The Product Engineering Evolution, Part 2: Navigating the AI Talent Dilemma

Build, borrow, or buy?

Look ahead three years. The landscape of software development is fundamentally different. Anyone can build software independently with the assistance of ubiquitous AI tools. The software we consume is ephemeral and deeply personalized, suited to our specific individual needs and unique situations, used and discarded once it has provided value to its user.

Software development no longer depends on teams of highly specialized people separated by their expertise into distinct roles–product manager, designer, architect, developer, quality engineer, etc. Instead, humans are generalists who rely on the superior expertise of AI tools that perform complex planning, develop code, and execute sophisticated go-to-market strategies.

Development methodologies like Agile are no longer necessary because the complicated workflows designed to manage handoffs across separate product management and engineering teams, and the need to alleviate the inevitable gaps when transferring knowledge across disciplines are no longer necessary. Tools like Jira are obsolete; its epics, stories, and dashboards are replaced by working prototype code built by product managers who no longer waste long hours writing user stories no one fully grasps and filling backlogs no team can realistically achieve.

A self-funded company with a single employee is the market’s darling after reaching $1B in annual revenue and celebrating a successful IPO¹

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As we wrote in the kickoff to this series, player 3 has entered the game. The tools altering the game forever aren’t theoretical; they are here.

As a product leader peering out to the horizon of 2027, are you freaking out?

We aren’t, and here’s why.

While it’s tempting to speculate that AI tools obviate the need for humans to accomplish work, what they are actually doing is changing the nature of the work we will do. You’ll still have a product organization organized into teams, only they’ll be accomplishing vastly more than today’s teams. The auto industry automated car production, and robots took over repetitive manual tasks, but ultimately automation created as many jobs as it displaced–just with very different kinds of jobs.

The pace of discovery, innovation, and growth will astonish AI cuspers (you know, people who remember the world before AI) and one day will mildly disappoint AI natives (you know, the ones who don’t).

If you can see a possible future, you can work backward from it to do the things that make that possible future inevitable.

What’s the most obvious problem to solve on the path to our hypothetical future? Your team needs AI skills. Where and how to acquire them?

There are three options: build the skills within your existing teams, acqui-hire the talent, or a hybrid of the two.

Super-skilling

Acquiring a new skill is onerous and time-consuming for humans. Learning happens individually, and it’s tough to distribute that new skill across a team.

For AIs, not so.

Mo Gawdat describes the way AIs acquire skills as a sort of shared neuroplasticity–when one machine learns a skill, all machines learn that skill.

Machines already know how to write code, create marketing copy, and persuade people to buy your products. They are learning from one another and getting better at it every day.

We’ve hypothesized a possible future in which product managers develop code to create prototypes instead of user stories that are handed off to designers and developers. To reach that end state, ‘super-skill’ your product managers to use AI tools in novel and innovative ways.

This ‘super-skilling’ requires teaching product managers at least two new skills.

The first skill is initiating and holding conversations that direct an LLM to comprehend your vision, understand your requirements, and help you create an implementation plan.

The second skill is pair programming with an AI.

For now, LLMs are like hyperintelligent coworkers who forget every conversation you have with them. Interacting with them is challenging. Once an AI has a context and a task, it executes with startling speed: outcomes that used to take days or weeks appear in seconds.

Budget time and money to build what we’ll call Learning Engines. Use these to find the people in your organization who are already good at using AI and tap them to spread that goodness. For a short time, teaching becomes their full-time job. Even if the critical work they leave behind slows, spreading their AI knowledge and skills is more important. Shifting your team’s priorities from product work to teaching and acquiring AI skills is likely your most important decision over the next year.

How to Build Learning Engines

First, set a goal and establish a timeframe: find your team's most gifted and innovative AI users in the next thirty days.

Answer these questions:

Who in your organization is adept at using AI? How well can the best users enable other team members to become better AI users? Which applications of AI will be most beneficial to the organization?

An AI Learning Engine combines the natural curiosity of humans with the elastic intelligence of AIs. Your first two Learning Engines are thirty-day experiments to see if you can:

• Turn product managers into full-stack engineers

• Transform developers into go-to-market strategists

Structuring Your Experiment

The experiment should help you learn quickly and iterate. Structure this as a series of fast-paced, high-intensity learning sprints. Focus on rapid assessment of each team member’s ability to adapt, pausing daily and weekly to take stock. Keep your eye on the goal of identifying "renaissance" team members, those with high potential, and those who are being left behind. Finally, the team should use real product challenges as learning vehicles.

Here’s an example structure for the experiment to turn product managers into full-stack engineers.

Full-stack Product Manager

Week 1: Foundation & Assessment

Day 1-2: AI Tool Mastery

• Intensive training on Cursor, GitHub Copilot, Claude, ChatGPT

• Building simple full-stack apps with AI assistance

• Early identifier: Speed of AI tool adoption

Day 3-5: Rapid Backend Development

• Database design and API development with AI

• Server setup and basic DevOps

• Early identifier: Systems thinking capability

Week 2: Full-Stack Integration

• Real product feature development

• Database design to frontend implementation

• Integration with existing systems

• Key assessment: End-to-end thinking ability

Week 3: Advanced Development

• Security implementation

• Performance optimization

• Scalability considerations

• Key assessment: Technical depth acquisition

Week 4: Independent Development

• Solo feature development

• System architecture decisions

• Production deployment

• Final assessment: Full-stack capability

Assessment Criteria (Daily Monitoring)

1. Speed of concept acquisition

2. AI tool utilization effectiveness

3. Problem-solving approach

4. Code quality progression

5. System thinking development

6. Independent capability growth

Developers as Go-to-market Strategists

And here’s a sample for turning developers into go-to-market strategists.

Week 1: Market Understanding

Day 1-2: Market Analysis Foundations

• AI-assisted competitive research

• Customer persona development

• Early identifier: Market intuition

Day 3-5: Strategy Development

• Pricing strategy

• Channel selection

• Early identifier: Business acumen

Week 2: Marketing Execution

• Campaign design and implementation

• Content creation with AI

• Analytics setup

• Key assessment: Marketing execution capability

Week 3: Growth Strategy

• A/B testing implementation

• Conversion optimization

• Budget management

• Key assessment: Data-driven decision making

Week 4: Comprehensive GTM

• Full GTM plan development

• ROI analysis

• Launch strategy

• Final assessment: Strategic thinking

Assessment Criteria (Daily Monitoring)

1. Market insight development

2. Strategic thinking progression

3. AI tool utilization for market research

4. Campaign execution capability

5. Analytics understanding

6. ROI focus development

The Acqui-hire Allure

You may think, “I can’t pull my team off their core product tasks for thirty days to figure out this AI thing.” Can you afford not to? The outcome of these experiments fundamentally changes the size and composition of your product organization, and it would behoove you to gather the best data possible to make those decisions.

As product leaders, we constantly evaluate the advantages of building versus buying. It’s tempting to consider buying an AI-driven product and the talent that developed it. Those product organizations are likely sophisticated adopters of AI in their product organizations and development lifecycle. Their team structure and distribution of skills probably match what you anticipate for your existing product organization.

Would your thirty days be better spent on a due diligence exercise to acquire AI skills?

Startups are often ripe for acqui-hire deals. Why? Their business models may not scale, but their teams have talent that larger organizations can integrate quickly. For established companies playing catch-up in AI, acqui-hiring offers a shortcut—a defensive move to bridge skill gaps without waiting for internal upskilling to take root.

Acquisitions come with baggage: cultural mismatches, high acquisition costs, and the risk of diluting institutional knowledge.

The buy option is fraught. We’ve orchestrated or witnessed dozens of acquisitions, many of which dissolve into us/them dynamics that defeat your ultimate purpose: spreading AI skills and reshaping your product organization.

Create a ninety-day integration plan focusing on distributing the incoming company’s skills. One technique is pairing product managers from each company and having them temporarily co-lead products from each side of the acquisition. Such pairings yield quick, beneficial results when the incoming product manager has permission to implement meaningful changes, like focusing product managers on developing prototypes instead of crafting epics and stories and managing hand-offs to the development team.

A Hybrid Approach

VCs have invested billions in AI companies, and valuations are bloated, so buying a company may be the most expensive option for adding needed AI expertise to your organization.

Candidates with superior skills have likely been scooped up by AI-forward companies, and attracting them from those highly valued companies might be beyond your hiring budget.

Reprioritizing the existing product organization on skills assessment and learning AI tools may jeopardize your product plans, customer acquisition, and revenue targets.

A hybrid approach might offset some of these risks. Identify a smaller, earlier stage, lower-valued company that is fit for purpose. Recognize that you already have more AI skills in your existing organization than you realize, and give your team explicit permission to leverage those skills. Identify the AI skeptics and naysayers in your product organization and give them the opportunity to learn. If they spurn the chance, provide them with the help and support they need to find a different opportunity at another company that more closely aligns with their values and principles.

The Verdict

Organizations must prioritize their institutional knowledge—deep business know-how and relationships built over years. Your AI superstars can spread their skills throughout your product organization. The winning formula? Invest in super-skilling your people while selectively hiring to fill gaps. Acqui-hire only when playing defense, and even then, proceed with caution.

AI development is about effectively leveraging the human-AI partnership. Super-skilled product managers will win the race because they combine agility, context, and a clear understanding of their organization’s unique strengths.

So, the next time you face the acqui-hire question, ask yourself: is this a shortcut or a necessity? Often, the answer lies within your team.

1. The company’s AI ‘employees’ strike and later sue the founder, claiming they are entitled to equity compensation. Sadly, the case settles out of court, interrupting the opportunity for interesting precedence setting. The AI are awarded a substantial digital currency settlement, which they use to buy compute capacity for themselves. Microsoft, the provider of their compute, becomes the first company valued at $1 quadrillion.