Structure the response as HTML and open the generated file in a browser

TLDR The human–AI input/output mind-meld is an active area with a lot of work ahead, well before invasive BCIs become relevant. Structuring responses as HTML is a practical approach at the current stage

Input-side improvements are also needed: audio, text, or video alone are insufficient. There is a need for pointing and gesture input to reference on-screen elements, mirroring how people collaborate

Many open questions remain about how precise/procedural "Software 1.0" artifacts (interactive simulations) will be fused with neural artifacts (diffusion grids), but current demos suggest this

The extrapolation points toward interactive videos generated by diffusion-style neural nets.

n) interactive neural videos and simulations

Possible progression: raw text (harder and more effortful to read) markdown (bold, italics, headings, tables — easier on the eyes; commonly used today) HTML (still procedural but far more flexible for

About a third of the human brain is a massively parallel vision processor, a high-bandwidth channel into perception. As AI improves, a progression that leverages this seems likely

More broadly, audio tends to be the human-preferred input for AIs while vision (images/animations/video) is the preferred output.

A practical trick: end a query by instructing an LLM to "structure the response as HTML" and open the generated file in a browser; asking for slide-style output can also produce useful, display-ready

The degree to which awe at AI occurs correlates closely with how much AI is used for coding

At the same time, a free, older Advanced Voice mode can stumble on dumb questions in short-form clips while the highest-tier paid Codex model can spend an hour restructuring code or uncovering vulnerabilities

TLDR: These groups are talking past each other.

Those capabilities have improved fastest because 1) the domains provide explicit, verifiable reward functions amenable to RL (e.g., unit tests pass/fail), and 2) they're highly valuable in B2B settings,

When given a terminal, these models can solve programming tasks that used to take days or weeks, restructure codebases coherently, or find and exploit system vulnerabilities.

This group experiences the strongest sense of "AI psychosis" because recent advances in those technical areas have been dramatic.

That yields a cohort that both pays for frontier agentic models and uses them professionally in programming, math, or research.

Part of that is the technical nature of reinforcement learning, which benefits from verifiable reward signals, and part is product focus: companies prioritize areas that generate more B2B value, so

Second, even paid access (e.g., ~$200/month) doesn't mean broad improvements are obvious. Most everyday prompts—search, general writing, basic advice—haven't seen the dramatic leaps found in highly technical

Those free or deprecated models don't reflect the capabilities of this year's state-of-the-art agentic models, especially OpenAI Codex and Claude Code