The Talent Stack for the Next Ten Years
How to build your productivity pod with AI
A lot has changed in the talent stack during the last few years. The most radical of those changes has been gradually taking place these past twelve months since the launch of ChatGPT. Whether you’re a founder, an operator, or a job-seeker, odds are that you’ve been impacted by the recent changes in talent dynamics. Understanding the new talent stack is crucial to navigating this new paradigm.
How Did We Get Here
Before we dive deeper into the new talent stack it’s worth doing a quick walkthrough of how we got here. In the last five years leading up to the launch of ChatGPT in late 2022, the most impactful tectonic shifts that got us here are listed below. I won’t go into detail about each one since there’s enough content written online about those shifts but they are worth mentioning as the context for this post!
Tectonic Shifts in the Tech Talent Ecosystem
The gloating years: 2017 - 2020 — Team size as a proxy for growth
The COVID rebound: 2020 - 2021 — Hiring wars
Raising interest rates: 2022 - 2023 — Layoffs
ChatGPT: 2022 - ongoing — Efficiency
The Impact of Talent Dynamics on Tech Teams
If I had to summarize the talent dynamics of the last five years it would be through an inverted U graph. On the X-axis you’d have a time horizon and on the Y-axis you’d have team size. Startups grew their teams aggressively through the post-covid boom until interest rates began to rise. Rising interest rates led to massive layoffs between 2022 and 2023 that have so far impacted ~500k individuals according to Layoffs.fyi.
But austere measures in startups and the VC ecosystem weren’t the death sentence of tech talent. The nail in the coffin for tech employees was ChatGPT. The emergence of ChatGPT and the hundreds of Gen-AI tools and LLMs that followed since late 2022 have completely reshaped the talent dynamic of tech companies.
It’s too early to tell if the talent dynamics are more or less favorable for tech employees or companies. What is evident is that the talent ecosystem is different. Both companies and individuals can become more productive by leveraging AI and by collapsing the talent stack, as Scott Belsky coined, in order to keep talent resourcing efficient.
Enter, the new talent stack for the next ten years.
The New Talent Stack
The benefits of AI and leaner teams have gotten founders and investors excited about increasing the performance of their teams. Individuals should be excited as well. AI is the first platform innovation in more than a decade and the promise for individuals to improve their day-to-day is pretty stark.
Operators should think about their value as a pyramid of value (aka “Productivity Pod”) instead of individual value. Depending on your role and seniority, there might be some permutation of the productivity pyramid which looks like this:
The Three Layers of the Productivity Pod
Foundation layer: Automation
Personal or company GPTs
AI-generated content
OpenAI’s API
Middle layer: Virtual Assistant (VA)
Repetitive tasks that can’t be automated
Vetted, offshore VAs with functional expertise
Top layer: functional expert
Strategic thinking
Creative thinking
Connecting the dots
Managing teams
Examples of Functional Productivity Pods
Product
Product Lead’s pod instead of:
Product Manager
Product Ops
UX designer
Recruiting
Head of Recruiting’s pod instead of:
Recruiting Lead
Recruiting Coordinator
Recruiting Operations Manager
Sales
Sales Lead’s pod instead of:
Account Manager
SDR
Sales Ops Associate
Marketing
Content Marketer’s pod instead of:
Content Marketer
Copywriter
Graphic Designer
Increasing Your Productivity
Productivity pods should increase the performance of most teams. For the most data-oriented, you can demonstrate this numerically by using Katie Dill’s formula: Performance = potential - interference. Katie is the Head of Design at Stripe and goes in-depth about team performance in this episode with Lenny.
💡 Formula for performance: performance = potential - interference (potential minus interference)
There’s no doubt that AI increases individual potential. With higher potential, the numerator in the formula increases, therefore, increasing productivity for individuals and teams. It’s as simple as that.
Unit Economics of Productivity Pods
The figures will vary depending on functions and seniority but will hover at around the following numbers:
Resourcing for Traditional Teams (per year)*
Cost of functional expert: $150k - $200k
Cost of functional associate or manager: $100k
Cost of ops associate or ops manager: $100k
💡 Total cost of running traditional teams*: $350k - $400k
Resourcing for Productivity Pods (per year)
Functional expert: $150k - $200k
Virtual assistant: $30k
Tooling: $10k
💡 Total cost of running productivity pods: $190k - $240k
*The range depends on whether the traditional team operates with two or three people
Finding or Building Your Productivity Pod
If you’ve read this far, you’re probably thinking about how to find or build your productivity pod. There are multiple ways to leverage AI and the talent-collapsing approach. At the risk of simplifying it, here’s how I think founders and operators should approach building out their productivity pod:
Founders
Make sure your functional leaders are experienced and understand this vision of a productivity pyramid
Provide the resources to equip functional leaders with VAs. There are dozens of companies offering VAs either for specific functions like MarketerHire or more general VAs like Athena
Encourage your technical team to build foundational automation, including OpenAI’s API, and coach your functional leaders to maintain those systems. Alternatively, if you prefer to offload the technical automation work you can leverage talent marketplaces like WeLoveNoCode to find expert no-coders to help you build your infrastructure.
Operators
Invest in your personal development by joining cohorts or online certifications. Examples of these could be DemandCurve for Growth or Product School for Product Managers.
If you haven’t used a VA before, ask your network for help. There are so many leaders in tech who have now learned the ins and outs of working with overseas VAs. There’s a learning curve so make sure you don’t overlook this one!
Pick up or sharpen your technical skills by learning how to use automation tools like Airtable, Make, and Zapier, or if you’re a newbie, start with the basics with courses like the ones offered by No Code MBA
Conclusion
We’re in the early days of the AI revolution. We can’t yet tell if the impact of AI will benefit the supply side or the demand side more in the talent ecosystem. What is certain is that AI is here to stay, and as with all platform revolutions before this one, the earlier you embrace it, the further ahead of the curve you will be.