A note to anyone feeling anxious about AI

A note to anyone feeling anxious about AI

> 作者：李笑来 · 2026年5月21日 · [原文链接](https://lixiaolai.com/articles/2026-05-21/ai-anxiety-honest-note)
> 本篇原文为英文，暂无中文译文，下面是英文原文。

If you're not technical, and you keep reading headlines about *"AI generates entire films from one sentence"* or watching demo reels that look like Hollywood — and feeling the floor tilt under you — here's what I'd want you to know.

**Your panic is calibrated to the demos, not to the reality. They're not the same thing, and the gap is much wider than the headlines admit.**

Let me explain this using a real, concrete example, because the abstract reassurance ("don't worry, it's not as advanced as it looks") is what people always say and it never lands.

NOTE

*A note before the example.* I'm going to walk through a specific open-source project to make the pattern concrete, and I'll name it. This is **not** an attack on the lab that wrote it. The pattern I'm describing — polished marketing wrapped around partly-finished code — is the rule across viral AI projects right now, not the exception. Princeton computer scientists Arvind Narayanan and Sayash Kapoor have a book-length name for this pattern — they call it *AI snake oil*, and they argue it's industry-wide rather than the failing of any single team. The lab whose project I'm using here did something genuinely valuable: they shipped a thoughtful architectural sketch and working integration code that others can learn from. The problem isn't them; the problem is the gap between how such projects are presented to the world and what they actually do. This project is simply the case I happen to know in detail. Replace its name throughout this article with nearly any other top-starred AI project of the last eighteen months, and most of the specifics would still apply.

There's an open-source project called ViMax. It's posted on GitHub, the place where software is published. It has 6,500 "stars" — GitHub's version of a thumbs-up. It comes from a respected university lab. Its README — the front page of the project — claims it can take a one-line idea like *"a cat and a dog meet a new cat"* and produce a complete short film, with consistent characters, professional cinematography, scenes that flow together, and synchronized audio. The page shows a grid of polished demo videos:

A non-technical reader sees this and reasonably concludes: **"AI can already make full movies. Filmmakers are about to be obsolete. My creative work doesn't matter anymore. My children won't have careers in storytelling."**

Here is what an honest read of the actual code shows:

1. **The headline "quality check" feature — the part that's supposed to make sure characters look consistent across the film — exists as a file in the project but is never actually run.** It's like a car ad that brags about airbags, when the airbags aren't installed.
2. **A whole pipeline labeled "Novel-to-Movie" in the README won't even start.** The code refers to files and folders that don't exist in the project. It's been shipped as if it works.
3. **There are no quality tests anywhere.** The program generates an image once, takes whatever comes back, and uses it. If the image is wrong, the program doesn't notice. The next step uses the wrong image as its reference and the error compounds. A bad scene cannot be regenerated except by manually deleting files.
4. **The demos in the README were cherry-picked.** No film made with this tool, end-to-end, with no human intervention, is published anywhere. Probably because they don't reliably come out watchable.
5. **The 6,500 stars** reflect that the README is well-written, the demo reel is compelling, and the lab name is recognizable. They do not reflect that anyone has used this tool to make something real. Most of the people who starred it never ran the code.

This is not a small gap between "what the front page claims" and "what the technology actually does." It's a chasm.

The same gap exists across the AI industry right now. Not always to the same degree, but it's the rule, not the exception:

- **Demos are best-of-many.** When you see an AI demo, you are seeing the one good output from ten or a hundred or a thousand attempts, often with a human curating which one to show you. The system is not capable of producing that output on demand for you. The cherry-picking happens even at the level of which model versions get evaluated on industry leaderboards: an analysis of 2.8 million model comparisons on the LMArena benchmark found that selective submissions inflated published scores by up to 100 points.
- **Architecture pitches are not products.** A diagram showing "AI Director → AI Screenwriter → AI Cinematographer → Finished Movie" describes an aspiration, not a tool. Building each of those arrows reliably is the work of years and is mostly not done. The most-starred AI project of 2023, AutoGPT, promised exactly this kind of autonomous-agent architecture; Amazon researchers measured it completing a basic shopping task **24% of the time**. By September 2024, BabyAGI — the second most-famous example — had been archived by its own author.
- **"From idea to finished video" is a marketing sentence, not a measurement.** Nobody is measuring what "finished" means, what quality bar, what length, what coherence, what failure rate. Words like "complete," "finished," "professional" are doing enormous unsupervised work in these claims. This pattern is now legally actionable: since March 2024 the U.S. Securities and Exchange Commission has been bringing enforcement cases against companies for what it calls "AI washing" — materially overstating AI capabilities to investors. Regulators don't invent named enforcement programs for one-off conduct.
- **Stars and download counts are vanity metrics.** They measure attention, not capability. An academic analysis of GitHub repositories (*The Fault in Our Stars*, NDSS MADWeb 2024) found no statistically significant correlation between a project's quality and its star count, and a 2026 study from Carnegie Mellon, NC State, and Socket identified roughly six million suspected *fake* stars across 18,617 repositories. The metric is not just weakly correlated with quality — it's actively gamed. A tool can have 50,000 stars and not work; a tool can be quietly excellent with 200 stars. The number tells you about hype velocity, not functional value.

Everything I've described so far — the curated demos, the inflated star counts, the architectural diagrams that describe wishes — those are tells you can spot on the surface of a project's web page. But there's a deeper tell, and once you know what to look for, you can use it without being a software engineer yourself.

**The tell is whether the underlying code is cared for.**

Real, durable software — the kind that runs banks, hospitals, the airplane you flew last month — has visible signs of care, the same way a well-maintained restaurant kitchen has signs of care even if you're not a chef. There are *tests* — small programs that verify the software actually does what it claims. Broken features are *deleted*, not shipped alongside working ones. Errors are handled deliberately, not swept under a rug. The setup instructions work on a stranger's computer, not just on the original author's. The whole thing is consistent with itself, not a layer of half-finished experiments piled on top of older half-finished experiments.

Most viral AI projects of this era fail nearly all of these tests. Using ViMax again, since it's the case we already opened up:

- **The entire project contains two test files, both contributed by an outside volunteer in a single pull request** that added a different feature (support for a Chinese AI provider), and the tests only cover *that* feature. Here's the part that makes it worse, not better: the two test files are actually well-written — a competent worked example of how this codebase *could* be tested. The lab merged the contribution, saw what good testing of their own code looks like, and then wrote no similar tests for any of the project's other roughly twenty-five modules. The absence of testing isn't a *limitation* of the project — it's a *choice* the project's authors kept making after being shown an alternative.
- **A whole pipeline called "Novel-to-Movie" is shipped as part of the project but doesn't function at all.** It references files and folders that don't exist in the project. Anyone who tried to run it would get an immediate error. This is the software equivalent of a restaurant menu listing dishes the kitchen can't make.
- **The headline "consistency check" feature** — the one the README brags about, the part that's supposed to make sure characters look the same across the whole film — **was written, but was never connected to the program that actually runs.** It's a hospital that built an emergency room and then forgot to install the doors.
- **The same operation is wrapped in two retry systems at once**, layered without anyone noticing. What should retry three times retries up to nine. This is what happens when patches are stacked over months and nobody cleans up.
- **The install instructions hard-code a Chinese university's software mirror as the default download source**, which works fine for the lab that wrote it and fails or runs glacially slow for everyone else in the world. Nobody on the team tried installing it from scratch as an outsider would.

None of this requires programming knowledge to evaluate. You can ask plain questions:

- Does the project have tests?
- Are there shipped features that don't actually work?
- Is what's claimed on the front page actually built and connected?
- Does the install procedure work for a stranger on a normal computer?
- Is there obvious dead code lying next to working code?

A project that fails those questions is, regardless of how many stars it has, how slick its README looks, or which university stamped it — a *sketch*, not a tool. Sketches are valuable. They demonstrate ideas. They inspire other people to build the real version. But a sketch is not what you should base your anxiety on, because a sketch is exactly the kind of thing whose pitch deck wildly overstates its capability.

**This is the strongest single tell you have**, and it's the one the AI hype cycle works hardest to keep out of view. Marketing pages are designed to be looked at. Code is not. The mismatch between the two — the polished front, the held-together-with-tape inside — is where most of the AI-anxiety industry lives. Once you've learned to look under the hood (or, more realistically, to ask someone trustworthy to do it for you), the panic loses most of its grip. You can see what's actually there.

AI demos right now are **concept cars at an auto show**. The concept car drives onto the stage. It looks like the future. It has features you've never seen. The headlights swivel. The doors open like wings.

What you are not shown:

- The car is towed back off the stage. It cannot actually drive a city block reliably.
- It would not pass any safety inspection.
- The production version, if it ever exists, will be five years away and will lack two-thirds of the demoed features.
- Many concept cars never become production cars at all.

When you see an AI video demo, you are watching the concept car cross the stage. You are not watching a tool that anyone can drive home. The gap between those two things is where most of the panic is being generated.

It would be dishonest to swing the other way and tell you "AI is fake, don't worry." It isn't fake. Here's a more honest picture:

**Genuinely true today:**

- AI can generate beautiful 5-to-10-second video clips from a text prompt. This was impossible three years ago. It is now real, and the clips can be stunning.
- AI is being usefully integrated into real creative workflows — as a sketching tool, a storyboarding aid, an asset generator, a way to test ideas quickly. Working creatives are using it to do their existing work faster, not to replace themselves.
- AI is changing some kinds of jobs in concrete ways — stock photography, certain illustration work, some kinds of copywriting. Not by replacing the *people* in those fields immediately, but by changing what they produce and how.

**Not true today, despite the demos:**

- AI cannot, on demand, generate a coherent feature film, or a coherent short film, or even a coherent 90-second narrative video, from a single prompt, without extensive human intervention and many failed attempts.
- AI does not "understand" stories, characters, or audiences in the way storytelling requires. It pattern-matches across very large amounts of text and image data, which is a different thing.
- AI is not on a smooth, inevitable curve to replacing filmmaking, music, or writing. It's on a *spiky* curve where some things get dramatically easier and other things stay stubbornly hard, and predicting which is which is much harder than the demos suggest.

**Worth thinking carefully about (but not panicking over):**

- The economic value of certain *easily automatable* creative tasks is genuinely dropping. If your work consists of producing the kind of output that AI now produces in 30 seconds — generic stock imagery, formulaic marketing copy, soulless explainer videos — that work is going to compress in value over the next few years.
- The economic value of *judgment, taste, originality, and craft* is, if anything, increasing — because so much mediocre AI output is flooding the channels that human-curated, human-crafted work stands out more.
- The right response to AI is not to retreat from creative work but to invest more in the parts of it that AI is bad at: specificity, point of view, lived experience, deliberate choice, real risk.

- **Try the tools yourself.** Twenty dollars and twenty minutes on Runway, Pika, Sora, or any consumer AI video tool will teach you more than a hundred articles. You will discover both that AI is more capable than you feared in some ways, and far more limited than the demos suggested in others.
- **Watch the full process, not just the output.** When you see a demo, ask: *what didn't they show me?* How many attempts? How much hand-curation? What was the prompt actually? How long did it take? Anything they didn't show you, assume the truth is worse than the highlight reel.
- **Notice what's missing from claims.** "From idea to finished video" doesn't claim *reliably,* at *quality,* of any *length,* without *human help.* Those omissions are doing all the work.
- **Distrust round numbers and stars.** Anything with viral metrics has been optimized for virality, which is mostly orthogonal to capability.
- **Find someone calibrated.** A friend who actually works in software or in AI can save you weeks of panic in a 30-minute conversation. Not someone who *talks about* AI on social media — those people have their own incentives. Someone who builds with it.
- **Recognize the panic itself is a product.** Doomsday framings drive clicks and subscriptions and political donations, and the economy around your AI anxiety isn't metaphorical: the Future of Life Institute has been funding viral AI-doom content at a stated $100,000 per month through a digital-media accelerator, post-ChatGPT media analyses document a measurable shift in news coverage toward danger framing and sensational headlines, and the doomers themselves are organized and well-resourced. Some of the panic being sold to you is being manufactured, deliberately, by people who profit from your fear. That doesn't mean nothing is changing — but it means the *intensity* of your anxiety is being deliberately tuned upward, and you don't have to accept that tuning.

The version of AI you see in the demos and the headlines is not the version that exists. The version that exists is genuinely impressive in narrow ways, genuinely limited in wide ways, and genuinely *not* about to render human creativity obsolete next year — or the year after, or probably the year after that.

Some things will change. Some jobs will reshape. Some industries will compress. That's been true since the loom. It is not the same as "everything you do no longer matters."

If you take one thing from this: **the gap between what AI is marketed as doing and what AI actually does is, right now, larger than at any point in the technology's history.** Your panic is responding to the marketing. Reality is much messier, much slower, and much more navigable than the marketing wants you to believe.

You have more time, more agency, and more relevance than the front pages are telling you.

The specific claims about ViMax in this article all point to publicly viewable files on GitHub. If you'd like to confirm any of them — or if the project has changed since this was written — here are the direct links, grouped by which claim each one backs.

**The project itself**

- Repository: https://github.com/HKUDS/ViMax — current star count is visible at the top right of the page.
- Full README: https://github.com/HKUDS/ViMax/blob/main/readme.md — every marketing claim quoted in this article is from this page.

**Claim: the "quality check" feature exists as a file but is never connected to the running program.**

- The file: https://github.com/HKUDS/ViMax/blob/main/agents/best\_image\_selector.py
- *How to verify:* use GitHub's search box (top of the repo) and search the repository for `BestImageSelector`. You'll find it defined in this one file and referenced nowhere else.

**Claim: the "Novel-to-Movie" pipeline is shipped but doesn't function.**

- The file: https://github.com/HKUDS/ViMax/blob/main/pipelines/novel2movie\_pipeline.py
- *How to verify:* read the very first line — `# TODO: NOT IMPLEMENTED YET` — and look at the imports at the top. It imports `from components.event import Event` and `from pipelines.base import BasePipeline`. Then look at the repository's file list: there is no `components/` folder and no `pipelines/base.py`. The pipeline cannot start.

**Claim: there are essentially no tests, and the tests that exist are there because of an outside contributor — not the lab.**

- The tests folder: https://github.com/HKUDS/ViMax/tree/main/tests
- The pull request the tests arrived in: https://github.com/HKUDS/ViMax/pull/36 (MiniMax provider support, by an external contributor — note that the tests are tied to *that* feature, not to the project's video-generation pipeline)
- *How to verify:* count the files in the tests folder — two, both about a feature unrelated to video generation. Then read them: they're actually well-written. That's the most damning detail, not a redeeming one. The lab was shown exactly what good testing of this codebase looks like, merged the contribution, and then did not write similar tests for any of the project's other modules. The absence of testing is a choice, not a constraint.

**Claim: the same operation is wrapped in two retry systems at once.**

- The file: https://github.com/HKUDS/ViMax/blob/main/tools/image\_generator\_nanobanana\_google\_api.py
- *How to verify:* note the `@retry(stop=stop_after_attempt(3))` decorator on the `generate_single_image` method, and then look inside the method for the `for attempt in range(max_retries)` loop that also retries up to 3 times. Both run in series on every call.

**Claim: the install instructions default to a Chinese university's software mirror.**

- The file: https://github.com/HKUDS/ViMax/blob/main/pyproject.toml
- *How to verify:* scroll to the `[[index]]` section near the bottom. The `pypi.tuna.tsinghua.edu.cn` mirror is set as `default = true`.

*All links verified live on 2026-05-21. If any have moved or been changed since, the main repository page remains the canonical entry point and the contents can be browsed from there.*

If you'd like the research behind the claims in this article — that the polished-marketing-around-broken-code pattern is industry-wide, that benchmarks are routinely gamed, that star counts are actively faked, that the panic itself is a funded industry — here are the most authoritative sources, grouped by topic.

**On the AI hype pattern itself**

- ***AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference*** by Arvind Narayanan and Sayash Kapoor (Princeton University Press, 2024). The book-length version of the argument in this article, by two of the most-cited academic critics of AI overpromise.
- Computer Science Researchers Call Out AI Hype as 'Snake Oil' — a short Princeton Alumni Weekly profile of the authors and their core claims.

**On the reproducibility crisis in machine learning**

- Kapoor, S. & Narayanan, A. (2023). *Leakage and the reproducibility crisis in machine-learning-based science* — PubMed Central full text; arXiv preprint here. Documents 17 scientific fields and 294 papers where data leakage produced wildly overoptimistic conclusions.
- *Nature* news coverage: Could machine learning fuel a reproducibility crisis in science?

**On benchmark gaming and cherry-picked demos**

- *Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation* — arXiv 2502.06559, the most comprehensive academic survey of the problem.
- What Is Benchmark Gaming in AI? Why Self-Reported Scores Are Often Inflated — accessible summary covering the LMArena finding (selective submissions inflating scores by up to 100 points).

**On GitHub stars as vanity / faked metrics**

- *The Fault in Our Stars: An Analysis of GitHub Stars as an Importance Metric for Web Source Code* — NDSS MADWeb 2024 workshop paper finding no statistically significant correlation between a project's quality and its star count.
- Inside GitHub's Fake Star Economy — summary of the 2026 ICSE study (Carnegie Mellon, NC State, Socket) identifying \~6 million suspected fake stars across 18,617 repositories.

**The AutoGPT / BabyAGI cautionary tale**

- AutoGPT Got 100K Stars and Then What? — retrospective on the most-starred AI project of 2023, including the 24% shopping-task success rate and BabyAGI's September-2024 archival by its own author.

**On SEC "AI washing" enforcement**

- SEC Continues to Target "AI Washing" — legal analysis of the enforcement pattern, from the first 2024 cases through 2025.
- SEC heightens enforcement for AI-related disclosures — Norton Rose Fulbright overview of the regulatory regime.

**On the economy of manufactured AI panic**

- What 10 Studies Reveal About AI Panic in the Media — meta-analysis of post-ChatGPT media-coverage shifts, including the named funders of viral AI-doom content.
- The AI doomers feel undeterred — *MIT Technology Review* (December 2025).
- The AI Technopanic and Its Effects — Abundance Institute report on the policy consequences of manufactured AI anxiety.

*Further-reading links also verified live on 2026-05-21.*

版权声明：© 超限思维 · LookMinds。本页为己方整理的亲口亲笔原稿处理稿/编者新译，非原文替代；原文版权归原作者/平台。本人原作请回原链。