This article was drafted together with AI and reviewed by an editor. It draws on an external article, which is credited at the bottom.
The history of AI is usually presented as a list of events. In 1950 Turing proposed a test of machine intelligence, in 1956 the Dartmouth conference gave it the name "artificial intelligence," in 1997 Deep Blue beat the chess champion, in 2012 AlexNet opened the deep learning era, in 2016 AlphaGo beat Lee Sedol, in 2017 the Transformer paper appeared, and in 2022 ChatGPT brought it into popular use. These are widely known public facts. We re-read this timeline from the viewpoint of someone who works.
What stays 1 — Popularization lags behind technology
It took decades for AI that won at chess and Go to become an everyday tool, and the decisive moment it came down to daily life was not performance but a form anyone can open and use. The same question applies to our products. What matters is not whether the features are outstanding but whether it can be opened without explanation.
What stays 2 — Scale is not the same as capability
For a time the belief that "if you make the model bigger, capability follows" led the industry, and afterward reasoning methods, open models and low-cost models shook up the landscape. There is no guarantee that today's leader will be tomorrow's. That is why we do not tie our way of working to a particular model.
What stays 3 — The stage moves off the screen
The last box of the recent timeline is agents, connection standards, and AI that moves in the real world. If we are moving from AI that talks to AI that does work, what becomes important is not only the quality of its answers but permission design: what to hand over and what a human presses.
Standards for a stack that lets you work like a team
The article we referred to introduces tools for each of five founding stages (research, validation, building, marketing, operations). Tool names change in a few months, so we remember the standards instead of the names.
- One tool per stage. Do not use two or more tools that do the same job.
- Put validation first. Filter out wrong hypotheses cheaply before building.
- Automate operations last. Automate only what has been confirmed to repeat.
- Keep the data in your own hands. Whichever tool you swap, your materials should move over intact.
Further reading
This article was rewritten from our own team's perspective and standards, drawing on the concerns raised in the articles below. We did not follow their wording or structure, and where we could not verify a figure an original article cited against the primary data, we marked it as such. For some of the original articles, the full text opens only after logging in, so we read them based on the publicly available portion.
- learn.bizni.usAI Development Timeline — From the Turing Test to the Age of Agents (Korean) (BIZNIUS LEARN:US)
- learn.bizni.usThe Solo Founder's AI Stack (Korean) (BIZNIUS LEARN:US)
Also checked against other sources: