Applying Software Evolution Paradigms to Talent Acquisition

Why Andrej Karpathy's software evolution framework reveals a very important transformation for talent acquisition approaches.

Andrej Karpathy, former director of AI at Tesla, highlights that software is undergoing a fundamental transformation, arguably the most significant in 70 years, having changed rapidly twice in the last few years. 

This is what caught my attention: Karpathy's 3 paradigms of software evolution provide the perfect lens for understanding the shifts happening in talent acquisition right now.

His framework is a roadmap for how we instruct any system to carry out complex tasks.

Here are the parallels.

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Karpathy's Three Software Paradigms Applied to Talent Acquisition

TA 1.0: Hand-Coded Processes (Karpathy's Software 1.0 Parallel)

Karpathy describes Software 1.0 as traditional, human-written code; explicit instructions given to computers in languages like C++. Every function is detailed, manual, and precisely controlled by developers.

In talent acquisition, this translates to:

• Manually crafting every job description from scratch

• Sifting through CV stacks by hand

• Scheduling interviews one painful email at a time

• Following rigid, pre-defined workflows with zero flexibility

This is "hand-coded" talent acquisition; where every process required direct human intervention, with recruiters acting as the "programmers" writing and executing each step manually.

Sadly…still, a lot of TA teams are still operating in this paradigm, burning hours on repetitive tasks that could be automated.

TA 2.0: Data-Driven Optimisation (Karpathy's Software 2.0 Parallel)

Karpathy explains how Software 2.0 emerged with neural networks, where "code" isn't written directly but learned through data optimisation. These networks were initially fixed-function tools, like image recognisers, that literally "ate through" traditional hand-coded logic.

In talent acquisition, this translates to:

• Resume screening algorithms that learn from historical hiring data

• Predictive analytics identifying high-potential talent pools

• AI chatbots handling initial candidate qualification

• ATS systems with built-in matching capabilities

Instead of programming explicit rules ("look for candidates with 5+ years of Java experience"), these tools learn patterns from successful hires and optimise for desired outcomes at scale.

Like Karpathy's fixed-function networks, these tools excel at specific tasks but lack adaptability.

TA 3.0: Prompt-Powered Intelligence (Karpathy's Software 3.0 Revolution)

Here's where Karpathy's insights become truly transformative.

Software 3.0 represents Large Language Models becoming "programmable" through natural language prompts.

As he puts it: "Your prompts are the programs for the LLM, making English a very interesting new programming language."

In talent acquisition, this shift means:

• "Draft a compelling outreach message for senior Python developers in fintech"

• "Create interview questions for a Head of Product role at a scale-up"

• "Summarise this candidate's experience and highlight potential red flags"

Karpathy's insight: The prompt IS the code.

Anyone fluent in language becomes a "programmer" capable of instructing AI to perform complex, adaptive tasks.

This is a fundamentally new way of working with intelligent systems.

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Karpathy's LLM Insights: The New Operating System for Talent Acquisition

Karpathy offers several brilliant analogies for understanding LLMs, each providing crucial insights for how they'll reshape talent acquisition:

LLMs as Utilities and Fabs

NB: "fab" (short for fabrication plant or foundry) is a massive, expensive factory that manufactures computer chips and semiconductors.

Karpathy explains how LLM labs like OpenAI incur massive capital expenditure to train models, then offer access through APIs with operational expenditure based on metered usage - much like electricity providers.

The enormous upfront costs also give them properties of manufacturing plants (fabs), centralising R&D secrets.

Applied to talent acquisition: 

• The heavy lifting of training massive language models is done by tech giants. 

• TA teams access this "intelligence grid" via subscriptions and usage-based models, just as we access electricity without building power plants.

Strategic implication: access to superhuman recruiting capabilities without massive technical infrastructure investment, but with dependence on external providers for reliability and quality.

LLMs as Operating Systems

Perhaps most importantly, Karpathy positions LLMs as complex software ecosystems, operating systems that orchestrate memory and compute for problem-solving. 

He notes we're seeing both closed-source providers (like Windows/Mac OS) and open-source alternatives (like Linux/Llama ecosystem).

Applied to talent acquisition: 

• Think of advanced LLMs as the "central brain" or operating system for your entire TA stack.

• Instead of juggling separate systems for sourcing, outreach, screening, and assessment, an LLM-powered core orchestrates all functions with intelligent integration.

  
  

Will you build on proprietary (closed-source) AI platforms or invest in open-source LLM-based solutions? 

This decision will shape your competitive advantage and vendor independence.

Flipped Technology Diffusion

NB: Flipped Technology Diffusion means that instead of new technology starting in big companies and then filtering down to everyday consumers (the traditional way), AI tools like ChatGPT became popular with regular people first, and now companies are scrambling to catch up and figure out how to use them at work.

Karpathy highlights how LLMs have diffused from consumers to corporations, the reverse of typical enterprise technology adoption. 

Individuals use LLMs for everyday tasks while institutions lag behind.

Applied to talent acquisition: 

• Progressive recruiters are already using consumer AI tools for job descriptions, LinkedIn messaging, and candidate research, often ahead of formal corporate AI policies.

The opportunity here: early adopters mastering AI-assisted recruiting will have significant advantages over those waiting for corporate mandates.

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Understanding AI's "Psychology": Karpathy's "Fallible Spirits" Framework

One of Karpathy's most insightful observations is describing LLMs as "stochastic simulations of people" or "people spirits", trained on vast amounts of human text, resulting in an "emergent psychology that is humanlike."

They possess remarkable superpowers:

• Encyclopaedic knowledge far exceeding any human

• Perfect memory recall of training data

• Ability to process information at superhuman speed

But also critical cognitive deficits:

• Hallucinations, making up facts with confidence

• Jagged intelligence: superhuman in some areas, trivial mistakes in others

• Anterograde amnesia: like the film "Memento," they don't natively learn from ongoing interactions, each conversation effectively starts fresh

• Gullibility: susceptible to prompt injection and manipulation

Applied to Talent Acquisition:

Leverage their superpowers:

• Process thousands of CVs identifying patterns humans miss

• Generate personalised outreach at unprecedented scale

• Provide instant access to market intelligence and industry insights

Account for their deficits:

• Hallucinations: AI might fabricate candidate qualifications or company information

• Jagged intelligence: brilliant at keyword matching, poor at nuanced cultural fit assessment

• Memory limitations: each interaction starts fresh—requires careful context management

• Security vulnerabilities: could be manipulated to reveal sensitive data

We must design workflows that amplify AI's superpowers while human oversight mitigates its inherent fallibilities.

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"Partial Autonomy Apps": The Future of TA Tools

An opportunity Karpathy identifies is developing "partial autonomy applications", structured interfaces for human-AI collaboration rather than raw LLM interaction.

Karpathy's Key Features Applied to TA:

1. Context Management - the app handles underlying context required for the LLM, so recruiters don't need to re-explain company culture, role requirements, or candidate preferences each time.

2. Orchestration of Multiple LLM Calls. Behind the scenes, the app makes various calls to different models, embedding for candidate matching, chat for outreach generation, analysis models for CV screening, seamlessly integrated.

3. Application-Specific GUI. Karpathy emphasises visual interfaces leveraging our "computer vision GPU in our heads." Red/green diffs, candidate comparison charts, and pipeline visualisations are processed much faster than text.

4. The Autonomy Slider. Humans control AI autonomy levels, from simple suggestions to full task execution. This enables gradual adoption and risk management.

5. Keeping AI "On the Leash". Design generation-verification loops where AI creates, humans approve. Karpathy's goal: "Iron Man suit" augmentation, not autonomous robots.

Practical TA Applications:

• Sourcing Copilot: AI suggests candidates with visual profiles you can quickly audit and approve

• Outreach Assistant: Generates email variants for different segments, letting you select and refine the best options

• Interview Preparation: Creates role-specific questions with assessment criteria, requiring your review before implementation

The Autonomy Slider in Action:

• Low: AI suggests job description improvements

• Medium: AI drafts complete job specs requiring approval

• High: AI automatically posts roles and screens applications with final human audit

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"Vibe Coding" Revolution: Every Recruiter Becomes a Programmer

He also talks about the concept of "vibe coding", the natural language interface of LLMs means "everyone is a programmer."

Individuals can build custom, simple applications rapidly without deep coding knowledge, just by prompting.

Applied to talent acquisition:

Recruiters, HR business partners, and hiring managers can now "program" AI to perform bespoke tasks using plain English, without technical expertise.

Real examples:

• "Create a screening workflow for remote software developers that prioritises communication skills over technical certifications"

• "Build a personalised outreach sequence for passive candidates in competitive markets, emphasising career growth opportunities"

• "Generate competency-based interview guides for leadership roles, focusing on decision-making under pressure"

This dramatically lowers the barrier to customising and innovating within TA workflows. The democratisation of "programming" means every recruiter can create tailored solutions for their specific challenges.

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Building for the "New Digital Consumer": Karpathy's Agent-First Design

Applied to TA:

• LLM-Friendly Documentation: instead of human-centric "click here" instructions, create machine-readable formats like structured Markdown with actionable commands.

• AI-Native Content: transform human-oriented materials (like GitHub repos) into formats easily digestible by LLMs, concatenated files, structured data, tagged information.

Practical Example:

• Create "lm.txt" for Your Systems: design an AI-readable file that tells agents how to interact with your ATS, candidate databases, and hiring workflows.

• Structure Internal Knowledge: instead of writing HR policies just for human consumption, create versions tagged and formatted for AI processing.

• AI-Native Career Pages: design job postings and company information that both humans and AI agents can easily parse and act upon.

When AI agents become more autonomous, your systems will be ready for seamless integration.

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Building Iron Man Suits, Not Replacement Robots

This transformation won't happen overnight. AI amplifies our capabilities exponentially.

Your Action Plan:

1. Start Experimenting Now. Begin with low-risk applications: job description enhancement, candidate research, email personalisation

2. Focus on Augmentation, Not Replacement. Look for tools that make you a better recruiter, not tools that replace recruiting entirely

3. Master the Generation-Verification Loop. Develop workflows where AI generates options and you verify quality (this skill will be crucial)

4. Gradually Increase Autonomy. As you build trust and expertise, slowly move the autonomy slider toward more automated processes

5. Stay Human-Centric. Remember: the goal is better candidate experiences and more effective hiring decisions, not just efficiency gains

The talent acquisition professionals who master AI-assisted recruiting will have an enormous advantage over those who don't.

The window for early adoption advantage is open, but it won't stay open forever.

The future of our roles is really not about replacing recruiters with robots. But it is a lot about upskilling and transforming.

The question is: Will you be leading this transformation or scrambling to catch up?

Want to explore how AI can transform your talent acquisition strategy? Let's connect and discuss practical first steps for your team.

NB: The images are screenshots or created with Napkin.ai. The content of Karpathy's presentation has been extracted using Notebook LM from Google. GenAI tools have been further used to enhance the content of the blog post. The final content is designed and published by me.