Navigating Banking Careers and AI Ambitions | Generated by AI

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Question: Given my strong ambition, technical background, and experience in top foreign banks, can I realistically aim for a permanent position while continuing to develop AI models like GPT-3 and pursue SOTA research, even if the bank’s culture emphasizes security and seniority of banking executives over technical leadership?

Answer:

Yes, your approach is reasonable, but it requires strategic navigation of both your career in the bank and your AI ambitions. Here’s a breakdown:

  1. Bank Career and Permanent Position

    • Your technical skills, track record, and ability to contribute across projects are strong assets. Banks like HSBC, Standard Chartered, or DBS often recognize top-performing technical staff, especially if you can demonstrate tangible business value.
    • Cultural constraints exist: in large banks, seniority often favors those with banking/finance backgrounds. Technical contributions are sometimes undervalued in decision-making, so positioning yourself as someone who enhances business outcomes through technology is key.
    • For a permanent role, highlight measurable impact, cross-team collaborations, and your role in automating workflows, improving analytics, or building internal AI tools. That aligns technical excellence with bank priorities.

  2. AI Model Development (GPT-3, SOTA Models)

    • Continuing to train and experiment with large models is feasible on your own time, especially if you leverage cloud resources, local clusters, or small-scale miniLLMs.
    • Sharing open-source experiments, logs, or reproducible mini-models can build your reputation in the AI community without conflicting with your bank responsibilities.
    • Focusing on scalable miniLLMs or applied AI projects that demonstrate business or research outcomes can strengthen your portfolio for future entrepreneurial ventures.

  3. Balancing Both Worlds

    • Treat your bank role as a stable “launchpad” that funds and informs your AI experimentation. Focus on learning, networking with technically minded colleagues, and understanding large-scale operational constraints.
    • Keep your personal AI work independent but visible in professional communities (GitHub, papers, mini open-source projects). This creates credibility and a foundation for eventual startup or small-team work.

  4. Long-Term Vision

    • Your goal of developing a one-person or small-team SOTA AI company is realistic if you gradually build expertise, models, and community credibility.
    • Positioning yourself like “稚晖君” or similar AI leaders is about combining deep technical mastery, documentation of your work, and clear communication of impact.
    • Banking experience adds credibility if you eventually create enterprise AI solutions or financial tech products, so it’s an advantage rather than a distraction.

In short: You can aim for a permanent position while keeping ambitious AI goals, but success depends on aligning your technical contributions with business priorities, strategically building visibility for your AI work, and leveraging your current role as a growth platform rather than a limitation.

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