---
# System prepended metadata

title: 'Sebastien Bubeck''s GPT-5 Pro Claim: A Reformatted Pre-Published Mathematical Solution and the Reality Behind the Hype'

---

# Sebastien Bubeck's GPT-5 Pro Claim: A Reformatted Pre-Published Mathematical Solution and the Reality Behind the Hype (v3.0)

```
Author: Niloy Deb Barma
Education: B.Tech in AI & Data Science
Developer: AllyCat Project (AI Alliance, Meta & IBM Joint Initiative)
Note: This analysis represents independent technical evaluation, not corporate position
Writing Enhancement: AI-assisted organization and presentation (Claude, Perplexity)

All analysis, evidence compilation, and conclusions represent the author's original work.
```

## The Broader Context: A Pattern of AI Hype Manipulation

This analysis of Sebastien Bubeck's GPT-5 Pro claims must be understood within a larger pattern of AI capability misrepresentation that has emerged over recent years. The systematic overhyping of AI capabilities by researchers, tech companies, and media has created significant problems:

*Educational Impact:* [Research shows AI hype is affecting student creativity and learning](https://www.psypost.org/beyond-the-ai-hype-real-effects-on-student-creativity-explored-in-new-research), with [unrealistic expectations about AI capabilities](https://www.wired.com/story/photo-essay-school-tech-hysteria) leading to poor educational outcomes and misguided career decisions.

*Business Failures:* [MIT studies indicate that 95% of generative AI projects are failing](https://timesofindia.indiatimes.com/technology/tech-news/mit-study-finds-95-of-generative-ai-projects-are-failing-only-hype-little-transformation/articleshow/123453071.cms), with massive investments based on inflated capability claims rather than realistic assessments. [Commonwealth Bank's AI failures](https://www.theaustralian.com.au/business/technology/commonwealth-banks-ai-fail-exposes-more-than-45-jobs-and-sounds-a-warning-for-all-businesses/news-story/842131dd463ec10e6ee034dc330474bb) exemplify how corporate AI implementations often fall short of promotional promises.

*Market Distortions:* Even [OpenAI's Sam Altman has warned of an AI bubble](https://nypost.com/2025/08/18/business/openai-ceo-sam-altman-warns-of-ai-bubble-says-investors-are-overexcited-report), with [venture capitalists exhibiting "herd mentality"](https://www.businessinsider.com/vc-ai-herd-mentality-jay-hoag-tcv-kids-playing-soccer-2025-06) around AI investments despite limited real-world transformation. [Wall Street's AI trade faces significant skepticism](https://www.marketwatch.com/story/why-the-man-behind-the-haters-guide-to-the-ai-bubble-thinks-wall-streets-hottest-trade-will-go-bust-ac398ce0) from analysts tracking actual performance versus promotional claims.

*Systemic Issues:* The phenomenon of ["AI washing"](https://en.wikipedia.org/wiki/AI_washing) - where companies and researchers exaggerate AI capabilities for marketing or funding purposes - has become widespread enough to warrant [academic study](https://www.researchgate.net/publication/379509466_The_mechanisms_of_AI_hype_and_its_planetary_and_social_costs) and regulatory attention. [Mass delusion events](https://www.theatlantic.com/technology/archive/2025/08/ai-mass-delusion-event/683909) around AI capabilities have been documented across multiple sectors.

*Ongoing Reality Gaps:* Despite years of promises, [AI hype continues to outpace actual capabilities](https://www.kognition.info/ai-hype-vs-reality), with [consulting analysis](https://semantics-consulting.com/ai-hype-update-its-not-getting-any-better) showing the gap between marketing claims and practical implementation is not improving. [Academic research](https://arxiv.org/abs/2102.07536) and [recent studies](https://arxiv.org/abs/2502.07663) continue to document systematic overstatement of AI capabilities.

*Community Recognition:* Both [data science professionals](https://www.reddit.com/r/datascience/comments/1c41y7n) and [broader technical communities](https://www.reddit.com/r/changemyview/comments/1dkd43z) have increasingly recognized and discussed these patterns of AI capability misrepresentation.

*Student Vulnerability:* Young people entering AI fields are particularly susceptible to these inflated claims, making career and educational decisions based on promotional narratives rather than technical realities.

The Bubeck case represents a specific instance of this broader pattern - where promotional social media claims about AI mathematical capabilities were initially presented without proper context about existing human achievements, requiring community pressure to force accurate disclosure.
---
---
Before evaluating [Sebastien Bubeck's claims about GPT-5 Pro proving new mathematics](https://x.com/SebastienBubeck/status/1958198661139009862?t=2XeTcUSfGwQcWc2Gzr8XKA&s=19), it is crucial to clearly distinguish between the actual facts and how they have been presented publicly. The excitement about GPT-5 "cracking new interesting mathematics" has led to much hype, but many promotional statements do not align with the technical and factual realities.

#### The X Post


![1](https://hackmd.io/_uploads/Hy1Z2vwYgl.jpg)


![1 (3)](https://hackmd.io/_uploads/B1573PDKlx.jpg)


#### Comment Picture 1

![1 (4)](https://hackmd.io/_uploads/rJK4nwwKle.jpg)


#### Comment Picture 2

![1 (3)](https://hackmd.io/_uploads/HJZB3DvYlg.jpg)


#### Comment Picture 3

![1 (2)](https://hackmd.io/_uploads/rJZ8hwwtxg.jpg)


#### Comment Picture 4

![1 (1)](https://hackmd.io/_uploads/HknI3DPFxg.jpg)


## Complete Documentation of Bubeck's X Post and Comments Claims: The Full Evidentiary Record

The images above show the mathematical content, but the complete story requires examining Bubeck's actual X post sequence, which reveals a troubling pattern of strategic misrepresentation followed by forced admissions when challenged by the mathematical community.

### Phase 1: The Strategic Initial Presentation (Aug 20, 2025)

**Bubeck's Opening Posts:**

**Comment Picture 1:**
> "The paper in question is this one arxiv.org/pdf/2503.10138... which studies the following very natural question: in smooth convex optimization, under what conditions on the stepsize eta in gradient descent will the curve traced by the function value of the iterates be convex?"

**Comment Picture 1.1:**
> "In the v1 of the paper they prove that if eta is smaller than 1/L (L is the smoothness) then one gets this property, and if eta is larger than 1.75/L then they construct a counterexample. So the open problem was: what happens in the range [1/L, 1.75/L]."

**Critical Issue:** At this point, Bubeck presents the mathematical problem as genuinely "open" despite Version 2 (published April 2, 2025) having already solved it completely months earlier with the optimal 1.75/L bound.

**Comment Picture 1.2:**
> "As you can see in the top post, gpt-5-pro was able to improve the bound from this paper and showed that in fact eta can be taken to be as large as 1.5/L, so not quite fully closing the gap but making good progress. Def. a novel contribution that'd be worthy of a nice arxiv note."

**Multiple Demonstrably False Claims:**
- **"Improve the bound"** - GPT-5 Pro's 1.5/L bound is mathematically inferior to the already-published optimal 1.75/L bound
- **"Making good progress"** - Achieving a worse result than existing published work represents regression, not progress
- **"Novel contribution"** - The result was already superseded by superior published work months earlier
- **"Worthy of arxiv note"** - Made while knowing that optimal results already existed in the literature

### Phase 2: The Smoking Gun Admission (Aug 20, 2025)

**Bubeck's Comment Picture 1.4 - The Crucial Revelation:**
> "Now the only reason why I won't post this as an arxiv note, is that the humans actually beat gpt-5 to the punch :-). Namely the arxiv paper has a v2 arxiv.org/pdf/2503.10138... with an additional author and they closed the gap completely, showing that 1.75/L is the tight bound."

**This single comment completely destroys the entire promotional narrative:**
- **Explicit admission:** Version 2 exists with a mathematically superior result (1.75/L > 1.5/L)
- **Timeline deception:** Creates false impression that v2 appeared after GPT-5 Pro's work ("beat gpt-5 to the punch") when v2 was published months earlier
- **Retroactive invalidation:** Makes all previous claims about "novelty," "improvement," and "progress" demonstrably false
- **Knowledge confirmation:** Reveals he was aware of the superior results while making promotional claims

### Phase 3: Technical Defense and Community Challenge (Aug 20-21, 2025)

**Bubeck's Comment Picture 3:**
> "And yeah the fact that it proves 1.5/L and not the 1.75/L also shows it didn't just search for the v2. Also the above proof is very different from the v2 proof, it's more of an evolution of the v1 proof."

**Mark Erdmann's Critical Question:**
> "and there's some way to rule out that it didn't find the v2 via search? or is it that gpt-5-pro's proof is so different from the v2 that it wouldn't have mattered?"

**Bubeck's Response:**
> "yeah it's different from the v2 proof and also v2 is a better result actually"

**Final explicit admission that Version 2 achieves a superior result, completely contradicting earlier claims of GPT-5 Pro making "good progress."**

**Bubeck's Methodological Revelation (Aug 21):**
> "This is literally just gpt-5-pro. Note that this was my second attempt at this question, in the first attempt I just asked it to improve theorem 1 and it added more assumptions to do so. So my second prompt clarified that I want no additional assumptions."

**Critical Methodological Issues Revealed:**
- **Multiple prompt attempts:** Required iterative refinement to achieve desired output
- **Cherry-picking:** Only the "successful" second attempt was presented publicly
- **No systematic evaluation:** No comprehensive assessment methodology disclosed
- **Guided process:** Success required specific human prompt engineering

## The Mathematical Reality Check

- GPT-5 Pro did produce a mathematically valid proof that claimed to improve the convex optimization step-size bound from the classical $$1/L$$ to roughly $$1.5/L$$, working under the assumptions of Theorem 1 from version 1 of an established convex optimization paper.

- However, **both Version 2 (April 2, 2025) and Version 3 (June 28, 2025) had already established the optimal bound of $$\eta \leq 1.75/L$$** - significantly stronger than GPT-5 Pro's claimed $$1.5/L$$ bound.

- [Version 1](https://arxiv.org/pdf/2503.10138v1) of the paper guarantees convexity of the optimization curve for step sizes $$\eta \in (0, 1/L]$$, but leaves an open gap up to $$2/L$$ where gradient descent still converges without guaranteed convexity.

- [Version 2](https://arxiv.org/pdf/2503.10138v2) (publicly available by April 2, 2025) formally extends the convexity guarantee for step sizes up to $$\eta \leq \frac{7}{4L} = 1.75/L$$ using sophisticated weighted inequality techniques.

- [Version 3](https://arxiv.org/pdf/2503.10138v3) (published June 28, 2025) confirms that $$\eta \leq 1.75/L$$ is the **optimal, sharp bound**, definitively proving no improvement is possible and constructing counterexamples beyond this bound.

## Detailed Mathematical Line-by-Line Analysis

Here's the smoking gun - a direct mathematical comparison between GPT-5 Pro's output and Version 2's proof:

### GPT-5 Pro's Mathematical Steps:
```
1. Lower bound for Dₖ with a Bregman term.
   Dₖ ≥ η⟨gₖ₊₁, gₖ⟩ + 1/(2L) ‖Δₖ‖²

2. Upper bound for Dₖ₊₁ by convexity.
   Dₖ₊₁ ≤ η ‖gₖ₊₁‖²

3. Subtract and use cocoercivity once.
   Dₖ - Dₖ₊₁ ≥ (3/(2L) - η) ‖Δₖ‖² ≥ 0 whenever η ≤ 3/(2L)
```

### Version 2's Mathematical Foundation:
```
f(x₀) - f(x₁) ≥ η⟨∇f(x₁), ∇f(x₀)⟩ + 1/(2L) ‖∇f(x₁) - ∇f(x₀)‖²
[Identical Bregman divergence inequality]

f(x₁) - f(x₂) ≤ η ‖∇f(x₁)‖²
[Same convexity bound]
```

**The mathematical tools are IDENTICAL**:
- Same Bregman divergence: $$\frac{1}{2L} \|\nabla f(x) - \nabla f(y)\|^2 \leq f(x) - f(y) - \langle\nabla f(y), x - y\rangle$$
- Same cocoercivity property: $$\langle\nabla f(x) - \nabla f(y), x - y\rangle \geq \frac{1}{L} \|\nabla f(x) - \nabla f(y)\|^2$$
- Same target: proving $$f(x_2) - f(x_1) \geq f(x_1) - f(x_0)$$

**Only differences**: GPT-5 Pro uses cosmetic notation ($$g_k$$, $$\Delta_k$$, $$D_k$$) while Version 2 uses standard $$\nabla f(x_i)$$ notation.

## The Critical Performance Gap

| Method | Bound Achieved | Publication Date | Optimality Status |
|--------|---------------|------------------|-------------------|
| Version 1 | η ≤ 1/L | March 13, 2025 | Suboptimal |
| **Version 2** | **η ≤ 1.75/L** | **April 2, 2025** | **Optimal** |
| Version 3 | η ≤ 1.75/L | June 28, 2025 | Proven optimal with counterexamples |
| **GPT-5 Pro** | **η ≤ 1.5/L** | **After April 2025 in Aug 20, 2025** | **Suboptimal** |

**The devastating reality exposed by the X posts**: GPT-5 Pro achieved a **weaker bound** (1.5/L) than what human mathematicians had already proven optimal (1.75/L) months earlier, yet this was initially presented as "good progress" and a "novel contribution."

## Why GPT-5 Pro Got a Weaker Result

The mathematical analysis reveals the key difference:

**GPT-5 Pro's Approach**: Direct cocoercivity application
- Simple, straightforward proof path
- Gets coefficient: $$\frac{3}{2L} - \eta$$
- **Bound**: $$\eta \leq 1.5/L$$

**Version 2's Approach**: Sophisticated weighted combination  
- Multiply three inequalities by coefficients (3/2, 1/2, 1/2)
- Advanced algebraic manipulation: $$(7/(8L) - η/2) \|\nabla f(x_1) - \nabla f(x_0)\|^2$$
- **Stronger bound**: $$\eta \leq 1.75/L$$

GPT-5 Pro essentially reproduced the mathematical foundations but failed to discover the advanced optimization techniques that yield the optimal result.

## The Systematic Misrepresentation Pattern Revealed by the X Post Analysis

The complete documentation of Bubeck's posts reveals a three-phase deception pattern:

### Phase 1: Strategic Context Omission
- **False problem framing:** Present solved problem as genuinely open
- **Misleading progress claims:** Assert "improvement" and "good progress" for inferior results  
- **Fabricated novelty:** Claim "novel contribution" despite superior published work existing for months
- **Publication worthiness:** Suggest arxiv publication while knowing optimal results already exist

### Phase 2: Forced Transparency Under Community Pressure
- **Belated admission:** Acknowledge Version 2's existence and mathematical superiority only when pressed
- **Timeline manipulation:** Create false competitive narrative ("beat gpt-5 to the punch") when v2 was published months earlier
- **Face-saving framing:** Maintain promotional tone despite completely contradictory evidence

### Phase 3: Technical Rationalization and Deflection
- **Unsubstantiated differentiation:** Claim methodological differences without detailed mathematical proof
- **Flawed contamination logic:** Use inferior result as evidence against training data access  
- **Process opacity:** Deflect systematic evaluation questions, reveal cherry-picking only when directly asked

## The Overwhelming Evidence of Reproduction

- **Identical mathematical tools**: Exact same Bregman divergence and cocoercivity inequalities used in Version 2
- **Same proof structure**: Both target proving convexity through consecutive decrease analysis  
- **Only notational cosmetics**: $$x_k$$ changed to $$D_k$$, equation numbering variations
- **Timing issues**: Version 2 was publicly available well before GPT-5 Pro's demonstration
- **Suboptimal performance**: GPT-5 Pro's 1.5/L bound is weaker than the known optimal 1.75/L

This shows that GPT-5 Pro's output is essentially a **sophisticated copy-paste reformatted and paraphrased version of the known mathematical approach from Version 2**, applied with less advanced techniques, generated in response to carefully crafted user prompts asking for an improved step-size bound under the same assumptions.

## What the Claims Actually Represent vs. What Was Promoted

**What GPT-5 Pro Actually Did** (Legitimate Technical Achievement):
- Reproduced sophisticated mathematical reasoning from training data
- Correctly applied advanced convex optimization techniques  
- Generated a valid proof improving on Version 1's bound
- Demonstrated impressive mathematical tool mastery

**What the Promotional Claims Suggested** (Misleading Implications Exposed by X Posts):
- ❌ "New interesting mathematics" - **False**: reproduced existing techniques
- ❌ "Better bound" - **Misleading**: weaker than already-published optimal result  
- ❌ "Good progress" - **False**: regression from known optimal results
- ❌ "Novel contribution" - **False**: reformulated existing mathematical approach
- ❌ "Very different from v2 proof" - **False**: uses identical mathematical foundations
- ❌ Timeline implications - **Deceptive**: v2 was published months before, not after

## The Missing Context and Transparency Issues Exposed

The X post analysis reveals systematic omissions and transparency failures:

- Bubeck's initial posts **deliberately fail to mention the existence of Version 2's stronger 1.75/L bound**, misleading readers about GPT-5 Pro's actual performance relative to human mathematical achievements
- **No mention of Version 3's optimality proof**, which definitively closes the problem GPT-5 Pro was supposedly "solving"
- **Hidden methodology**: Multiple prompt attempts and cherry-picking revealed only when directly questioned
- **Missing training data disclosure** about potential access to Version 2
- **Overstated language**: Terms like "cracked reasoning" and "deep research agent" overstate the AI's originality when it achieved suboptimal results

## Training Data and Timeline Analysis

- GPT-5 and similar models operate on static training data without continuous retraining or real-time web browsing
- Version 2 was publicly available by April 2, 2025 - likely within GPT-5 Pro's training data cutoff
- GPT-5 Pro's 17-minute reasoning time aligns with known-user prompted recomposition of existing literature rather than autonomous discovery
- No evidence suggests GPT-5 Pro had access to Version 3's June 2025 optimality results
- The mathematical reproduction pattern suggests sophisticated training data reconstruction, not independent research

## The Broader Implications for AI Mathematical Claims

This case study reveals a **significant gap between promotional AI narratives and rigorous mathematical evaluation**:

- **Technical Capability vs. Innovation**: GPT-5 Pro demonstrated impressive mathematical assistance while falling short of mathematical discovery
- **Marketing vs. Mathematics**: Promotional terms obscured the reality that GPT-5 Pro achieved suboptimal results compared to published work
- **Transparency Standards**: The X post progression shows how initial misrepresentation required community pressure to force accurate disclosure
- **Human Attribution**: The definitive mathematical solution (optimal 1.75/L bound with sharpness proof) remains the achievement of human mathematicians Barzilai, Shamir, and Zamani
- **Responsible Discourse**: AI capabilities should be evaluated against existing literature, not just previous AI performance

## Recommendations Moving Forward

- **Transparency Requirements**: AI mathematical claims should include training data cutoffs, baseline comparisons, and complete literature reviews
- **Accurate Positioning**: Frame AI as sophisticated mathematical assistant, not independent researcher
- **Credit Attribution**: Acknowledge human mathematical contributions and prior work
- **Performance Context**: Always compare AI results against state-of-the-art human achievements, not just historical baselines
- **Community Verification**: Peer review and expert questioning serve essential roles in validating AI capability claims

## Conclusion

GPT-5 Pro's partial step-size improvement in convex optimization represents a meaningful milestone in AI-assisted mathematical reasoning, demonstrating sophisticated reproduction and application of advanced optimization techniques. However, the complete X post documentation reveals it falls dramatically short of the revolutionary breakthrough initially portrayed in promotional materials.

**The mathematical and documentary facts are clear**: GPT-5 Pro achieved a 1.5/L bound using techniques identical to those in Version 2, while human mathematicians had already proven the optimal 1.75/L bound months earlier. The progression from false "novelty" claims to forced admissions shows this represents impressive **mathematical reproduction** capabilities rather than **mathematical discovery**.

**The X post analysis exposes a systematic pattern**: strategic omission of superior existing results, misleading progress claims, and technical rationalizations that only emerged under community scrutiny. This case demonstrates both the genuine utility of AI mathematical assistance and the critical importance of transparent, honest assessment.

The definitive step-size convexity bound remains the work of human mathematicians, with AI providing powerful tools for mathematical assistance guided by human expertise rather than independent innovation. Responsible discourse about AI capabilities requires complete transparency, systematic evaluation, and clear distinction between technical reproduction and genuine research breakthroughs.

**In summary**: GPT-5 Pro demonstrated sophisticated mathematical assistance while achieving suboptimal results compared to published human work - a significant technical achievement that promotional claims unfortunately misrepresented as groundbreaking mathematical discovery. The complete documentary record serves as a crucial case study for evaluating future AI capability claims with appropriate rigor and transparency.