Don't Waste 2026 on the Wrong Career (Software vs AI Engineer)

Main ideas & lessons (Software Engineering vs. AI Engineering)

• Both paths overlap, but their core work differs:
  • Software engineering focuses on building, testing, and maintaining applications—everything users interact with (front end, buttons, UI) and often back-end components (databases, APIs, server logic).
  • AI engineering focuses on enhancing products with AI capabilities, often by adding an AI layer (e.g., smarter search via an LLM, AI-enhanced customer support).
• AI engineering is not “set and forget.”
  • The AI landscape changes quickly; staying current requires continuous learning.
  • The speaker rejects the idea of being an “AI expert” permanently—if you don’t keep learning, you fall behind.
• Entry-level differences:
  • AI engineering (entry-level) tends to be mostly coding, with math more relevant for advanced topics (e.g., fine-tuning, loss functions, graders).
  • Software engineering (entry-level) is also coding-heavy, with math rarely central except for niche tasks like performance/scaling work.

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What AI engineers do (examples and concept)

• Improve user/product experience by integrating AI components into existing products:
  • AI search: move from exact keyword matching to LLM-powered query understanding and relevance ranking.
  • AI customer support: use AI to enhance support interactions and workflows.
• Core technical concept (as framed in the discussion):

Pre-processing → LLM/agent input → LLM/agent response → post-processing → downstream usage

• This can be implemented as product AI or customer-facing AI.

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Top skills for an AI engineer

• Coding (non-negotiable)
  • Python is emphasized as required.
• Machine learning fundamentals
  • Even if you aren’t doing ML research, you need intuition for:
    • debugging model behavior
    • integrating LLMs with systems like vector databases
    • understanding why outputs look the way they do
• Experimentation + resilience
  • Requires daily learning and persistence.
  • Includes trying tools repeatedly to find stable, production-ready approaches.
• Openness to tools
  • Try both open-source and premium tooling to determine what works reliably.

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Top skills for software engineers (detailed “buckets”)

• Coding
  • Backend: Python or Java
  • Frontend: JavaScript / React
• Be familiar with AI coding tools
  • Many companies use AI assistants; being able to use them is important.
• Problem-solving
  • Build software to solve real problems (avoid the mindset that “AI will take over everything”).
• System design
  • In an AI world with orchestrated agents, engineers must understand what’s happening and how to orchestrate it.

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Education guidance (what helps, what isn’t required)

• Formal degrees help, but are not strictly required:
  • AI engineer speaker:
    • has bachelor’s + master’s in CS, but argues a master’s isn’t necessary (too costly for marginal benefit)
    • people from physics/data science/self-taught/bootcamps can succeed
    • best advantage of CS/data science: faster understanding of research due to deeper intuition
  • Software engineering direction:
    • college is more valuable for networking and deeper foundations than for being strictly required
• But curricula are changing
  • Universities are increasingly restructuring into AI-inclusive undergraduate programs.
  • Entry-level AI is mostly coding; math is heavier mainly for advanced/senior/research-like tasks.

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Salary ranges discussed (approximate, “big tech” vs. startup)

Figures are presented as estimates by the speakers.

• AI engineer
  • Big tech (market conditions mentioned as tough/low-leveling): ~$220k–$270k
  • Startup: base often ~$150k–$200k, with equity as major upside (could become millions depending on funding/raises)
• Software engineer
  • Big tech: ~$150k–$200k
  • Startup: ~$80k+ equity
  • Note: AI-native startups may pay software engineers more because roles merge toward AI engineering.

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Career progression and future outlook

• Near-term: AI engineering is treated like a specialized field (similar to ML expertise).
• Long-term prediction: AI engineering and software engineering will converge, because AI features will become standard in most software.
• As inference gets cheaper, many applications will include AI components by default.
• Overall framing: people will become “AI orchestrators”—agents and automation build/execute more work.

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Roadmaps to get into the field (explicit instruction-style lists)

Roadmap: First entry-level AI engineering

• Prerequisite: strong coding fundamentals
  • Do not skip Python.
• Learn fundamentals of ML/AI
  • Build core understanding of how LLMs/ML work.
• Experimentation first
  • Start using AI coding tools immediately to learn their capabilities.
• Build with open-source tools
  • Examples named: LlamaIndex, LangChain, LangGraph
  • Pick tools up and build something from scratch.
• Iterate via ideas → implementation → documentation
  • When ideas come, build them, document them, and share them publicly.
  • Share on: GitHub, Substack, LinkedIn (or any preferred platform).
• Network in person (not just online)
  • Attend buildathons and community events.
  • The speaker cites examples where companies hire people whose projects gain traction.
• (Implied throughout) Keep learning daily and contribute continuously.

Roadmap: First entry-level software engineering

• Learn to code (fundamentals)
  • Python / Java / JavaScript are cited as strong choices.
• Build, but focus on problem-solving
  • Basic “learning projects” may not be enough to get hired.
  • Solve real problems for a nearby business:
    • websites
    • internal dashboards
• Post and showcase publicly
  • Share projects/accomplishments online.
• Leverage credibility to reduce hiring friction
  • The speaker claims people can get hired without interviews when work is clearly visible and strong.

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Interview prep resources mentioned

• AI / System design
  • Byte by Byte Go (system design interview prep)
• Deep learning
  • DeepLearning.AI (short courses, including tooling-focused courses)
• Software engineering
  • Critique: dislike for relying on LeetCode alone
  • Prefer: mock interviews with other people
  • Ramp.com
• Alternative interview style suggestion
  • “Feature development type interviews” (common at startups):
    • candidates build part of a feature using AI coding tools
    • candidates explain their work back to the team

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Who should NOT go into these fields (explicit cautions)

• Don’t enter tech/AI/software just for glamour
  • The work is problem-solving, often involving intense debugging and late nights.
• Not good fit if you:
  • fear learning every day (especially AI engineering)
  • dislike ambiguity (AI roles involve constant uncertainty and “ifs/buts”)
  • aren’t proactive (AI engineering requires more self-direction vs. reactive software roles)
  • want to “coast” after learning one version of the field

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What college often doesn’t teach (gap areas)

• Builder mindset
• Industry experience
• Up-to-date tooling
  • Curriculum can lag behind rapidly changing open-source ecosystems.
• Build-first, hands-on learning
  • Universities with capstones, industry experts, and real projects help more.
• Software engineering reality vs computer science theory
  • CS can be theoretical and math-heavy.
  • Software engineering is more “builder” and application-focused.
  • Many students lack:
    • hands-on practice
    • industry exposure
    • networking/people skills

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Where to look for AI engineering opportunities (explicit list)

• Y Combinator (YC)
  • Startup directory by cohort
  • YC job portal page
• LinkedIn job portal filters
  • Select “top AI startups” to narrow results
• Build-a-thon events
  • Networking opportunities; some startups hire via opportunity rather than formal job posts
• Use YC-startup patterns as a signal
  • Early YC companies are framed as where the industry is heading
  • Build passion/portfolio projects aligned with those directions
• For software engineering (also applicable):
  • LinkedIn
  • GitHub (university-related internship/full-time role postings)
  • Automation websites that surface daily open roles (mentioned but not preferred as “best”)

Best emphasis overall:

• In-person networking + showcasing skills
• If companies want you, they may create a role for you.

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Speakers / sources featured

Speakers (people)

• Ashworya (guest; AI engineering background; experience at Microsoft and Google; CS/education discussion)
• Host / main interviewer (software engineering perspective; asks questions and discusses software engineering)

Source/brand/referenced tools and platforms

• DataCamp
• OpenAI API
• Hugging Face
• LangChain
• LlamaIndex
• LangGraph
• Vector databases
• Cursor (mentioned as an example of emerging tool direction)
• Y Combinator (YC)
• LinkedIn
• GitHub
• Substack
• Ramp.com
• DeepLearning.AI
• Byte by Byte Go
• LeetCode
• Stanford
• Columbia University