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The Three Key Patterns For Hiring AI Talent in Defense.
"Exploring the frontiers of science, technology and startup scaling"
Steven Bourne, Founder - New Intelligence
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Introduction: Fusion Temperature
The battle for AI talent in defense tech has reached fusion temperatures. Thousands of inmails sent (now sent by AI Agents) and fancy job descriptions (crafted with AI ...and applications also crafted using AI), hiring the top 5% of AI talent still requires a nuanced understanding of how talent flows through ecosystems, where it develops, and when it’s poised for its next leap.
Enter the Talent Topology Map: a strategic tool for visualising and predicting talent movement. Like Professor Lambeau spotting Will Hunting's mathematical genius through patterns left on hallway chalkboards, this framework helps you identify patterns in their career trajectories.
Decoding the Talent Topology Map
The Talent Topology Map serves as your strategic command center for talent intelligence, built on three core layers:
Primary Companies: Your direct competitive ecosystem, from legacy giants like Lockheed Martin to disruptors like Anduril and Palantir.
Development Grounds: The talent incubators—Stanford AI Lab, MIT CSAIL, and increasingly, open-source AI communities.
Transformation Points: The career crucibles where talent evolves, often hiding in plain sight at the intersection of different sectors.
The Three Critical Patterns
Pattern One: The Research Innovation Pipeline
Pattern One: Academic Virtuoso to Industry Pioneer
From theoretical breakthroughs to market disruption
Academia → Big Tech → Start up
This pattern represents the trajectory of talent from academia, through industry research, and then landing in the start up world (often fast-tracked to leadership or jumping straight in as Founders). Early career researchers often develop foundational expertise at institutions like Stanford or MIT. After a few years, many transition to applied research roles in Big Tech such as Google DeepMind or Microsoft Research, where they gain practical experience in scaling their innovations. Ultimately, the earlier you pay attention, track and nurture talent while in academia the less inmails you (or your agent) will need to send later.
Typical Progression Timeline:
Years 0-3: Academic research (Stanford, MIT).
Years 3-5: Industry research (Google DeepMind, Microsoft Research).
Years 5+: Startup > leadership.
Pattern One Example:
Dileep George
Stanford PhD → Google DeepMind → Vicarious AI
Dileep's career is the perfect paragon case showcasing the research to Innovation pattern. From Stanford to Google Deepmind and then Founding a start up which ended in blockbuster exit.
Stanford PhD: Developed hierarchical temporal memory theories under Jeff Hawkins
Research Focus: Neocortical algorithms, biological learning systems
Google DeepMind: Led research teams in applied AI, bridging theoretical models with practical applications
Vicarious AI: Co-founded and served as CTO, developing AGI systems for robotics
Exit Success: Acquired by Intrinsic (Alphabet)
Pattern Significance: Demonstrates the successful transition from theoretical research to commercial innovation
Key Pattern Indicators:
Publication impact in both academic and industry venues
Bridge-building between theoretical frameworks and practical applications
Early industry collaborations while in academia
Pattern Two: The Defense Tech Evolution
Pattern Two: Defense Evolution Navigator
Mastering the legacy-to-innovation transition
Traditional Defense → Modern Defense → New Defense (Disruptor)
This pattern follows talent as it moves from traditional defense contractors and/or government, through modern defense firms, and eventually landing in new defense disruptors. Talent in this path begins by developing deep expertise at legacy contractors like Boeing, Lockheed Martin or BAE Systems. They then transition to faster-paced environments at modern companies like SpaceX or Palantir, where they are exposed to agile development and the latest technology. Finally, this expertise converges in defense startups like Anduril or Helsing, where the combination of strong legacy knowledge and modern innovation commands a premium.
Pattern Two Example:
Trae Stephens - The Defense Tech Evolutionist
US Gov → Palantir → Anduril
Another Paragon case, Trae Stevens, Co-founder of Anduril:
Early Career: U.S. Intelligence Community, specialising in counterterrorism analysis
Palantir Technologies: Early employee, helped scale defense sector operations
Founders Fund: Partner focusing on defense and security investments
Key investments: Anduril (co-led $1.4B Series F round) , Epirus, Shield AI
Pattern Significance: Shows how deep defense knowledge combined with commercial tech scaling creates uniquely valuable leaders
Key Pattern Indicators:
Experience spanning classified and commercial environments
Track record of modernizing legacy systems
Understanding of both defense requirements and startup velocity
Pattern Three: Fullstack Transformation
Pattern Three: Technical Specialist to Defense Tech Visionary
Engineering excellence meets strategic leadership
Pattern Three Example:
Devaki Raj - The Full-Stack Transformer
Specialised Position (Big Tech) → Transformation → New Defense Disruptor
Google: Data Scientist and Product Manager
Led machine learning projects for Maps and Earth
Developed computer vision algorithms for satellite imagery
CrowdAI: Co-founder and CEO
Built AI platform for critical infrastructure analysis
Secured major defense contracts
Led successful exit to Saab
Saab: Chief Digital and Artificial Intelligence Officer
Drives AI integration across defense platforms
Bridges commercial AI capabilities with defense requirements
Pattern Significance: Exemplifies the evolution from technical specialist to strategic defense tech leader
Key Pattern Indicators:
Progressive expansion of scope and responsibility
Success in both technical and business roles
Ability to translate commercial tech advantages to defense applications
The Good Will Hunting Principle in Talent Spotting
Like Professor Lambeau recognising Will's genius through solved theorems left anonymously, exceptional talent often leaves traces before becoming obvious to the market. Your task is to spot these signatures:
Open source contributions that solve complex problems elegantly
Novel approaches to technical challenges in unexpected places
Cross-disciplinary insights that bridge traditional domains
Building Your Talent Command Center
A Talent Command Center is your strategic hub for managing, optimising, and controlling talent flows within your ecosystem. By centralising insights, you can turn scattered data into actionable talent strategies, increasing efficiency and building greater talent density.
Map → Track → Validate
Use the Map → Track → Validate framework to construct and refine your Talent Command Center:
Map:
Start with one layer of your ecosystem, such as feeder companies or universities. Use tools like Miro to visualize relationships and talent flows, identifying where key talent originates and how it moves.
Track:
Monitor hiring patterns, team compositions, and transitions using Google Sheets. Track ~500 profiles to uncover trends such as:
Average tenure at target companies.
Shared educational or professional backgrounds.
Role transitions within organisations.
Validate:
Compare identified patterns against actual outcomes to refine your Ideal Talent Profile (ITP). Analyse movement triggers, timing, and performance metrics to improve accuracy and efficiency in talent acquisition.
Example Insight: 15 employees moved from Company A to Company B in the last year, with a mean tenure of 2.3 years and 80% having studied at XYZ University. This data suggests targeting Company A employees approaching the 2-year mark for proactive outreach.
Beyond the Basics Moving beyond the basics, you can integrate prompting, custom Python scripts, and AI agents to accelerate, automate, and scale your Talent Command Center for long-range, strategic planning.
Schedule a discovery call if you are interested in learning more
Conclusion
The defense tech landscape rewards those who can spot and nurture transformative talent before it becomes obvious. The Talent Topology Map provides the framework to identify these hidden gems, much like finding the next Will Hunting solving complex problems on hallway chalkboards. Success lies not in competing for known talent, but in discovering and developing the next generation of defense tech leaders.
About Me
I'm Steve, and I've spent over half my life as a headhunter (top 1% - context.. and humble-brag). My career has been forged in the fires of both spectacular successes and instructive failures—each adding to a deeper understanding of how to build start ups. .
From seed-stage Deeptech ideas to hyper-growth SaaS, I've lead full-scale team transformations and closed executive placements that have reshaped industries. What I've discovered, often by swimming against the current of conventional wisdom, is that across all sectors, three fundamental truths emerge:
Exceptional people build extraordinary companies,
and extraordinary companies change the world.
(the secret to changing the world? Finding exceptional people).
Ready to Scale?
If these insights resonate and you're navigating the challenges of scaling (whether as a founder or VC with portfolio companies), reach out at steven@newintelligence.io to discuss how we can build something remarkable together.
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