Better Results Together
The rumors of teamwork’s death have been greatly exaggerated.
I've built my entire career around one conviction: diverse people working closely together create bigger and better outcomes than brilliant individuals working alone.
Now, as AI tools and techniques proliferate and teams shrink to three-person units of product engineers managing a fleet of agents, I'm watching that belief get stress-tested in real time. We're not just changing how teams work. We're accidentally dismantling what makes teams work at all.
Teams Are Systems
A team is not simply a collection of people sharing proximity on an org chart. That’s a workgroup or a department, but it's not a team.
A team is a system made up of people. Dr. Russell Ackoff said it best:
A system is never the sum of its parts; it's the product of their interaction. The performance of a system doesn't depend on how the parts perform taken separately, it depends on how they perform together – how they interact, not on how they act, taken separately.
A good team is like Voltron: the parts come together to form a greater whole. There’s even a German word for this concept: Gestalt.
In other words, we produce better results together.
I’ve built my entire career in software development around this belief.
Effective Teams: Small & Capable
What are the systemic properties that make some teams extremely productive?
They possess all the capabilities necessary to produce value. The Agile movement converged on a nice definition of team: groups of cross-functional people aligned on delivering value.
They’re small. Amazon's management culture defined "two pizza teams"—small teams whose catering order could be satisfied by two pizzas. Size matters when it comes to teams because we're trying to reduce the number of communication paths.
And when small, cross-functional teams rigorously manage their work-in-progress, prioritize learning, and focus on meaningful outcomes? They can produce an outsized impact.
My Gold Standard
My gold standard for teaming comes from what we did at VersionOne a decade or so ago: a product manager, a floating designer, and a pair or two of engineers working closely together.
Our teams also had a versatile tester role. These people curated various automated testing suites while helping the rest of the team frame their work in testable specifications. The term “tester” undersells their contribution. They were detail-oriented product people, on hand to collaborate with developers while freeing up product managers to get out of the building and talk with customers.
Our teams were also super capable. They could, for example, adapt their workflow in near real-time to suit the feature they were working on. One quarter, a team would use a kanban-like process being fed by a detective board maintained by their designers. In the next quarter, they’d switch to ticket-based development using GitHub issues. Their workflow served their mission, and the mission was never the process itself.
Context matters here. We worked in a collective ownership model. We had only one main product, a couple of small sidecar products, and a significant number of integrations. However, with 5-6 teams, we were able to deliver substantial value to our customers quickly by working in an ultra-collaborative manner.
Nostalgia is a hell of a drug, but given a similar set of circumstances, this will always be the gold standard for me.
Who needs a team when you have Jira?
Along with the many positive aspects of the Agile and DevOps movements came a commercial gold rush in tooling, and with that, we lost some of the principled approaches we found in eXtreme Programming and DevOps culture. And, yes, I’m aware that VersionOne, as an agile tracking tool, had a part in this.
It became common for me to work with groups that focused on tooling and tickets rather than customers, users, and their unmet needs.
Teams are the indivisible unit of performance.
Unfortunately, every good idea eventually gets twisted by monetization. “Ride the wave,” conventional wisdom tells us. For DevOps, it was tool mania. For Agile, it was an overindexing on process, certification, and tracking tools. As for AI? This movement requires vast capital expenditure, so monetization is built in from the start.
One shouldn’t entirely blame these revolutions. They still produce a lot of good. Still, each wave of innovation took its shot at teamwork, promising tools that would shuttle issues to engineers, measuring them as replaceable parts. By default, vendor tools appear to be winning over the principled style of work that once held teams together as the indivisible unit of performance.
The Individual vs. The Collective
In the GenAI era, some teams are getting extremely small. One pizza team! Three slice team!
Solopreneurs are the future! The sole founder unicorn startup! Today’s zeitgeist bends toward certain stock characters: the rugged individual and the eccentric genius. Ayn Rand would be proud.
This kind of talk is the trade of startup hustle culture. It’s a cool idea on the surface. New tools and techniques are certainly empowering smaller outfits to have outsized productivity and reduced development costs.
While these trends are most visible in startup or B2C contexts rather than big enterprises, it's not hard to see the writing on the wall: team sizes are shrinking.
We're seeing this new role (which isn't new at all) called the "product engineer." This is an exceptionally versatile person who can do everything a product manager can do, everything a designer can do, and everything an engineer can do, with the caveat that they're heavily augmented by AI tooling.
Hmm… Where have we heard this before? Full-stack developers, of course!
Embracing New Forms of Collaboration
A more optimistic view is that we’re converging toward small, 3-5 person teams where everyone has a strong foundation in product mindset and reflexive skills that enable rapid exploration and experimentation in parallel. Smaller, faster, better.
The nature of work itself is changing in ways that necessitate a reevaluation of what collaboration entails.
Here’s one scenario: as AI tools become more prevalent in software engineering and product development, the interactions between engineering teammates may begin to approach what Toyota calls Yokoten, which means “the horizontal deployment or lateral sharing of best practices.”
Yokoten isn't your typical corporate "best practices" initiative where someone creates a wiki that nobody reads. It's an active, intentional practice of:
Spotting improvements worth spreading - Not every local solution deserves global deployment, but the good ones shouldn't stay hidden.
Adapting, not copying - Smart teams don't just copy-paste solutions. They understand the principles and adapt them to their context.
Benefiting from feedback loops - When Team A shares a new prompting with Team B, Team B's usage might improve the original solution or make it more generalizable.
Suppose each engineer is managing a fleet of AI agents, iterating on prompts and evaluations. That’s full-stack, at least in the application layer of AI Engineering. Here, collaboration becomes less about dividing tasks among people with different yet complementary skills and more about sharing our insights around achieving results.
In this near future, teams may resemble communities of practice. Instead of a product manager, designer, and engineer each owning their functional skill set, you might have three people who are all wielding similar AI-augmented capabilities but bringing different perspectives to how they prompt, validate, and iterate. The collaboration occurs at a meta-cognitive level—how do we improve our ability to work with these tools together?
Wither teaming?
Does this mean the concept of teams is headed toward an extinction event?
I don’t think so. Here’s why:
1. Humans are still the best LLM. Despite all the advances in AI, humans bring context, intuition, and judgment that no tool can replicate. We understand nuance, navigate ambiguity, and make connections that stochastic algorithms cannot, especially when we’re building for our species.
2. Collective wisdom rejects bad ideas. Having someone to bounce ideas off generally helps reject bad ideas. Sure, this can create either “naysaying” or “egging on” conditions, but tapping into collective wisdom is invaluable. The best ideas survive scrutiny. The not-so-great ones get chalked up to learning, rejected before they waste too much time.
3. Teams create accountability networks. If I say I'm going to do something to the people I work closely with, I'm more likely to do it. I've put that commitment external to myself, and that social contract matters. It’s how we evaluate the reliability of our teammates.
4. Complex systems require some degree of specialization. As Robert Heinlein said, "specialization is for insects," but in larger, more complex systems, you need elements like human resilience and risk management to mitigate knowledge silos. Teams are the most effective means to these ends.
5. Progression happens around other people. Teams and community can provide access to aspirational people who challenge you to elevate your game. I learn a lot from reading and through solo trial and error, but my most profound learnings have come from working closely with people who were masterful or virtuosic in a skill I envied.
Teaming Evolution, Not Extinction
What we're witnessing isn't the death of teams, it's their evolution. For a teaming nerd like me, that's something to be excited about.
The dinosaurs of team structure—sequential handoffs, rigid role definitions, process-heavy coordination—are indeed facing extinction. Good riddance! The core principle that diverse perspectives and skills can create something greater than the sum of their parts? That's the mammal that survives the meteor.
Tomorrow's teams will likely be smaller, more fluid, and AI-reflexive. They might form and dissolve more rapidly in response to specific needs rather than as permanent structures. We could certainly improve in that area as an industry. But they'll still be teams because the fundamental challenges of building great products (understanding users, navigating complexity, maintaining quality while moving quickly) remain greater than any individual can overcome alone.
The value of human collaboration extends beyond task division and specialized skills. It's about collective judgment, accountability networks, the spark of unexpected connections, and the resilience that comes from shared purpose. These qualities remain relevant even in the wake of technological advancements. They become even more essential as we navigate increasingly complex systems.
Whether we call it Yokoten, Gestalt, or good old-fashioned collaboration, we're still talking about humans creating better outcomes together than they could achieve alone or paired with the most advanced neural network. The mechanics will and should evolve, but the principle remains rock solid: we produce better results together.
That's not nostalgia talking. It's systems thinking shouting from the mountain. And until someone invents an AI that can simultaneously replace human taste, judgment, collaboration, and accountability without posing an existential threat, I'll continue to bet on teams, however they might take shape in the years ahead.