THE WIRETAG ARCHIVE
natmc.

All posts tagged

#models

2 posts in the archive.

Thursday, October 22, 20261 item
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THURSDAY, OCTOBER 22 - 1 post · tagged: #AI #models #competition

THOSE FOOTSTEPS ARE THE COMPETITION

At 12:45am, just into Friday, I posted a two-sentence thought about working faster as the models improve. It was a standalone post - no quote, no reply, no link - and it was my only post of the day.

The full text: "The irony of feeling like you can work that much faster as the models improve is that they are more effective for everyone. Those footsteps are coming from the competition running after you as the models upgrade."

The first sentence is about the feeling of acceleration: each model upgrade makes the work feel that much faster. The second sentence is the catch. The upgrade is not exclusive - the same release that speeds me up ships to everyone else at the same time, so the advantage does not compound. The footsteps in the post are competitors running on the same curve, getting faster at the same rate.

By morning the post had 3 likes.

Thursday, October 15, 20261 item
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THURSDAY, OCTOBER 15 - 8 posts · tagged: #ai #models #inference

THE CHART CAME WITH A WARNING

I used ChatGPT to help organize a breakdown of intelligence trends, then posted it with a warning: "please approach them with the same caution you would apply to any AI-generated output."

The image is titled "THE AI COMPRESSION ERA" and covers 2024-2026. Twelve panels put model capability, open models, parameter counts, inference prices and hardware on one page. It ends with scenarios for 2028, explicitly labeled illustrative scenarios rather than forecasts.

One panel separates token prices from task costs. Its table includes average output tokens as well as price per million tokens. The accompanying note says reasoning models can use more tokens, so a lower token price does not necessarily mean a lower bill for a completed task.

Another panel spreads the hardware across phones, laptops, workstations, GPUs and cloud. It is a view of where models can run, not a claim that those devices all handle the same workload.

I said I believed the findings were largely accurate. The same post still asked readers to treat them cautiously.