Enrich brought senior product and technology leaders together in NYC with Zakir Tyebjee and Wes Repass, co-founders of Stride, for a discussion on how the bar for product talent is evolving.
The conversation explored everything from AI's impact on product careers to the signals that matter in hiring, the future of interviews, and how experienced leaders can position themselves for what's next.
Here are the biggest lessons and practical takeaways from the discussion.
1. When building gets easier, business outcomes matter more.
AI is making it faster and cheaper to build software. But being able to ship more features doesn't automatically mean you're creating more value.
As execution becomes more accessible, the differentiator shifts toward knowing what to build, why it matters, and whether it delivers meaningful results.
That distinction is increasingly important in hiring.
Candidates who focus exclusively on input metrics, such as feature launches or activation rates, may struggle to stand out against those who can demonstrate measurable business impact.
For example, there's a meaningful difference between saying:
"I improved user activation by 20%."
"I improved user activation by 20%, contributing to higher conversion and measurable revenue growth."
The second connects the work to an outcome the business cares about.
The same principle applies to AI-generated work. When anyone can produce a polished presentation in minutes, quality becomes less about how quickly someone created it and more about whether they understood the audience, exercised judgment, and communicated something valuable.
The lesson: Don't just showcase what you've built. Demonstrate the impact of your decisions.
For product leaders, that means getting comfortable connecting your work to revenue, customer value, retention, efficiency, and other meaningful business outcomes.
2. Product judgment becomes more valuable as technical execution becomes cheaper.
One of the more provocative discussions centered on how AI could reshape competition.
As companies gain access to increasingly similar AI capabilities, the advantage may shift toward those that already own customer relationships and distribution.
An established company with a large customer base may not need the most sophisticated AI product to win. It may simply need a solution that's good enough, delivered through channels customers already trust.
The group also explored how AI could enable smaller businesses to build custom software rather than purchase traditional off-the-shelf products.
If these shifts continue, product leaders will need to think beyond feature differentiation.
The important questions become:
What customer problem is worth solving?
Where do we have a meaningful distribution advantage?
What should we build versus buy?
How do we create value that competitors can't easily replicate?
The lesson: Technical execution alone isn't a durable competitive advantage.
Develop strong customer understanding, commercial judgment, and the ability to identify opportunities where your organization can win.
3. Your LinkedIn profile is becoming your professional storefront.
One of the most immediately actionable discussions was about how recruiters discover and evaluate candidates.
As recruiting tools increasingly use AI to search and surface potential hires, your online presence plays a larger role in determining whether you're discovered in the first place.
The group emphasized that LinkedIn should be treated as personal SEO, not simply an online copy of your résumé.
But that doesn't mean stuffing your profile with keywords.
A clear, credible profile that communicates your experience and accomplishments is more valuable than one overloaded with buzzwords or generic AI-generated content.
Public professional profiles also create a form of social proof. Recruiters can compare your stated experience with your visible career history, connections, and accomplishments.
The lesson: Make it easy for recruiters to understand who you are, what you're great at, and what you've accomplished.
A practical exercise:
Write down your biggest accomplishments from the past five to seven years.
Include the business outcomes, teams, and initiatives you've influenced.
Use ChatGPT or Claude to organize those notes into a concise professional summary.
Edit the result so it sounds like you, highlights your strengths, and accurately reflects your experience.
Your LinkedIn profile should tell a clear story about the value you bring—not just list the jobs you've held.
4. Recognizable company names can open doors. They don't win the offer.
With more applicants and more AI-generated applications, hiring teams are struggling to separate meaningful signals from noise.
One response has been to lean more heavily on familiar credentials: recognizable universities, well-known technology companies, and respected startup backgrounds.
Those signals can help a candidate get noticed, but they don't necessarily demonstrate that someone will succeed in a particular role.
The discussion highlighted an important distinction: the experience that makes someone successful at a large technology company isn't always the same experience required at an early-stage startup.
A seed-stage company may need someone comfortable operating with ambiguity, building from scratch, and working without extensive resources.
A later-stage organization may value experience navigating complexity, managing risk, and scaling established systems.
Neither background is inherently better. What matters is the match.
The lesson for hiring managers: Evaluate candidates against the company's actual stage, challenges, and needs—not just the prestige of their previous employers.
The lesson for candidates: Show how your experience translates to the environment you're entering. Don't assume your résumé will make that connection for you.
5. AI is forcing companies to rethink how they interview.
Candidates can now use AI to write applications, complete assignments, prepare presentations, and generate polished responses.
But many hiring processes haven't evolved much beyond allowing candidates to use AI.
That creates a new challenge: How do you evaluate someone's thinking when AI can produce much of the output?
One approach discussed at the dinner was using real company problems in interviews rather than hypothetical case studies.
Instead of asking candidates to redesign a familiar consumer app, give them a relevant business challenge, meaningful context, and realistic constraints.
Then have them present their thinking live.
The discussion that follows can reveal far more than the final presentation:
How did they frame the problem?
What assumptions did they make?
Which tradeoffs did they consider?
How do they respond to challenging questions?
Can they explain and defend their decisions?
How do they incorporate feedback?
Cross-functional interviews can add another valuable perspective, showing how candidates collaborate with people outside their immediate discipline.
The lesson: Evaluate the thinking behind the work, not just the finished deliverable.
AI may help candidates produce stronger outputs, but the ability to reason through ambiguity, exercise judgment, and collaborate remains essential.
6. Don't underestimate what you learn from spending time together.
While structured interviews and assessments are valuable, the group also emphasized the importance of observing candidates in realistic working environments.
One example involved bringing a senior sales candidate to an in-person company offsite.
Within hours, the team noticed behavior that hadn't surfaced in earlier conversations: the candidate repeatedly talked over others and struggled to engage collaboratively.
That experience revealed important information about how the person might work with the team.
The broader takeaway wasn't that every interview needs to happen in person. It's that traditional interviews don't always reveal how someone behaves when they're actually working alongside others.
The lesson: Create opportunities to observe collaboration, communication, and judgment in practice.
For senior hires especially, consider working sessions, cross-functional discussions, or other realistic interactions that help both sides understand what working together would feel like.
7. The strongest hiring strategies prioritize fit over headcount.
Another theme was the importance of hiring for what a company actually needs—not simply filling every role in the original plan.
Wes and Zakir shared examples of advising founders to hire one person instead of three when the business didn't yet require a larger team.
This reflects a broader principle: hiring more people doesn't automatically create more progress.
Especially in an AI-enabled environment, leaders need to think carefully about which capabilities are genuinely missing, which responsibilities can be combined, and where technology can create leverage.
The discussion also reinforced the importance of matching candidates to company stage.
The ideal hire for a seed-stage startup may look very different from the ideal hire for a Series B company or a mature enterprise.
The lesson: Define the business problem before defining the position.
Ask what success looks like, what capabilities are essential, and whether the role is truly necessary at the company's current stage.
A smaller team with the right people can be more effective than a larger team built around assumptions about future needs.
8. Relationships and reputation still matter in an AI-driven hiring market.
AI may be changing how candidates are discovered, but trusted relationships remain an important part of recruiting.
At smaller organizations, referrals can be especially valuable because they provide context that applications alone often cannot.
The group also discussed the value of maintaining long-term relationships with recruiters.
For candidates, a strong recruiter relationship can provide guidance on positioning, interview preparation, and identifying opportunities that fit their experience.
For hiring managers, those relationships can create access to a network of candidates whose strengths, interests, and working styles are already understood.
The discussion also highlighted an often-overlooked opportunity: constructive feedback.
Candidates rarely receive detailed explanations for why they weren't selected. Yet thoughtful feedback can help someone improve how they present their experience and prepare for future opportunities.
The lesson: Invest in your professional relationships before you need them.
Build a reputation for delivering results, maintain relationships with people who understand your strengths, and be intentional about how you communicate your career story.
9. Some of AI's biggest opportunities may be outside the technology industry.
One of the most interesting parts of the conversation looked beyond traditional technology companies.
The group discussed opportunities in established service businesses and industries that haven't yet benefited from modern software and automation.
Think HVAC companies, manufacturing businesses, and other operationally intensive organizations where processes may still rely heavily on manual work or outdated systems.
As owners retire and new operators enter these industries, AI creates opportunities to modernize workflows, automate administrative tasks, and improve operational efficiency.
For product and technology leaders, this raises an interesting possibility.
The next wave of meaningful innovation may not be limited to building another software startup. It could come from applying technology to businesses that have historically been underserved by it.
The lesson: Look beyond familiar technology markets when evaluating where your skills can create value.
Deep operational problems, overlooked industries, and businesses with outdated processes may offer significant opportunities for leaders who know how to combine technology with business judgment.
The through line: The bar is shifting from what you can produce to the value you create.
AI is changing the mechanics of building products and evaluating talent. But throughout the discussion, one theme kept resurfacing:
The ability to produce more isn't the same as the ability to deliver better outcomes.
For product leaders, standing out increasingly means demonstrating business impact, sound judgment, customer understanding, and the ability to make smart decisions in changing environments.
For hiring managers, it means looking beyond credentials and polished deliverables to understand how someone thinks, collaborates, and solves real problems.
And for anyone thinking about their next career move, it means being intentional about how they communicate their strengths and the value they bring.
The tools will keep evolving. The opportunity is to develop—and recognize—the capabilities that matter beyond the tools themselves.
