Large Model

After observing the capital movements of large model companies these past few days, it’s easy to confuse two issues.

Zhipu and MiniMax are both pursuing the A-share listing path. Anthropic has already made a confidential filing of draft S-1 to the SEC, and OpenAI was also reported by Axios to be preparing a confidential IPO prospectus. Having these pieces of news lined up suggests that this industry has finally reached its peak/harvesting period.

But merely opening up an IPO window does not mean that the profitable window is open. Large model companies do have revenue—some revenue streams are growing very fast. What has not generally turned around yet is the set of accounts related to net profit, operating cash flow, and sustained model investment.

My previous article covered the semiconductor cycle, and I feel like there’s a piece of background/context missing.

Your judgment/conclusion regarding this point—the overall direction is correct. Furthermore, I believe it is a prerequisite that is easiest to overlook when trying to understand this current semiconductor boom.

A more accurate way to put it is not that “all internet giants are fighting in the same field,” but rather: Large Models have, for the first time, brought together major players previously scattered across different domains—such as search, advertising, social media, e-commerce, office productivity, cloud computing, and content distribution—into direct competition within the same technical stack.

This technology stack includes models, computational power, inference, cloud, Agents, distribution gateways, and commercialization closed loops. Everyone’s original “moat” is different, but now we must all fill the same gap. Those who fail to do so will see their future search entry points, ad pricing, office suites, e-commerce conversion, and social traffic distribution rewritten by others.

Scrolling through the model updates tonight was genuinely mind-boggling.

My current judgment is straightforward: this round is no longer merely a wave of model releases. It involves three fronts working simultaneously—model capabilities, API pricing, and chip stack ownership. Anyone who focuses on only one of these aspects will likely have a biased view. And it is precisely because these three dimensions are intertwining that the large model sector appears so intensely competitive.

I recently went back and reviewed the articles in my blog related to AI from the past two years, and I found that the content is no longer just simple experiences like “whether a certain model is good or not.” Instead, it has gradually formed a relatively clear main thread: How AI truly entered my development workflow, and what efficiency gains, costs, and new constraints it brought.

Throughout March, I was constantly testing between various large model API hubs. It is indeed cheap. You can test out foreign models like ChatGPT, Claude, and Gemini for a small amount of money per month, which at first glance seems like finding an extremely cost-effective solution. However, after actually using it, I increasingly feel that this path has always been constrained by an impossible triangle: Quality, Stability, and Affordability—it is difficult for all three to be achieved simultaneously. By last weekend, the situation became quite clear. During the two days from 2026-03-28 to 2026-03-29, I felt a noticeable tightening of risk controls on ChatGPT channels, and Claude was no different. Many low-cost relays that were previously usable suddenly became unstable or even completely failed. For me, this basically signaled the temporary end of the low-cost API relay model.

Recently, I’ve been observing discussions within the industry, and it seems there’s been a fundamental shift in the definition of “growth.”

Previously, when we discussed the internet, we talked about “four ounces moving a thousand pounds” – writing a few lines of code, renting a few cloud servers, and leveraging excellent interaction and operations to unlock hundreds of millions of users. However, as of 2026, this “low-asset” illusion is being completely shattered by large models.

Recently mingling in various programming large model communication circles, the most complained about thing is model degradation.

• Models deployed on local desktop computers are quantized models, essentially downgraded versions.
• With “vibe coding” so popular, could it be that the content output by current large models is the most valuable product – code?

“Previously, it was mentioned that when logging into Gemini Cli, you needed to configure the Google Cloud Project ID. This is already wrong – if it’s a personal account, there shouldn’t be this restriction. The fact that this restriction exists indicates that you’ve started triggering Google’s security system and are being identified as not being a personal account.

It’s frustrating; after using it for half a month and getting used to it, now I have to return to the embrace of cc + domestic models.”