(Photo: from left to right: Lewis Levin, Kartik Raghavan, Jeff Raikes, Luis Salazar)
Last week, I enjoyed joining my friend and AI4SP Advisory Board Chair, Jeff Raikes, alongside Lewis Levin (former Corporate VP at Microsoft) and Kartik Raghavan (former Microsoft executive and innovator in financial markets/biotech), at the University of Nebraska-Lincoln’s Raikes School of Computer Science and Management. We spoke to students, faculty, and local business leaders about “Leading Machines” and the global business transformation unfolding in 2025.
The students were excited about AI, but also grappling with uncertainty about their future in the labor market. Our discussions kept circling back to two critical calls to action for them:
- Start building your personal AI agents now – aim to have a team of 10 agents working with you by the time you graduate.
- As entrepreneurs, explore how AI agents can disrupt the last 50 years of software and the last 30 years of the internet in areas you’re passionate about.
These students’ concerns about their future job prospects reflect a global fundamental shift: organizations aren’t evaluating individual skill sets; they’re starting to assess complete human-AI production units.
We expect this to fundamentally change how organizations acquire talent, a shift beyond “traditional hiring.”
The future of work isn’t just about *using* AI, it’s about *integrating* complex human-AI ecosystems. For organizations to succeed, they must add AI tools and build or acquire high-functioning micro-business units of humans + AI.
Leading organizations, which are few at this point, are starting to think of human-AI production units, which look much like mini-mergers and acquisitions (M&A).
Explore more this week’s podcast, 🎙️
📊 The New Talent Landscape: Individuals vs. Production Units
The traditional model of hiring an individual based on their resume and experience is quickly becoming outdated. Today, the most valuable talent isn’t just a person; it’s a person coupled with the sophisticated AI tools and agents they’ve built, trained, and mastered. This is the rise of the human-AI production unit.
Organizations are increasingly evaluating candidates not just on their individual skills, but on the ‘portfolio’ of AI capabilities they bring with them. Our global tracker shows that over 33% of new job openings list AI-related requirements.
This is happening in a job market already seeing significant shifts, with AI roles growing by 70%. In comparison, overall tech job postings fell by 20%, and in the tech sector, organizations report 50,000 layoffs in the US over the past 12 months.
This paradigm shift fundamentally changes how top candidates are assessed, asking: Are we hiring an individual or a mini team? Organizations increasingly acquire a package of capabilities and potential IP, not just an individual’s time.
Consider this insight:
“We recently hired a financial analyst who had developed a suite of AI agents for market trends. We structured the compensation package to acquire her expertise and her AI portfolio specifically – it accelerated our capabilities by approximately 9 months compared to building those tools internally.” – Chief Investment Officer, Asset Management Firm.
Insights from super users also highlight this shift:
Conversations with 80 super users in professional services, consulting, marketing, legal, and finance highlight a brewing tension:
- 70% disagree that the IP of the agents they created (sometimes using their own paid tools like ChatGPT) should automatically stay with the employer if they change jobs.
- 80% are hesitant to share their agents broadly within a new company after being hired, fearing their IP could be absorbed and they could become redundant.
- 90% are open to selling the IP as part of a hiring bonus or even independently, highlighting a move towards transactional IP acquisition alongside talent.
📲 What can we learn from the Software Industry during this transition?
This transition borrows heavily from the software industry’s long-standing practice of acquiring intellectual property through strategic hires.
Frameworks for IP acquisition terms, compensation structures reflecting both human expertise and accompanying IP, and clear delineation of ownership are also becoming essential in non-tech sectors.
30% of leaders across multiple industries report directing their HR leaders and outsourced recruiting firms to seek talent that has experience creating and using AI Agents to perform their daily tasks, treating certain hires as strategic “mini-M&A” transactions.
🤔 The Uncharted Territory: IP, Learning, and Ownership
While the mini-M&A analogy provides a starting point, AI agents’ continuous learning nature introduces complex new scenarios.
What happens when an employee’s personal AI agent continues to learn and evolve using company data after they are hired?
“If an employee creates an AI assistant on their own time, brings it to our company, and it continues learning while working on our projects – who owns the resulting capabilities? The employee who created the initial system? Our company that provided the learning environment? Some hybrid ownership model? We’re writing policies for scenarios that have no clear precedent.”
- Over 90% of organizations (outside the technology sector and some in academia) lack clear policies regarding employee-developed AI assets.
- Almost all have not addressed the “continuous learning” aspect of AI systems in employment agreements.
- Legal departments acknowledge insufficient guidance in existing case law.
This creates a need for new legal and operational frameworks, such as knowledge domain agreements and learning partition systems.
🚀 Implications for Talent Strategy
This shift has profound implications for how organizations attract, evaluate, and retain talent:
- New Evaluation Metrics: Assessing a candidate’s “AI portfolio” becomes as crucial as evaluating their experience and credentials.
- Compensation Structures: Packages must account for the value of the AI assets brought into the organization.
Our Leading Machines Research Paper shows that professionals actively developing production unit capabilities command 30-45% higher compensation.
- Onboarding Processes: Integrating AI agents and ensuring knowledge transfer is as critical as onboarding the human employee.
At AI4SP, we’ve designed a specific onboarding process for AI agents accompanying new hires. When we bring a new agent, our onboarding includes three phases: knowledge transfer (sharing our proprietary datasets), capability testing (evaluating accuracy), and integration (establishing workflow handoffs with human researchers).
- Talent Development: Fostering the ability to build, manage, and orchestrate AI teams becomes a key competency.
- Workforce Planning: Understanding that the workforce evolves from individuals to hybrid production units changes how roles and teams are structured.
- Addressing the Skills Gap: With 90% of workers lacking essential AI proficiency, and 58% of companies (as per AI4SP global tracker) actively seeking new hires with these skills, bringing a ready-made AI production unit is a significant advantage.
- Navigating Workforce Reduction: As per the World Economic Forum’s Future of Jobs Report 2025, 40% of employers expect to reduce their workforce where AI can automate tasks. Acquiring human-AI units with proven efficiency becomes a strategy for targeted capability enhancement rather than broad-based reduction.
Beyond Automation: Augmentation, Not Replacement
There’s a prevalent narrative that AI automates tasks, potentially replacing human workers. While AI can automate, its most transformative power lies in augmentation. It equips individuals with capabilities previously requiring years of specialized training.
Consider the excitement around AI-assisted coding tools. These tools allow individuals with limited or no programming background to generate functional code, a trend sometimes referred to as “VIBE coding” (Visually Inspired By Example).
However, this doesn’t mean the expert developer is obsolete.
An expert software developer has the critical background, deep understanding of architecture, debugging skills, and nuanced problem-solving ability to effectively lead and manage an AI agent.
They can provide the precise context, evaluate the generated code for quality, security, and efficiency, and orchestrate the AI’s output within complex projects in a way a novice cannot.
The result is a dramatically more productive and capable human-AI production unit.
This principle extends across all domains: AI democratizes access to creation, but expertise in guiding and evaluating AI drives superior outcomes.
📉 The Critical AI Skills Gap: What Your New Hires Need
The ability to effectively work with and lead AI agents is no longer a niche skill; it’s becoming foundational. Our research highlights critical gaps in the workforce:
| Skill Area | Average Score (AI4SP Research) |
Why It Matters in Hiring Production Units |
|---|---|---|
| Digital Skills | 42/100 | Foundational to interacting with and managing diverse AI tools and platforms. |
| Critical Thinking | 38/100 | Essential for evaluating AI outputs, detecting errors (Less than 20% of users can effectively detect AI errors), and making informed decisions. |
| Data Literacy | 42/100 | Necessary for understanding the data AI is trained on and managing knowledge for AI agents. |
| Data Security & Handling | 38/100 | Crucial for ensuring secure information handling when integrating personal AI agents into an organization. |
| Conversational AI Literacy | Only 10% of the population is proficient | Communicating effectively with AI is key to directing AI teams. |
| AI Orchestration & Management | Emerging | The core skill for leading a human-AI production unit (setting objectives, evaluating performance, resource allocation). |
| Production Unit Development | Emerging | Building and curating a valuable portfolio of AI capabilities. |
Hiring managers are increasingly looking for candidates who demonstrate these skills, indicating they can arrive not just as an individual contributor but as a ready-to-deploy, efficient production unit.
🔮 One More Thing…
Are you still thinking about hiring as filling a headcount? The organizations winning in the AI era are already thinking like investors, evaluating the ROI of acquiring sophisticated human-AI capabilities.
The question isn’t just who you hire but what production power you bring into your organization. Are you ready for the mini-M&A revolution in talent acquisition?
✅ Ready to transition from a traditional organization to an AI-powered one?
We advise forward-thinking organizations to develop strategic frameworks for evaluating, integrating, and optimizing human-AI production units. Contact us to explore how we can support your organization’s evolution in this new talent landscape.
📚 Resources
- Digital Skills Compass: Free assessment in 7 languages at skills.ai4sp.org
- Workshops & Training: Book sessions for your team
- Complete Research: Request our detailed findings
Luis J. Salazar
Founder | AI4SP
Sources:
Our insights are based on +250 million data points from individuals and organizations who used our AI-powered tools, participated in our panels and research sessions, or attended our workshops and keynotes.
