From Bots to Blockchain: How Smart Solutions are Built

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@TechCrushHQOct 5, 2025

The Spaces brought together voices from AI and Web3 to share candid career journeys, practical entry paths, and grounded expectations. Hamid traced his route from mechanical engineering through robotics to a PhD focused on AI, highlighting motivation, hands-on practice, and the multidisciplinary nature of the field where domain experts (e.g., linguistics, education, fashion) are vital. He outlined day-to-day AI work—reading research, curating/labeling data, training models—and cautioned that large language models require informed prompting and verification. Kenneth described moving from physiotherapy into Web3 via technical writing and ambassador programs, then upskilling into Solidity and security, emphasizing Web3’s breadth beyond trading and meme coins, and the large opportunity in tokenizing real-world assets. Smart contract developer Ola Amit shared a transition from community management to building and hackathons, underscoring mentorship’s importance and the portability of skills (design, law, writing) into Web3 with proper blockchain fundamentals. Q&A focused on balancing studies, finding mentorship, gaining AI experience through bootcamps, portfolios, internships, and the recommended path into blockchain security: learn smart contracts first, then security. The session closed with reflections on purpose, independence, and actionable advice to set goals, build projects, publish work, and make the most of limited scholarship opportunities.

Tech Cross Webinar Recap: AI and Web3 Career Pathways, Skills, and Practical Advice

Overview

A Tech Cross webinar explored how newcomers can enter Artificial Intelligence (AI) and Web3, what day-to-day work looks like, the importance of multidisciplinary skills, and how to plan credible career paths without traditional backgrounds. The session featured:

  • Hamid (AI researcher; mechanical engineering background; MSc in Robotics; PhD in Robotics & AI; applied AI across web apps, robotics, agriculture/greenhouse optimization, and even fashion-related problems)
  • Kenneth (Web3 builder; physiotherapist by training; transitioned through technical writing/ambassadorships into Solidity/Web3 development and Web3 security)
  • Ola Amit (Smart contract developer; mechanical engineering background; community manager-turned-developer; hackathon participant and project cofounder)
  • Moderator (Tech Cross host)

The conversation emphasized motivation, structured learning, transferable skills, and realistic, hands-on pathways to build credibility and portfolios.

Can Anyone Enter AI Without a Traditional Background?

  • Hamid’s stance
    • Yes. AI is genuinely multidisciplinary. Domain experts (e.g., linguists, educators, fashion designers, agricultural experts) are critical for translating theoretical methods into real-world applications.
    • Two entry routes:
      • Algorithm and systems route (designing/implementing models, infrastructure)
      • Applied, domain-driven route (bringing subject-matter expertise into AI workflows)
    • Programming helps but is not mandatory. If you can program, you can extend and customize solutions, leverage open-source frameworks, and move faster. Without programming, you can still apply AI via low/no-code tools and APIs—though with limitations.
    • Practical constraint: computational resources (for training and experimentation) often matter more than paid software; many AI frameworks and research artifacts are open-source.

What Does an AI Practitioner Do Day-to-Day?

  • Hamid outlined a research-informed, experimental workflow:
    • Continuous literature tracking: review new research outcomes to stay current in a fast-moving, research-heavy field.
    • Data work: collection, annotation/labeling, cleaning, curation; better data generally yields better models.
    • Training and evaluation: “training” an AI is akin to a structured curriculum—models learn via exposure to labeled or well-structured data.
    • Use of LLMs (e.g., ChatGPT, Claude, Gemini) as assistants, with caution:
      • Always know what you expect from an LLM. Models will produce confident outputs—even when off-topic.
      • Example: a fashion-related query returned chemistry-focused “dye” information; only domain awareness enabled quick course-correction. Guardrails and specificity in prompts are key.

AI’s Near-Term Outlook and Career Implications

  • Still early: Despite impressive LLMs, Hamid views AI as “in its infancy.” Recruiters should not (and generally do not) expect 10–15 years of AI-specific experience.
  • Research-driven: Many practical solutions trace to cutting-edge papers. Expect to read, replicate, and adapt research.
  • Rapid evolution:
    • 2013–2018: milestones in game-playing AI
    • 2023–2024: widespread general-purpose LLMs and generative systems
    • Trendline: performance and capability expanding quickly; integrate LLMs into workflows now.
  • How beginners can prepare:
    • Build fundamentals (data, basic ML concepts, evaluation)
    • Get hands-on with projects using open-source repos and datasets
    • Document work in a portfolio (GitHub, write-ups, demos)

Web3: A Realistic Route Without a CS Background

  • Kenneth’s journey and perspective
    • Non-technical origin: From physiotherapy to blockchain via curiosity, independent study, and community programs.
    • Early steps: Read whitepapers, wrote technical content, and joined an ambassador program. First payouts validated the path.
    • Pivot during the bear market: Used the downturn to skill up—learned JavaScript and Solidity; then moved into Web3 security.
    • Core message: Web3 is not just crypto trading or memes. It’s about decentralization, ownership, and new market structures (e.g., tokenization of real-world assets). He cited an article projecting real-world assets in blockchain could grow from roughly $200B to $10T by 2030—illustrating the scale of opportunity.
    • Practical warning: The space is vast; without guidance, you can get lost. Don’t try to learn everything at once. Seek structure and mentorship.

Transferring Skills Into Web3

  • Moderator’s question: Can non-devs (designers, writers, lawyers, PMs) thrive in Web3?
  • Ola Amit’s answer
    • Yes. Most Web2 skills transfer to Web3 (design, content, legal, community). But you must understand blockchain concepts to contextualize your work (e.g., how NFTs are created and traded, what “ownership” and on-chain provenance mean).
    • Advice for creatives: Study current NFT aesthetics, collection structures, and utility patterns before translating your art/design practice to Web3.

Web3 Developer Journey and Mentorship Lessons

  • Ola Amit’s path to smart contracts
    • From mechanical engineering to programming curiosity (early tools), then to community roles in crypto.
    • Chose smart contracts as a focus due to interest and differentiation.
    • Hackathons and cofounding a project (“Bubblefile”) accelerated learning and credibility.
  • Mentorship matters
    • Biggest early obstacle: lack of guidance. Example misstep—studying C++ purely because Bitcoin Core uses it. Proper mentorship would have shortened the path.
    • Today’s advantage: community presence, curated learning paths, and AI-assisted tooling reduce time-to-competence.

Q&A Highlights

  • How to combine university study with Web3 and find mentors? (Web3 Wanderer)

    • Ola’s guidance:
      • While studying, take on non-technical roles (e.g., community moderation/management) that build context without heavy time/skill demands.
      • When you’re ready for development, reserve focused blocks (he did this during a full-time service period) to go deep.
      • Define a niche (e.g., smart contracts, community, design). Then use YouTube, GitHub, Medium, TikTok, and hackathons to learn and network. Bootcamps help provide structure and feedback.

  • How to get “starting” experience in AI and access resources? (Omega)

    • Hamid’s advice:
      • Bootcamps (e.g., Tech Cross) are a strong entry point: you’ll build projects for a portfolio repository.
      • Pursue internships to apply skills in context (he interned in Japan applying AI to agricultural data). Many labs/companies offer such opportunities.
      • Recruiters won’t expect long AI tenure—demonstrable projects matter more.
      • Resources: YouTube (cost-effective), formal courses, open-source repos. Programming skills unlock deeper hands-on learning.
      • Expect “productive struggle”: tackling real problems builds confidence and practical intuition.

  • Path into blockchain security vs. traditional cybersecurity

    • Kenneth’s guidance:
      • Traditional cybersecurity is different from smart contract security.
      • Start by learning smart contract development (Solidity, EVM tooling), then specialize in security (threat models, common vulnerabilities, auditing approaches, CTFs, formal verification basics).
      • Security research is fundamentally about breaking and proving weaknesses—understanding how to build is prerequisite to finding flaws.

  • Clarifying Web3/blockchain’s societal value (Onyeka’s confusion)

    • From earlier answers and examples:
      • Web3 re-architects ownership and control of data/assets (decentralization, trust minimization).
      • Use cases include NFTs for creative economies, decentralized finance (DeFi), tokenization of real-world assets (RWA), and transparent, programmable markets.
      • For society: it can broaden access, reduce gatekeeping, and enable new forms of collaboration and verification. The learning curve is real, but value grows with understanding and responsible application.

Practical Career Building: Portfolios and Visibility

  • The moderator emphasized:
    • Goal-setting: Don’t switch paths impulsively. Align choices with your interests and capacity.
    • Portfolio-first: In Tech Cross programs, students implement capstone projects after roughly two months—this becomes the core of a public portfolio.
    • Visibility: Publish your work (demos, write-ups). Private excellence is invisible to the market.
    • Scholarships are limited: If you get one, maximize it. If not, progress with publicly available resources and community support.

Key Takeaways

  • Motivation and clarity of purpose drive progress; mentorship prevents wasted cycles.
  • AI is multidisciplinary and still early; build domain-context plus technical literacy. Portfolios trump years of “experience.”
  • Programming accelerates AI progress but is not strictly mandatory; many open tools exist. Compute and data quality are key constraints.
  • Web3 is far more than trading: it’s a shift toward decentralized ownership and programmable markets, with expanding opportunities (e.g., RWAs).
  • Transferrable skills are welcome in Web3—learn the underlying concepts and context to apply your craft effectively.
  • For security roles (Web3): learn to build before you learn to break; specialize in smart contract vulnerabilities and auditing.

Suggested Next Steps for Attendees

  • Choose a path and niche:
    • AI: pick an applied domain (e.g., agriculture, fashion, education) or a systems track (ML engineering, MLOps, data engineering).
    • Web3: pick a role (smart contracts, front-end dApps, security, protocol research, design, community, legal/policy).
  • Build a public portfolio:
    • 2–3 scoped projects with documentation, code, and short demo videos.
    • Write postmortems: what worked, what didn’t, what you’d improve.
  • Learn with structure:
    • Bootcamps, curated course tracks, and reading groups reduce noise.
    • Follow 3–5 reputable sources (labs, maintainers, auditors, protocol teams) rather than consuming everything.
  • Network with intent:
    • Join focused communities, attend spaces with strong signal, and ask specific questions.
    • Seek micro-mentorship: short, targeted feedback beats unbounded advice.
  • Apply early and iterate:
    • Internships, hackathons, bounties, and open-source contributions offer real experience and references.

Closing Notes from Speakers

  • Ola Amit: Web3 provided direction and motivation—“a higher purpose.”
  • Hamid: AI became both a career and a network-builder across borders and disciplines.
  • Kenneth: Beyond financial independence, blockchain offered a front-row seat to how markets and computing paradigms evolve—being part of a transformative shift is rewarding.