This is the spot Burry told his Substack followers he was buying SOXX, NVDA, QQQ puts. At the same time he told Twitter at this exact spot "shorts aren't forever". Why do people listen to this guy? He literally lead his followers off a cliff while pretending on Twitter to sell his shorts. I'm a member of his Substack.... Well was, after his disastrous calls I stopped
I want to talk about this in a productive manner. I don't intend for this post just to make fun of him. But seriously, I think that his substack is now disastrous since he is actively losing money on nearly all his trades. I know that on a longer term basis maybe some of his trades will be eventually validated but you are currently better off just buying and holding SPY than listening to him.
Especially with his options trades. Since contracts expire, those can lead to irreversible losses. Burry showcased having QQQ and NVDA puts a month ago which are now down 50 percent. There is still time left on the options so theoretically they could still print but immediately leading his followers to take a 50 percent haircut is very troubling.
Nearly all of his stock picks are underperforming the market. Freddie/fannie, software companies, LULU for examples. Again he said that these picks might require time but how long can somebody hold onto that pain when people just holding an index fund without thinking are crushing their returns?
The disastrous Ryan Cohen interview is also very embarassing when Burry previously offered praise to Cohen.
Being early unfortunately is the same thing as being wrong. If the market doesn't collapse until years later what will it mean for his reputation?
(Note: i have not been following his trades myself)
New signal that we're probably extremely late cycle or near the top. When you see increased trolling here, don't waste your time with facts. Something that I've learned from reading about the history from financial speculation is that behavior never changes from crisis to crisis, it just shows up in the same asinine way. The older investors that got ruined taking excess risk get replaced by new dumbasses that have never lived through real economic hardship.
Even if you don't follow Burry and copy his trades 100%, just stick to your own investment process and ignore the noise. Actually evaluate the merits of the companies you are taking positions in against the future macro. Whether that would be a value investing, relative value spread trades, volatility arbitrage, or similar type strategies, stick to your core competences and ignore the dumbasses that brigade the sub.
History has proven again and again that they'll lose their shirts in a few months. Buffett and Burry have lived through many bubbles. This won't be the last one either.
Keep seeing “AI supercycle” / “prolonged cycle for AI” etc all over X…fund managers like Picet Asset Management shifting 30% of cash into AI stocks which have already gone up massively…do you think this will continue or is a correction ahead?
**TL;DR**: OpenAI and Anthropic aren't suddenly "good at enterprise transformation." They're partnering with private equity and consulting firms because LLMs still hallucinate by design, enterprises don't trust black-box outputs, and valuations demand revenue *now*. This isn't a go-to-market strategy—it's valuation maintenance disguised as innovation. The .com bubble didn't kill e-commerce; it killed companies that tried to compress social adoption with capital. Watch closely: this playbook looks familiar.
---
## 🎭 The News Hook: Why Are AI Startups Suddenly "Consulting Firms"?
Last month, two moves flew under the radar:
- **Anthropic** × Blackstone / Hellman & Friedman / Goldman Sachs → new *enterprise AI services company*
- **OpenAI** × TPG / Bain Capital → *"The Deployment Company"*
On the surface: smart distribution.
Reality check: **They're outsourcing credibility**.
If LLMs were truly ready for enterprise workflows, you wouldn't need McKinsey to convince a CIO to adopt them. You'd just show the ROI. But when your core technology:
- Hallucinates by statistical design [[OpenAI Blog](https://openai.com/index/why-language-models-hallucinate/)**\]**
- Struggles with "lost in the middle" in long contexts
- Can't guarantee safe, auditable multi-agent coordination
...you don't sell *capability*. You sell *urgency*. And who's best at manufacturing urgency for enterprise boards? Consultants.
> 🧭 **Burry-style insight**: When a tech company starts acting like a consulting firm, ask: *Is this scaling a product—or scaling a story?*
---
## 🔍 Follow the Incentives: Valuation Pressure ≠ Product Readiness
Let's be clear: OpenAI and Anthropic aren't "expanding into consulting" because they discovered a new competitive advantage. They're doing it because:
| Pressure | Reality |
|---|---|
| **Valuations** | OpenAI's implied valuation ($460B–$852B) demands enterprise-scale revenue *yesterday* |
| **Adoption friction** | Enterprises won't replace human workflows with models that "might hallucinate" |
| **Distribution gap** | Direct sales cycles for complex AI are 12–18 months. Consultants compress that to 3–6. |
| **Narrative defense** | "AGI is coming" creates FOMO. "Helpful copilot" does not. |
This isn't unique to AI. In the late 1990s, e-commerce startups hired "digital transformation" consultants to sell the *idea* of online shopping—before payments, logistics, and trust were ready. The result? Consultants got paid. Startups burned cash. The ecosystem matured... and Amazon, not Pets.com, captured the value.
> 📉 **Pattern recognition**: When distribution depends on selling anxiety rather than demonstrated ROI, the business model is fragile.
---
## ⚙️ The Technical Gap They're Not Solving (But Papers Already Have)
> The core issue isn't "LLMs are bad." It's that *deployment-ready safeguards exist in research papers but not in products*. Rushing enterprise adoption before they're integrated is how you get scale-level hallucination disasters.
### 1️⃣ Lost in the Middle → Structured Indexing Works
- **Problem**: Long contexts dilute attention; critical info in the middle gets ignored.
- **Solution**: Pre-structure data with **dual-layer summaries + indexes** to guide retrieval, not force search-in-noise.
- **Paper**: [Self-Describing Structured Data with Dual-Layer Guidance](https://www.researchgate.net/publication/403842614\_Self-Describing\_Structured\_Data\_with\_Dual-Layer\_Guidance\_A\_Lightweight\_Alternative\_to\_RAG\_for\_Precision\_Retrieval\_in\_Large-Scale\_LLM\_Knowledge\_Navigation)
- **Reality check**: Not productized. Enterprises deploying vanilla RAG today are accumulating *hallucination debt*.
### 2️⃣ Prompt Security → Semantic Monitoring Is Broken
- **Risk**: AI can hide intent *inside* seemingly benign output (steganographic collusion). Semantic monitoring alone won't catch it.
- **Papers**:
- [Steganographic Intent Detection](https://openreview.net/forum?id=Ylh8617Qyd)
- [Instruction Following ≠ Reward Function](https://arxiv.org/pdf/2602.20021)
- [Circuit-Breaking for MARL Safety](https://www.researchgate.net/publication/402611883\_Beyond\_Reward\_Suppression\_Reshaping\_Steganographic\_Communication\_Protocols\_in\_MARL\_via\_Dynamic\_Representational\_Circuit\_Breaking)
- **Practical fix**: Compress agent communication to simple signals (red/green) + statistical anomaly detection. Not sexy. Not deployed.
### 3️⃣ Real AGI Needs Constraints, Not Just Scale
- **Framework**: Predefine business-logic "elements," let LLMs *compose within verified boundaries* rather than invent freely.
- **Human-AI Handoff**: AI handles pattern matching & retrieval; humans handle boundary judgment & value tradeoffs.
- **Papers**:
- [Constraint-Driven Human-AI Collaboration](https://www.researchgate.net/publication/403842380\_A\_Constraint-Driven\_Framework\_for\_Process-Traceable\_HumanAI\_Collaboration)
- [Auditable Behavioral Inference Library](https://www.researchgate.net/publication/403951418\_From\_Explicit\_Elements\_to\_Implicit\_Intent\_A\_Predened\_Library\_for\_Auditable\_Behavioral\_Inference)
- **Key insight**: Experts don't know all answers—they know *when their reasoning fails*. Current LLM deployments simulate the opposite.
---
## 📉 The .com Parallel: It's Not About the Tech, It's About Timing
| 2000 E-commerce | 2026 Enterprise AI |
|---|---|
| Pets.com spent millions teaching people to buy pet food online | OpenAI spends millions teaching enterprises to trust hallucinating models |
| Webvan built warehouses before last-mile logistics existed | Anthropic builds "contextual retrieval" before enterprise data is structured |
| **Failure mode**: Burned cash educating a market that wasn't ready | **Failure mode**: Burning cash deploying models that aren't ready |
| **Winner**: Amazon, which picked "books"—lowest ecosystem dependency | **Potential winner**: Whoever picks the "books" of AI (code assist? knowledge retrieval?) and waits for the ecosystem to catch up |
> 🧭 **The Burry Thesis**: Intrinsic value ≠ narrative valuation.
> Amazon survived 2000 not by being "more visionary," but by picking a product that required minimal ecosystem support.
> Who's picking the "books" of AI today? (Hint: It's not the $460B-valued startups rushing into consulting partnerships.)
---
## ⚖️ Final Question for the Room
If you were a value investor looking at OpenAI/Anthropic today:
- Do you see a company with durable competitive advantages?
- Or a company with durable *capital pressure* to perform before readiness—and now using consulting partnerships to bridge the gap?
> *"The market can stay irrational longer than you can stay solvent."*
> But when a tech company starts selling urgency through consultants instead of ROI through capability? That's when the clock starts ticking.
*Not financial advice. Just connecting dots the market is ignoring.*
---
**Sources for deeper digging**:
- Hallucinations: [OpenAI: Why Language Models Hallucinate](https://openai.com/index/why-language-models-hallucinate/)
- Technical papers: All ResearchGate/OpenReview/arXiv links embedded above
- Financing context (background): [Bloomberg](https://www.bloomberg.com/news/articles/2026-05-08/softbank-cuts-target-for-openai-margin-loan-by-40-to-6-billion) | [AInvest](https://www.ainvest.com/news/softbank-6b-openai-loan-cut-signals-collateral-crack-64-6b-leveraged-bet-2605/)
*What's your take? Are AI-consulting JVs a smart distribution play—or a signal that the tech isn't ready for prime time? Happy to discuss with data.*
I have been trying to buy a bunch of the unloved names for a couple of years now and the hit rate is honestly bad. My thesis usually goes: market has overreacted to a short-term issue, balance sheet is fine, normalized earnings would put it at 8x, easy money, about six months later the issue turns out to be structural, and not cyclical. I am sitting on a 30% loser wondering how I missed it, I am starting to think I am pattern matching to 'Burry-style contrarian' but skipping the part where he actually does the work to verify the thing he is contrarian about is genuinely transient. To anyone who has actually pulled this off more than once, what is the missing step? Is it more channel checks? Is it more time? Am I weighting balance sheet over business quality and getting punished for it?
Mr Plastic Face Larry Ellison from Oracle & his son David Ellison have taken over TikTok & 60 minutes
Trump blocked TikTok, then forced China to sell it to his mates...
Now I'm spammed with Anduril, eating disorder content & nothing about the protests, criticisms of Trump or ICE...
Is any of our media not controlled by those guys?
Now Mr Plastic Face is quickly becoming the wealthiest person in the USA... he might even replace Elon LOL... I guess Mr Plastic Face is Trump's new favourite