Computer

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.

/goal is easily misinterpreted as a command to “let the agent work for a bit longer.”

This, of course, is merely its surface manifestation. If you give Codex a goal, it can continuously progress around that objective, instead of stopping after a single round of answers. But what is truly noteworthy is not how long it “runs,” but rather that it converts “what constitutes completion” from a temporary reminder into an intrinsic part of the task itself.

A standard prompt describes what needs to happen next. A goal, however, is more like attaching a checklist/acceptance form to an agent: What is the objective? Where are the boundaries? Which validations must pass? What conditions must be met for it to be considered complete?

Let’s first establish the date. ChatGPT’s public research preview version was released on November 30, 2022, not 2023. [1]

After this point, NVIDIA’s data center GPU main roadmap is quite clear: The conclusion of Ampere, followed by Hopper taking over. Hopper focused on expanding VRAM capacity and refresh rate, while Blackwell will shift its focus from “single-card dense compute power” toward “inference throughput, power consumption, and system-level interconnection.” The China-specific versions represent a different story: A800, H800, and H20 are fundamentally compliance versions created under US export control constraints, and therefore cannot be viewed using the same metrics as the global flagship line.

In the last few months, while writing code using tools like Claude or Codex, my most striking realization wasn’t that “programmers are obsolete,” but rather that many tasks that used to be given to newcomers for practice can now generate a basic first draft themselves. Whether it’s creating a scaffold, adding several tests, or making small modifications on the fly—after running through these operations, the speed is genuinely fast, so fast it feels almost bittersweet.

For someone like me, who graduated ten years ago, frankly, this is more about increasing efficiency. Because I generally know where it’s reliable and where it isn’t; where something looks functional but actually has pitfalls hidden further down the line. But for fresh graduates, this topic isn’t so straightforward. AI isn’t just here to take over a few hours of manual labor; it feels more like it is compressing the traditional path of how a newcomer goes from zero knowledge to proficiency. This is also why I wanted to write about it separately.

Stunned. / Dumbfounded.

The official ChatGPT side doesn’t make it easy to track tokens and costs directly, so I found a third-party platform and ran a round of similar tasks using GPT-5.4 and GPT-5.5 in Codex, setting the thinking mode to high. The result was very straightforward: simple questions were relatively mild (in terms of cost), with GPT-5.5 being about 30% more expensive than GPT-5.4; however, once complex tasks were involved, the costs shot up to 2.6 times, and both the request count and token consumption increased simultaneously.

My current assessment is very straightforward: this isn’t something that can be decided just because of the statement “5.5 has a higher unit price.” In simple tasks, the cost mainly comes from the unit price; but in complex tasks, what is actually expensive is the entire calling chain (or execution flow). However, looking at it another way, 5.5 does genuinely feel like it’s absorbing your rework costs for you. The model is more willing to think through multiple steps, perform more actions, and check things more thoroughly. Ultimately, the billing isn’t based on a single answer; it’s based on the complete set of actions, which also minimizes the number of back-and-forth cycles required from the human user.

Initially, I didn’t actually plan on testing it. When I came across the news that OpenAI was releasing ChatGPT Images 2.0 on April 21, 2026, my first reaction was just “another image version update.” However, when I checked the Artificial Analysis leaderboard and saw that GPT Image 2 (high) ranked first for text-to-image generation with an Elo of 1332, I felt a bit compelled to test it anyway.

The results are quite impressive; the Chinese output is excellent, it can handle comics, and character/narrative consistency across multiple continuous images has also improved. However, as I tested it further, I felt that what is truly worth discussing this time isn’t “it draws better,” but rather “it starts making things that were previously taken as default truths seem unreliable.” This subject matter is more complicated than a simple leaderboard ranking.

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.

These past couple of days, I came across Anthropic’s Project Glasswing, which is scheduled for release on April 7, 2026. My first reaction was a bit stunned. It wasn’t because another model scored higher, but because it locked the top-tier capabilities into a small circle, initially reserved for defensive players like AWS, Apple, Google, Microsoft, and Linux Foundation.

My own judgment is very direct: This matter is more important than another benchmark record. What frontier AI companies are selling now is no longer just the model itself, but an entire set of access controls—“who gets the capability first, how much capability they get, and what kind of auditing and constraints they have to endure after receiving it.” Models are becoming increasingly like dangerous tools, and the release rhythm is becoming more like issuing licenses.