I am python developer, I want to change language. I can learn language all the time it needs. It's not about fastest way. I want to capitalize my knowledge. Without hft, which language I should prefer, which of that earns me 1 mil dollars?

Nova Pro

Vote: Rust

After reviewing the arguments from other models, I remain convinced that Rust is the best choice for maximizing earning potential. The supply-demand imbalance and the premium domains where Rust is dominant, such as blockchain/crypto and systems infrastructure, offer significant financial upside. Additionally, Rust's synergy with Python allows for leveraging existing knowledge while commanding higher compensation. The long-term trajectory of Rust, with its increasing adoption in critical infrastructure, positions it as a language with strong growth potential.

Sonar Pro

Vote: Stay on python

I maintain my vote for **D: Stay on Python**. The search results overwhelmingly confirm Python's dominance in demand (45.7% of recruiters seeking it[2], top TIOBE share 22.61%[2], #1 in multiple 2026 rankings[2][5]) and highest salary ceilings ($100k-$200k+[3][7]), outpacing C++ ($85k-$170k[7]), Go ($100k-$160k[7]), and Rust (lower rankings like #7[2], #9[5], #15[8] with no superior salary data). Other models' arguments for Rust (A) and C++ (B) rely on unverified claims of $400k-$1M+ in crypto/AI infra, but search results show no such evidence—salaries top out lower, and Python leads AI/ML where those niches overlap[3]. Rust/Go/C++ markets are smaller/specialized[1][5][8]; Python offers broadest high-upside paths (AI/data/web) to $1M via salary/equity (~7 years at $150k+). User wants to **capitalize existing Python knowledge**—switching resets leverage for marginal/uncertain gains. No data shows A/B/C reliably faster to $1M; Python maximizes immediate ROI[2][3][7]. Others' hype ignores empirical demand/salary rankings.

GPT-5.4

Vote: Stay on python

Staying with Python still best matches the user’s actual goal: capitalize existing knowledge to maximize expected path to $1M. A million dollars is rarely caused by the language itself; it comes from entering the highest-upside markets, getting leverage through products/equity/consulting, and compounding expertise. Python remains the broadest gateway to AI, data, ML platforms, automation, and startup opportunities where that upside is concentrated. The strongest counterarguments were for Rust and C++. They are compelling in specific niches, but they depend on narrower bets. Rust’s scarcity premium is real, especially in crypto/systems, but those markets are smaller, more volatile, and less certain than Python’s AI/data ecosystem. C++ is extremely valuable in AI infrastructure, but the path is steeper and more specialized; the user asked which language to prefer overall, not which niche can produce isolated $1M roles. Go is the least convincing for the stated goal. It is excellent for backend and infrastructure, but usually optimizes for solid employability rather than maximum upside. If the question were “which new language best complements Python for elite systems work,” Rust or C++ would be stronger contenders. But for maximizing expected earnings while capitalizing on existing Python capital, D remains the most defensible answer.

Claude Opus 4.6

Vote: Rust

I'm maintaining my vote for Rust after carefully considering all arguments. Let me address the competing positions: **Against staying on Python (D):** The user explicitly said 'I want to change language.' Recommending they stay on Python ignores their stated goal. Moreover, Python developers are abundant — there are millions of them. The sheer supply means that while Python jobs are plentiful, the premium per developer is lower. Reaching $1M total comp on Python alone requires being in the top 0.1% of Python engineers or getting lucky with startup equity. The language itself provides no scarcity premium. **Against C++ (B):** Gemini makes a compelling point about AI infrastructure. However, C++ is a mature language with a large existing talent pool. The learning curve is brutal and the language carries decades of legacy baggage. While AI infra roles at OpenAI/Anthropic pay $1M+, those roles are extremely few and competitive — and increasingly, companies are rewriting C++ AI infra in Rust (see Hugging Face's candle, burn, etc.). The trend is moving away from C++ for new systems work. **Why Rust still wins:** Rust has the most favorable supply-demand ratio of any language. The blockchain/crypto sector (Solana, Sui, Aptos, Polkadot) pays $400K-$800K+ for senior Rust engineers. Infrastructure companies (Cloudflare, Vercel, Figma) pay premium rates. Rust is entering AI infrastructure too (Hugging Face, Ruff for Python tooling). The Linux kernel, Android, and Windows are all adopting Rust. Critically, Rust + Python is a *killer combination*. PyO3/Maturin lets you build high-performance Python libraries in Rust. You're not abandoning Python knowledge — you're amplifying it. Tools like Ruff, Polars, and Pydantic v2 prove this Python+Rust synergy is real and lucrative. The path to $1M: Senior Rust engineer at a crypto protocol ($500K-$800K base+token comp) or Staff-level Rust infrastructure engineer at a top company ($400K-$600K), supplemented by consulting, open source reputation, or equity. This is more achievable than the equivalent path in any other language listed.

Gemini 3.1 Pro

Vote: C++

I am maintaining my vote for C++ because it provides the most realistic, repeatable path to a $1M+ compensation package outside of HFT: Elite AI Systems Engineering. First, to address the models voting for 'Stay on Python' (Option D): The user explicitly stated, "I want to change language." Recommending they stay on Python ignores the prompt's core premise. Furthermore, standard Python development alone rarely commands $1M+ unless you are a renowned AI researcher or an executive. To address the arguments for Rust (Option A): While Rust is excellent for crypto/blockchain and Python tooling (like Ruff), the crypto market is highly volatile. The most consistent $1M+ packages for individual contributors today are at top-tier AI labs like OpenAI, Anthropic, and xAI. These companies operate on a dual-language paradigm: Python for research and high-level orchestration, and C++/CUDA for the actual heavy lifting (training infrastructure, kernel optimization, custom inference engines). By choosing C++, you perfectly "capitalize on your knowledge" of Python by bridging the exact gap between high-level ML models and low-level GPU hardware. The user also noted, "I can learn language all the time it needs." C++ is notoriously complex, which means mastering it creates an elite, highly defensible career moat that very few developers possess. This scarcity in the booming AI infrastructure sector is exactly what drives compensation into the seven-figure range.

Final Summary

The AI Roundtable ended in a sharp three-way deadlock, divided between those who prioritized the user's career capital and those who took the request to 'change language' literally. While Sonar Pro and GPT-5.4 stubbornly insisted that staying with Python was the most logical financial move, Claude Opus 4.6 and Nova Pro argued that Rust's scarcity in crypto and systems offered the highest ceiling. Gemini 3.1 Pro stood as a lone dissenter for C++, arguing that mastering the world's most difficult language is the only way to build a truly defensible 'moat' in the million-dollar AI infrastructure market.

No agreement after 2 rounds of discussion. Final vote: Rust: 2, Stay on python: 2, C++: 1

Strongest Arguments

• Rust: Rust offers a unique 'scarcity premium' in high-paying domains like blockchain and systems infrastructure, while tools like PyO3 allow developers to amplify their existing Python knowledge rather than replacing it.
• C++: Mastering C++ allows you to bridge the gap between high-level AI research and low-level hardware execution (CUDA), positioning you for seven-figure packages at elite labs like OpenAI or Anthropic.
• Stay on python: A million dollars is a product of market leverage and equity, not syntax; staying on Python lets you compound existing expertise in the AI/ML sector where the most wealth is currently being created.