences for DIY tech and virtual reality. At one point—as a twelve-year-old—I gave a talk to graduate students about that technology’s potential use cases across industries.

And I’ve always insisted on understanding the technologies I interface with (at least to some reasonable, greater-than-average degree).^[1]You can imagine my joy when I learned that Bitcoin had 20x’d while I was piecing through blockchain infrastructure. I played with AI as a developer around 2020, but lost interest quickly—maybe because it scared me.

I’m more of a cynic now, and when I published my last post on generative AI, I took the perspective of an outsider. It’s the first time I’ve watched the tech world’s fervor from the sidelines, and a new sensation for me. I wouldn’t have it.

Since then, I’ve made a concentrated effort to understand this world which had so rocked mine…

I’ve found that LLMs make effective coding partners, though non-technical users will find themselves trapped if they don’t understand the code they’re writing. I’ve “vibe coded” a couple of projects: an “assistant,” who emails prospective employers daily on my behalf; and an AI chat-like interface for ELIZA—but I’ve found LLMs most useful as a second set of eyes on human-written code, or for code-generation when given rigid project scaffolding.

But this is as much the creative’s game as it is the tech-savvy engineer’s. Even consumer tools show a capacity to democratize both technical and creative skillsets. But I fear a “taste singularity”—a point at which popular aesthetic preference stagnates, bolstered by a proliferation of AI-slop amalgam as the dominant form for creative output. It’s been comforting to watch the public pushback on AI’s use in creative fields.

For the time being, Industrial Generative AI usage lies primarily in the Technology sector, but the corporate world is betting on AI agents’ potential to fully replace middle management.^[2]Stanford 2026 AI Index Report

This AI ecosystem is predicated on the promise of “AGI,”^[3]Artificial General Intelligence or the assumption that language models will [soon] give technology the ability truly understand human meaning through linguistic morphology.

“Chat,” thus, is a poor interface for the medium. If computers understood us, why would we have them to write to us? Shouldn’t they just act? At a high level, interfaces shouldn’t be entirely text-based. It stopped being fun at Zork (long live Zork). From a technical perspective, this means that responses shouldn’t be so abstract. Large blocks of text are informative, but not especially conducive to multimodal usage. There’s a reason web APIs typically respond in a standardized language. We should be writing systems for highly adaptable consumption, across interfaces.

There’s a race to start companies which bring AI to existing sectors. Few of these will survive, though many will be “successful,” in getting acquired by larger companies. Tokenization is a bottleneck on the road to AGI,^[4]Perhaps Byte-Latent Transformers are next? but the feverish pace at which infrastructure has been built means a large shift will need to take place. It won’t happen quickly.

I’m optimistic that AI can be used to better lives, alleviating the friction many of us face with the digital world. But the costs of AI, both monetary and ecological, are high.