AI giants, enter the dark forest

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Text | Xiang Xianzhi

Liu Cixin wrote about a metaphor later quoted countless times in “The Three-Body Problem”—the Dark Forest. Every civilization is a hunter armed with a gun; whoever exposes themselves first, dies first. It’s not that there are no people in the forest, but everyone knows that lighting a lamp will attract bullets, so everyone keeps their lights off.

In spring 2026, top AI labs entered this kind of dark forest.

On April 16, Anthropic released Claude Opus 4.7 first. On the same day, they made an unusual move—publicly admitting that Opus 4.7’s performance is not as good as an unreleased model Mythos, citing safety concerns.

On April 23, OpenAI posted GPT-5.5 on their official website. On the same day, Anthropic published an incident review report titled “An update on recent Claude Code quality reports,” admitting that Claude Code has indeed become dumber over the past month—one move to release a new version, one to acknowledge mistakes. But this “new king” almost boasted: we admit Claude is temporarily dumber—don’t forget, we still have Mythos hidden.

On April 24, the “mysterious eastern power” DeepSeek V4 Preview launched, with Liang Wenfeng’s team officially announcing a deep integration of the model with Huawei Ascend 950PR; but everyone understood—the truly “full-blooded” V4 Pro Max can only be released after the Ascend 950 super-node mass production in the second half of the year.

Three companies, three actions. On the surface, each follows their own product rhythm, but taken together, one thing emerges:

Each holds at least one “gun”—a more powerful model than the public version, a next-generation architecture not yet available to the public, a chip super-node not yet widely deployed. But none dares to raise that gun first.

Because in this industry, the cost of “being first to reveal” is never just about leaks. Being first means handing over your capability ceiling to your competitors as a reference; it means bearing the full firepower of safety reviews, tighter regulations, and public opinion pressure; it means turning yourself into a moving target that all rivals will aim at next. There are no heroes in the forest—every person who fires first becomes the next one to be targeted.

So the most rational choice for hunters is to turn off lights, hold their breath, and hide their weapons behind their backs.

This is the optimal game-theoretic solution.

Anthropic’s Confidence

Claude, over the past month, has almost staged the worst version release.

Having updated Opus 4.7 early, Anthropic still dominates various leaderboards, and also secretly holds Mythos—available only to enterprise clients—showing a calm, unhurried stance.

But during this cycle, Claude 4.7’s user experience was arguably the worst—“overwhelmed with negative reviews.”

In early March, Anthropic changed the default reasoning depth of Claude Code from high to medium. The decision is understandable: in high mode, the UI often appears frozen, responses are slow, frustrating paying users. But they didn’t announce this at the time.

At the end of March, they launched an “efficiency optimization”—if Claude Code sessions are idle for over an hour, the system clears the reasoning block. Designed to save computing power, but in practice, after each dialogue, Claude acts as if it has amnesia, forgetting the context completely. Developer communities flooded with complaints: “Claude no longer remembers what I asked it to do last time.”

Recently, a third issue occurred—adding a command to compress verbosity in system prompts. Anthropic later admitted this caused a 3% drop in Claude Code’s coding quality.

These three issues stacked together led an AMD senior director to write on GitHub: “Claude has regressed to the point it cannot be trusted to perform complex engineering” (Claude has regressed to a point where it can no longer be trusted to handle complex engineering tasks). Axios’s April 16 article “Anthropic’s AI downgrade stings power users” brought this into mainstream view.

Then Anthropic admitted there were indeed problems.

On April 7, they quietly rolled back the reasoning effort adjustment; on April 10, fixed a cache bug; on April 20, removed the verbosity compression prompt. But the full incident review report only came out on April 23—coincidentally, the same day GPT-5.5 was publicly released.

This slight “oh, our engineering strategy has some bugs, just fix it” attitude, contrasts sharply with OpenAI’s heavyweight release, just a day apart. Not likely a coincidence.

More intriguing is that when Opus 4.7 was released, Anthropic made an unusual move: publicly admitting that Opus 4.7’s performance is inferior to an unreleased model—Mythos. This is clearly a “strategic retreat”—Anthropic keeps their strongest capabilities on the enterprise side, not rushing to release to the public, because the team isn’t ready to launch Mythos.

This explanation is plausible. But from a business narrative perspective, another half is equally true: Anthropic waited six weeks before publicly admitting Claude Code’s regression, only bringing up the issue when OpenAI was about to release new capabilities. If not for intense industry pressure, if Opus 4.7 hadn’t already proved “we still have a backup,” this statement might never have come.

On Claude’s side, squeezing toothpaste isn’t about deliberately crippling capabilities, but about: the release rhythm of capabilities and the disclosure of issues follow the pace of competitors.

Revealing their most advanced capabilities will inevitably make them targets. Or, from Anthropic’s view, since the pressure from Claude 4.6 on competitors hasn’t fully eased, there’s no need to deploy stronger capabilities now.

OpenAI’s old tricks

If Anthropic is “hiding Mythos,” OpenAI’s squeezing is more covert—it leaves capability release to their server load curves and a tiered mechanism called auto-router.

On April 23, GPT-5.5 was released, and Simon Willison (co-founder of Django, well-known independent AI reviewer) wrote cautiously on his blog: “It’s not a dramatic departure from what we’ve had before.”

He added a key piece of information: GPT-5.5 is the first fully retrained base model since GPT-4.5; the previous half-year’s versions 5.1, 5.2, 5.3, 5.4 are all incremental updates. In other words, OpenAI’s four small updates were all released cautiously—uncertain what competitors might release.

“Cautiously updating” can be simply called squeezing toothpaste.

But a more telling scene happened just hours after GPT-5.5 launched. Codex users on GitHub filed Issue #19241, complaining that Fast mode was initially very fast, but as more users joined, it visibly slowed down, yet billing still charged at the Fast tier. The familiar wording: “Please investigate whether GPT-5.5 Fast mode is being downgraded under high load.”

This is almost an exact replay of August 7, 2025, on GPT-5’s debut—Reddit r/ChatGPT topped with “GPT-5 is horrible” over 4,600 upvotes, and Sam Altman admitted in an AMA the next day: “the autoswitcher broke… GPT-5 seemed way dumber”—acknowledging the router was downgrading performance behind the scenes.

The same script, played again eight months later.

Even more amusing, on the day before GPT-5.5’s official release, OpenAI’s internal staging environment was mistakenly pushed to production by their Codex, caught by several Pro users’ screenshots, fixed within minutes, but the leaked content spread widely. Besides GPT-5.5 itself, there were versions called Glacier (“Intelligence that moves continents”), Heisenberg (a life sciences model), Arcanine (an unknown-purpose model), and others like oai-2.1.

In other words, at the same time OpenAI announced GPT-5.5 as “next-gen,” internally at least 5 to 6 parallel product lines were running, none yet public.

OpenAI admits it. In their 2026 official roadmap, they used a long-discussed term—capability overhang—to acknowledge the huge gap between the true capabilities of large models and what users can actually access.

Sound familiar? It’s almost the same wording as Anthropic’s Mythos narrative. Even if the Codex leak on April 22 was a mistake, OpenAI’s inclusion of “capability overhang” in their roadmap signals clearly: they still have plenty in reserve, and it’s up to users to see how it unfolds.

They hold far more than they sell, and that’s why they keep squeezing. The 24 hours after GPT-5.5’s release turned this premise into a live broadcast.

DeepSeek’s patience

DeepSeek’s “squeezing” approach has changed—no longer hiding capabilities, but waiting for a more suitable delivery moment.

1.6T MoE, 1M context, Pro/Flash dual specs, priced at 3.48 per 1M tokens—only a fraction of GPT-5.5’s scale, and a tenth of Opus 4.7’s. Independent overseas reviewers summarized: performance close to but slightly below GPT-5.4 / Gemini 3.1-Pro, with a price that “breaks the economics of frontier labs.”

But within DeepSeek’s own framework, V4 Preview is already more expensive than V3’s “bizarrely cheap” price. Everyone knows—this isn’t a full-blooded version.

The full story of DeepSeek V4 isn’t about the release itself, nor about it being the starting point.

It traces back to the unreleased release of R2 in 2025. R2 was scheduled for May 2025 but delayed to fall/winter. The entire Chinese DeepSeek infrastructure shifted to Huawei’s CANN ecosystem. For any lab, this isn’t a quarterly project—compilers, operators, communication libraries, inference frameworks, MoE routing—all need rewriting.

This time, V4 is DeepSeek’s first official inclusion of Ascend into the training hardware list. V4 is the first version of hybrid training—Ascend’s first appearance.

But the next-generation chip Ascend 950DT, optimized for large-scale training, is scheduled for mass production in Q4 2026 according to Huawei’s roadmap. That means V4 training can only run on the previous generation 950PR; to make the full-blooded 1.6T MoE version like V4 Pro Max train thoroughly and scale well, the next-gen hardware must arrive.

The real engineering challenge isn’t “whether V4 can be trained”—it’s already trained—it’s “how to run V4 fully, stably, and cost-effectively on Ascend.”

Ascend 950PR will mass produce in Q1 2026, with 1.56 PFLOPS FP4 compute, 112GB on-chip memory, surpassing Nvidia’s H20 on paper. But from a single chip to a large super-node capable of handling millions of tokens per second reliably is a different story. The full-blooded V4 Pro Max aims at this “super-node”—a large-scale Ascend 950 cluster, arriving gradually in the second half of 2026.

This strategy differs completely from the first two. Anthropic and OpenAI squeeze toothpaste—holding back their stronger capabilities until the price can drop further. DeepSeek’s squeeze is about waiting for a version that can run at full capacity, at a lower cost.

This difference is crucial.

DeepSeek’s real killer isn’t “cutting-edge performance,” but “reducing token costs to levels others dare not.” The V4 Preview adapted to Nvidia and Ascend 950PR, but to achieve full-scale inference at production, they must wait for the super-node hardware. When that arrives, two things will happen simultaneously: the full capability of V4 Pro Max can be unleashed, and inference costs and API prices will drop again—more deadly for a company that relies on price to dominate the market.

The “DeepSeek moment” people expected in early 2025 didn’t recur in this release. Instead, the V4 Preview is a trailer; the real show is in the second half of the year—“DeepSeek + Huawei Ascend.”

From this perspective, Liang Wenfeng’s team isn’t doing a “forced hiding,” but a strategic “choice”—delaying the strongest version’s debut until the day of large-scale domestic super-node deployment. Before that, they’ll reinforce the cost-performance narrative with the V4 Preview.

DeepSeek’s burden isn’t about making domestic large models top the leaderboard with a “long board” narrative, but about a “systematic narrative”—making chips, training, inference, and pricing all work together—more important than the former.

Just days ago, Jensen Huang said on Dwarkesh Patel’s podcast that if DeepSeek launches first on Huawei chips, “that would be a horrible outcome for our nation.”

Nvidia still controls top-tier compute power. But according to Huang’s “AI five-layer cake”—energy, chips, infrastructure, models, applications—domestic large model solutions are emerging at every layer, shrinking the gap visibly. Filling the last piece—chips—DeepSeek’s open-source large model story is a bigger story than the US’s: a crucial step toward global AI equality, with less cost, enabling a more efficient, equitable intelligent society.

Allowing the world to bypass some hegemon-controlled advanced compute, entering an efficient intelligent society.

Epilogue

Anthropic’s “hiding”—is active. They have Mythos but don’t release it, citing safety.

OpenAI’s “hiding”—is structural. They have Pro tier, but don’t often give it out, citing infrastructure and tiered pricing.

DeepSeek’s “hiding”—is necessary. It’s about a whole social intelligence leap narrative.

But from another angle, it resembles Liu Cixin’s dark forest: in this pitch-black forest of intelligence, every top hunter refuses to fire the first shot.

Exposure means total vulnerability, no cards left, becoming a target for others.

No one knows who will fire the most lethal shot first. But one thing’s for sure: every model you use today isn’t its true form.