Every organization has a theory of how to fix coordination problems.

Most of them are wrong.

The default theory goes like this: things are getting complicated, people are misaligned, decisions aren’t being made. The fix? Add someone. Hire a project manager. Create a coordination role. Put a person in the middle to keep things moving.

It feels logical. It rarely works. And the reason it doesn’t work is mathematical before it’s even managerial.

Adding a person doesn’t add one connection. It adds many.

In any network, the number of potential communication channels is:

n(n–1) / 2

Five people: 10 channels. Ten people: 45. Add one “coordinator” to a team of ten and you don’t just add one link. You add ten new ones. The new manager doesn’t solve the coordination problem. They become a new surface area for it.

If the underlying system doesn’t change, the new hire spends most of their time managing the relationships their own presence created.

This is why adding management to a broken coordination system so often makes things slower, not faster. You haven’t fixed anything. You’ve made the problem more expensive.

Every layer you add is a lossy translation

Even when a manager does exactly what they’re supposed to do (synthesize information, relay priorities, keep people aligned) something goes wrong in the transfer.

A manager takes ten signals from their team and condenses them for their boss. In that condensation, the specific, awkward, important details get smoothed into something that sounds more like progress than it is. The edge cases disappear. The real blockers soften. The thing that actually needs a decision gets buried under the summary.

By the time the CEO’s strategic intent has passed through three layers of management to reach the person executing it, it has mutated into a set of KPIs that nobody finds particularly meaningful.

The distortion is structural. Every human router introduces noise. The signal degrades with every hop.

High-performing organizations don’t solve this by finding better translators. They build infrastructure that carries intent without distorting it, where the original meaning survives the trip.

The deeper problem is what layers do to the people inside them

There’s a more insidious effect that doesn’t show up in org charts.

When you add a layer of management between a person and their work, you’re sending a signal, even if no one intended to. The signal is: we don’t trust you to coordinate your own judgment.

It may be a nice manager. It may be a brilliant manager. But their presence as a gatekeeper changes how people behave. Decisions start requiring sign-off. Ideas need to be “socialized.” Ownership becomes blurry because technically the manager holds the thread.

Slowly, the people underneath stop practicing the skill of leadership. They stop asking “what needs to happen here?” They start asking “what do you want me to do?”

My friend Graziella has been watching this play out in her coaching work right now. Her observation:

burnout in these environments isn’t about workload. It’s about futility. Bandura’s research on self-efficacy backs this up. The behavior doesn’t change because people are weak, it changes because the system has repeatedly signaled that their actions don’t matter. Eventually people stop proposing. That’s not a character flaw. It’s a rational adaptation to a broken environment.

High-performing teams get built by making accountability visible and inescapable, where people can see the work, own the work, and be measured by it. That is an infrastructure problem, not a headcount problem.

The economic case is just as blunt

Managers are usually among the most expensive people in an organization. And every hour a high-output person spends updating a manager is an hour they’re not doing the work they were hired to do.

In a mediocre organization, this is treated as the cost of doing business.

In an ambitious one, it’s treated as a system failure.

The question to ask is: why pay a permanent tax on something that could be engineered away?

Complexity doesn’t get managed away. It gets engineered away. A management layer is a permanent solution to what was originally a temporary lack of clarity. Once it exists, it defends its own existence through more meetings, more check-ins, more process.

The people who’ve already figured this out

There’s a specific type of professional who has lived through this and come out the other side with a particular kind of impatience.

They’ve been in organizations where their best work got diluted by process. Where decisions they made got relitigated three levels up. Where a meeting was called to “socialize” something everyone already knew. Where “who owns this?” was a question that never got a clean answer.

They care too much to accept a system that buries signal in bureaucracy.

What they want is cleaner rules. Visible commitments. Clear ownership. A system that shows who said what, who owns what, and what happens next, so they don’t have to fight for clarity at every turn.

They’re not asking for less accountability. They want more of it, for everyone.

They show up in a few recognizable forms:

• The person who has burned out in a traditional organization and is looking for somewhere their work can actually land.

• The systems-thinker who looks at a tangled Slack channel and wants to redesign the whole thing.

• The craft-obsessed executor who wants to spend their time doing the work, not reporting on it.

• The person who doesn’t wait for a title to take ownership and just sees the gap and fills it.

What all of them share: a high tolerance for truth and a low tolerance for friction.

In most organizations, these people get called “difficult”. They’re the ones asking “wait, did we actually decide that?” and “who is the primary owner here?” They get flagged as impatient, as too demanding, as hard to manage.

They are, in fact, the most valuable people in any organization. They just need a system that matches how they think.

Clarity is a technical requirement

The tension here isn’t really between management and autonomy. It’s between two modes that every organization needs: managing for predictability (decisions, governance, execution) and leading for complexity (shared sense-making, collective judgment, experimentation).

Most organizations only know how to do the first. And under pressure, they double down on it. More layers, tighter control, more reporting. It feels decisive. It looks like leadership. What it actually does is collapse the right side of the loop entirely, the part that produces agility, breakthrough thinking, and resilience in shifting markets.

The infrastructure argument isn’t about removing accountability. It’s about building the operational layer tightly enough that the human layer can actually function. When decisions, ownership, and next steps are clear and visible, people don’t need a manager to hold the thread. They can see the map and navigate themselves.

That’s what separates the organizations that move fast under uncertainty from the ones that freeze. Not fewer rules. Better infrastructure. A system tight enough that the humans inside it can lead, not just execute.

The organizations that will move fastest in the next decade will build coordination into the infrastructure itself, so that intent travels cleanly, decisions stick, ownership is unambiguous, and the people doing the work can see the full picture without asking for a briefing.

That is an engineering problem. Always has been.

And the people who’ve always known this are already looking for each other.

Growth Wise is building the infrastructure layer for teams that are done paying the coordination tax. It takes human coordination, qualifies its structural integrity, and compiles it into durable organizational reality. PM tools track execution. Growth Wise compiles coordination into executable reality.

A month ago, Jack Dorsey and Sequoia published a provocation: what if AI could replace middle management entirely?

The idea centres on what they call a "World Model." A centralised system that ingests every Slack message, document, meeting transcript, and KPI to create a continuously updated digital twin of the organisation. AI uses that picture to infer priorities, surface insights, and guide action. With comprehensive, real-time context available to everyone, the information asymmetry that managers exist to solve would disappear. People closest to the work would know what is happening and what should happen next, without waiting for direction to travel down a chain of command.

The argument contains a real insight. Middle management, as most organisations practice it, is an expensive patch for a bandwidth problem.

But solving the information problem does not solve the coordination problem. Context without commitment is just a well-informed group of people drifting in different directions. The hardest coordination problems are commitment problems. And commitment is a social act, not a computational one.

Context without commitment is noise

Organisations don’t fail because they don’t know what to do. They often fail because no one is clearly accountable for doing it.

You can give every person in the organisation the entire company’s data, strategy, and real-time signals. It doesn’t matter. Nothing becomes real until someone owns what happens next.

What people agreed to matters more than what the algorithm predicted.

We are pilots, not sensors

At enterprise scale, the World Model makes centralised sense-making the default. The system models the world; humans feed it data from the edges. The AI does the high-status work: sense-making, prioritisation, strategy. Humans execute and refine its outputs. The human gets to pick from a shortlist the machine already curated.

That is not leadership. That is editing.

We reject that inversion. The human is the sovereign pilot, the sovereign creator. By “sovereign,” we mean the one who holds judgment and is accountable for what happens next. The AI does not route the human; the human uses the AI to structure their intent. AI can and should enhance local judgment, surface patterns humans miss, and accelerate the thinking that precedes every commitment. The question is who remains sovereign in the loop.

Where intelligence lives

The World Model centralises sense-making. One system ingests everything, computes priorities, and distributes direction to the edges. The people closest to the work receive guidance from a system that does not share the consequences of their decisions.

We believe judgment becomes meaningful where the work happens: in the negotiation between people at the point of contact. The system’s job is not to decide. It is to make what was decided visible, durable, and shared.

Complicated vs. complex

The World Model is strongest on complicated work. It struggles where work becomes genuinely complex.

Complicated work has right answers. It can be decomposed, analysed, and optimised. Complex work has competing priorities, ambiguous signals, and tradeoffs that only make sense in context. You cannot optimise your way through complexity. You have to coordinate your way through it.

AI will increasingly handle the complicated. The work that remains for humans is the complex work: navigating ambiguity, making tradeoffs, committing under uncertainty.

This is where the “verifier’s curse” hurts. The World Model improves AI agent performance on complicated tasks. Humans verify the output. The better the agent gets, the less the human matters. Then suddenly you are a quality check on a machine that is getting better at the thing you’re checking. That is a shrinking role.

The opposite trajectory is possible. As AI takes over the complicated, humans are freed for the work that actually requires them: coordinating with each other to decide what matters and what happens next.

Why you can’t skip the human part

Humans aren’t pre-wired for complex coordination. We figure it out by working with the same people repeatedly until patterns form and shared understanding develops. That shared understanding is the real infrastructure of trust. It’s what lets a team of ten move faster than a team of a hundred.

The World Model assumes you can replace that with a database and a context window. But context is not trust.

When a system assigns priorities without shared understanding, it feels like a suggestion from a stranger. People treat it that way. Without the moment where people align, decide, and take ownership, the work has no weight. It doesn’t carry forward.

The best systems help you make sense of the situation and make sure decisions are clearly made, owned, and followed through. Neither works without the other.

Creators, not editors

We don’t believe a small group of people are creators. We believe most people are capable of meaningful judgment. But the systems we’ve built often reduce them to editors. AI will amplify that pattern. It will either concentrate judgment further, or make it easier to distribute it.

People don’t lack judgment. Most systems just don’t ask for it. A system that treats humans as verification layers will get people who behave like verification layers.

The answer is not to suppress judgment. It is to structure it.

The choice

AI is forcing an organisational design choice that most haven’t recognised yet. There are two default paths.

Path one: reinforced hierarchy. AI increases visibility and control. More dashboards, more oversight. Humans become more legible to power.

Path two: centralised World Model. AI becomes the sense-making centre. Humans become inputs and execution nodes. The system decides. Humans approve.

Both paths move judgment away from the people closest to the work. And if you don’t choose, the system will default to centralisation. That is the real risk. Organisational design can quietly demote human judgment while pretending to augment it.

To preserve the role of human judgment where it actually matters, we need a different path.

Not more middle management or centralised intelligence, but coordination as infrastructure. A system where decisions are made explicit, ownership is clear, and commitments are durable. Where what gets decided in rooms actually carries forward into the work.

AI expands the space of possibilities. It does nothing to ensure anything actually becomes real. That part still requires humans. The question is whether we build systems that honour that fact or not.

Most systems optimise for knowing what to do. The harder problem is making sure it actually happens.

As AI becomes embedded in how organisations operate, the real question is not how much work machines can take on, but where human judgment belongs.

Ultimately, the advantage will go to organisations that know how to balance automation with human judgment. Knowing what to optimise and what must remain shaped by human judgment, imagination, and people thinking together.

There is a third path. Growth Wise is built for organisations that have chosen it. Where human judgment is structured, not suppressed, and where what gets decided together actually survives into execution.

Jack Dorsey and published something worth reading last month. “From Hierarchy to Intelligence” is Block’s public statement of intent to restructure itself as an AI-coordinated company, replacing layers of management with a shared world model that gives every person in the organization the context they need to act.

at Superesque wrote a sharp breakdown of what Block is actually building and why it matters for founder-led teams. Read it. It’s one of the clearest accounts I’ve seen of where this is all going.

One thing he touches on deserves more attention.

The argument Dorsey and Botha are making (that AI should replace what management does: routing information, maintaining alignment, coordinating work) is the same argument I’ve been making from a different angle.

The coordination tax isn’t a people problem. It’s a system design problem. Every layer you add between a decision and the person executing it is a translation layer. Every translation layer introduces distortion. The signal degrades. The intent mutates. By the time strategy reaches execution, it has become something else.

Sangeet Paul Choudary calls this “coordination without consensus.” AI reduces the translation costs of turning one team’s outputs into another team’s inputs. You don’t need a manager in the middle to relay, interpret, and repackage. The infrastructure does it cleanly, at speed, without the loss.

Growth Wise is built to solve exactly this problem, at a different scale than Block but with the same underlying logic: coordination should be an infrastructure problem, not a headcount problem.

The more interesting question is what gets coordinated.

Block’s world model is ambitious. It aggregates millions of data points — transactions, decisions, code, designs, plans — into a continuously updated picture of what the organization knows and does. The AI intelligence layer reads that picture and acts on it.

But there’s a layer that picture doesn’t capture cleanly. The layer where humans are actually in a room together, negotiating reality.

The decisions that matter most don’t happen in task trackers or code repositories. They happen in conversations, where assumptions collide, where commitments get made, where the ambiguity that no document can fully resolve gets worked through in real time. That is the richest coordination moment any organization has. And it is also the most consistently under-captured.

Most organizations have already patched this with AI notetakers. The transcript exists. The summary gets generated. But a summary of what was said is not the same as a structured record of what was decided, who owns it, and what happens next. The notetaker captures the conversation. It doesn’t qualify the coordination. It doesn’t tell you whether a real commitment was made or whether people just seemed to agree. That distinction is where the signal still gets lost.

Ambiguity doesn’t announce itself. It just shows up later as a missed deadline, a relitigated decision, or a project that has to start over because nobody wrote down what was actually agreed. The cost compounds quietly.

That is the gap worth closing.

The convergence Dr. Tim Rayner identifies — between what Block is building at enterprise scale and what forward-thinking founder-led firms are building now — is real. The architecture is the same. Build a shared knowledge base. Give people access to it. Let AI do the coordination work that humans were never well suited for anyway.

The difference, as he rightly points out, is what role people play inside it.

At enterprise scale, the risk is that people become appendages. The system models the world; humans feed it data from the edges.

At the scale of a founder-led team, the opportunity is different. The knowledge base is comprehensible. The people inside it can actually inhabit it rather than just serve it. The AI amplifies what they know rather than routing around them.

There’s a more precise way to say what the bottleneck actually is, and where Growth Wise sits in this picture.

The goal isn’t primarily knowledge accumulation for improved automation. That framing, while part of it, misses where the real friction lives. The bottleneck has shifted upstream. Execution is getting faster every month. The bottleneck is now the moment before execution: the work people do together to create shared reality.

Coordination, properly defined, is that work. Understanding, decisions, commitments, ownership, priorities, handoffs. The moment where a group of people negotiates what is true, what matters, and who is responsible. That moment is where collective judgment actually occurs. It’s where sensemaking happens. It’s where the direction of work gets set.

And it is still, in most organizations, almost entirely unstructured.

Most world models start from the bottom up: aggregate everything, build the picture, hope coordination falls out of it. Growth Wise starts from the highest-fidelity moment. Synchronous dialogue sits at the top of any coordination hierarchy. Structure that layer reliably, and everything else (documents, plans, async updates, downstream systems) connects in as supporting context rather than load-bearing foundation. The constraint is the architecture.

AI notetakers capture what was said. Project tools track what gets done. Neither touches the quality of what happened in between: whether a real decision was made, whether the commitment is durable, whether the people in the room actually reached shared understanding or just moved on.

Growth Wise is built for that layer. The goal is to recognize where judgment happens, give it structure, and make the output of collective sensemaking durable enough to survive into execution.

The encoded knowledge matters. The more important thing is that the moment of coordination, where humans decide together, produces something real that the rest of the system can act from.

Better conversations. Cleaner commitments. A system that lets both humans and machines act from the same structured understanding.

That is the coordination architecture worth building toward.

Growth Wise exists to help ambitious organizations innovate and execute at a high standard by ensuring that ideas, decisions, and commitments survive into reality.

For a long time, a strange thing has sat at the center of modern work. We’ve treated it as normal.

A group of people gets into a room. They talk through priorities, tradeoffs, risks, decisions, dependencies, tensions, and next steps. They negotiate reality together in real time. They do the messy human work of deciding what matters.

And then the meeting ends.

After that, one person is expected to go translate whatever just happened into something the organization can use. They write the notes. They summarize the decisions. They assign the actions. They clean up the ambiguity. They interpret what people meant. They decide what to leave out. They update the project management tool. They turn a living conversation into a static record.

That person is often a manager.

And we’ve been using humans to do a job that is both too mechanical and too lossy for humans to do well at scale.

Meetings are where organizational reality gets negotiated

The interesting AI shift isn’t that it will replace all thinking. It’s that it will make us more aware of which kinds of work humans should still be doing.

One answer is clear.

Humans should spend more time doing what only humans can do well together: making tradeoffs, negotiating meaning, aligning around uncertainty, resolving tension, committing to action, deciding what matters.

That work happens most powerfully in synchronous conversation.

Most meetings are terrible. But when people are in the same room, physical or digital, and have to respond to each other in real time, something happens that no document or workflow tool can replicate.

Ambiguity gets surfaced. Assumptions collide. People challenge each other. Misunderstandings become visible. A decision can actually become shared.

That’s a higher-fidelity coordination moment than a status update in a tool or a polished strategy memo. A strategy document matters. A plan matters. A procedure matters. But they are downstream artifacts. The conversation is where the reality changes.

The bizarre inefficiency at the center of work

Once you see that, the current workflow looks ridiculous.

We gather the expensive humans. We do the expensive human work. Then we ask one of those humans to do clerical translation after the fact.

That person becomes the bottleneck between human coordination and organizational action.

They are expected to remember: what was actually decided, what people only seemed to agree on, what was deferred, what was assigned, what was implied, what still felt unresolved, and which part of the discussion mattered enough to log.

That is not a neutral act. It is interpretive. It is filtered. It is biased. It is incomplete.

The notes are never the meeting. The project management update is never the conversation. The official recap is always a compressed, partial rendering of what actually happened.

And yet many organizations still rely on that rendering as if it were reality.

This is one reason so much work feels fuzzy. People aren’t lazy. Teams aren’t stupid. We are simply forcing human beings to manually re-encode shared reality after the richest moment of shared sense-making has already passed.

That is terrible system design.

AI changes the threshold of what feels absurd

In a world where AI can draft documents, generate prototypes, summarize research, analyze transcripts, and route information between systems, the manager-as-transcription-layer model starts to feel even more irrational.

You can feel the absurdity more sharply.

AI can write code, synthesize a market landscape, and generate ten versions of a board memo in seconds. But after a meeting, we still expect someone to manually type out who is doing what by when. We still expect a person to open a project management system and reconstruct the discussion from memory. We still expect middle managers to spend energy converting live coordination into admin artifacts.

That doesn’t just feel inefficient. It feels primitive.

The more capable AI becomes, the more intolerable this clerical burden will feel. And many organizations are still running managers as translation middleware — not leaders, not facilitators of judgment, just translation middleware.

That is the layer AI will absorb.

What AI should actually do

The right frame isn’t AI replacing human judgment. It’s this: humans should do more of the work only humans can do well, and machines should do more of the work humans should never have had to do in the first place.

After a real coordination moment, AI should be able to capture what was decided, identify what was agreed, spot what remained partial, register who owns what, and turn the output of the conversation into something that other humans and machines can actually work from.

That is not replacing the meeting. That is not replacing leadership. That is not replacing accountability.

It is removing the clerical translation layer that currently sits between conversation and execution.

Humans negotiate reality together. AI turns that negotiated reality into structured, usable downstream artifacts. That makes sense.

This is not about making people speak like machines

There is an important tension here.

You can’t build systems like this without some ontology. The machine needs structure. It needs categories. It needs a way to distinguish a real commitment from vague agreement. It needs to know the difference between a decision and a partial one. It needs some way of recognizing ownership, alignment, and next steps.

That can sound rigid.

But the point is the opposite of rigid. The machine should carry most of the framework burden so that humans can remain relatively natural.

People should not have to leave every meeting and fill out a ritualized template to prove they coordinated. They should not need to remember which workflow category applies to which discussion. They should not have to behave like clerks for the system.

What they do need, over time, is to become slightly more explicit about a few very basic coordination signals.

• What did we decide?

• What are we aligned on?

• Who owns this?

• What happens next?

• By when?

That is not bureaucracy. That is the minimum viable structure of coordinated action. When those things never become explicit, much of what organizations call coordination is just socially acceptable ambiguity.

Better AI should make better meetings more valuable

There’s a tempting fantasy that AI will reduce the need for conversation. I think the opposite happens.

As AI makes drafting, producing, analyzing, and executing easier, the real bottleneck shifts upstream. The scarce work becomes judgment, tradeoffs, clarity, alignment, priority, commitment.

The more execution accelerates, the more valuable high-quality human coordination becomes.

The meeting of the future should not be a place where people passively report status so that someone can later type it into a system. It should be a place where people do the highest-value work: sense-making, decision-making, alignment, conflict resolution, real commitment formation.

If AI can reliably capture the outputs of those conversations, people can spend less time on clerical aftercare and more time actually thinking together.

That is a more interesting future of work than “AI summarizes your meeting.”

The manager’s role does not disappear. It changes.

The mechanical parts of management become harder to justify as human labor.

The manager who mainly exists to collect updates, restate priorities, translate decisions into tasks, chase follow-up, and manually maintain the system of record — that role is standing on unstable ground.

The manager who can set direction, create clarity, hold tension, facilitate judgment, surface tradeoffs, protect focus, and help groups reach durable commitments — that role becomes more valuable.

Less transcription. More facilitation. Less clerical reconciliation. More structured human coordination.

AI doesn’t replace management. It strips management down to the parts that are actually human.

Why this matters beyond productivity

Every time a meeting ends and one person manually reconstructs what happened, the organization loses signal.

It loses nuance. It loses dissent. It loses uncertainty. It loses the exact form of the commitment. It loses the difference between what was said and what was assumed. It loses the shape of the conversation that produced the outcome.

That loss compounds. Then we wonder why teams revisit decisions, why ownership feels muddy, why strategy doesn’t translate into execution, why meetings multiply, why coordination feels exhausting.

The problem isn’t that people failed to think. It’s that the outputs of thinking were never captured cleanly enough to travel.

My bet

My bet is that this old system will start to feel increasingly absurd.

We’ll look back and wonder why, after a room full of people did the hard work of making sense together, we handed the burden of translation to one person with a notes document and a project management tab.

We’ll wonder why we tolerated so much loss between conversation and action. We’ll wonder why managers spent so much time encoding reality instead of helping shape it.

The organizations that move first won’t be the ones that use AI to generate more internal sludge. They’ll be the ones that use AI to protect the highest-value human work and remove the clerical friction around it.

Better conversations, clearer commitments, cleaner transfer of reality between groups, and a system that lets both humans and machines act from the same structured understanding.

AI as the semantic layer between human coordination and machine execution.

That’s the future worth building toward.

Growth Wise is a Decision Reliability platform. It instruments the human coordination layer so organizations can see which decisions are closing, which delegations are landing, and where coordination is breaking down.

I spoke with a product ops leader who has run operations at three very different companies: a 6,000-person fashion retailer, an IPO-track automotive marketplace, and a 200-person SaaS company. Different industries, different tools, different team sizes, completely different cultures.

The coordination failures were identical at all three.

That’s the part worth sitting with. When the same ops discipline, applied by the same person across three radically different organizations, keeps finding the same gaps, the gap is systemic. Coordination debt accumulates from the product development model itself, not from the particular people or tools involved.

The first thing that always breaks

At every company, the first fix was the same: making sure people understood what they were expected to do within the development cycle. Not which tools to use. Not which ceremonies to attend. Whether they knew their role in the sequence, when their input was needed, what “done” looked like at their stage.

Everything else varied. That stayed constant.

Process compliance is not coordination

At the 200-person SaaS company, teams followed the process. PRDs were written on time. Stakeholders were tagged in Notion. Every box on the checklist was checked. The coordination was still broken.

PMs and engineering managers were tagging each other in 300 to 400 Notion notifications a day instead of sending a five-minute Slack message asking for fifteen minutes to walk through something. The tagging was process compliance. The conversation would have been coordination.

This is a pattern I see repeatedly. A decision can look closed on paper — the PRD was approved, the ticket was created, the stakeholder was notified — but if the people involved never had the conversation where they surfaced the assumption that will blow up in sprint two, the closure is cosmetic. The rework is already baked in.

Where cycles actually break

Delivery, the middle of the product development cycle, is typically the easiest part to fix. The tools are mature. Engineering teams know how to ship.

The hard parts sit at both ends.

At the beginning: choosing what to build. With AI now generating more customer data, user feedback, and market signals than any team can manually process, the curation problem has scaled. Product teams have always struggled with prioritization. The volume of input they need to filter has multiplied, and the frameworks for doing that filtering haven’t caught up.

At the end: closing the loop on whether what was built actually worked. He called it “set and forget” — teams ship a feature and immediately move to the next one. Nobody circles back to assess whether the thing they built solved the problem it was meant to solve. That missing feedback loop means beginning-of-cycle decisions get made against stale assumptions. The cycle compounds.

The handshake signal

At the 200-person company, he tracks one specific leading indicator: time to confirm scope.

The product-engineering handshake — the moment when product and engineering agree on scope for a planning cycle — should happen by week two. When it slips to week four, something upstream is already broken.

The diagnosis forks from there. If scope was unclear, that’s a product problem. If scope was clear but engineering didn’t engage until late, that’s an engineering engagement problem. Either way, the delivery miss is locked in by the time the handshake slips. Waiting for the sprint review to discover it means finding out six weeks too late.

DORA metrics will register the same problem later as extended lead time. The handshake signal catches it weeks earlier.

Proactive ops versus reactive ops

He described two versions of himself.

Reactive: he shows up after deadlines have been missed. He asks why. He traces the cause. He recommends a fix. Teams experience him as the auditor. He called himself “the bad cop.”

Proactive: he catches the signals before the miss. The handshake hasn’t happened by week two. A cross-functional dependency hasn’t been confirmed. A stakeholder hasn’t been looped in. He nudges the team before anything breaks. Teams experience him as the enabler.

The difference between those two modes comes down to one thing: whether he has visibility into coordination signals early enough to act on them. Most ops leaders don’t. They’re buried in the operational work itself, producing reports, running ceremonies, chasing status updates. The reactive mode prevents the proactive mode from ever starting.

What AI actually changed

He uses AI extensively, but not to automate coordination. He uses it to automate the grunt work that was consuming his signal-reading time. Reviewing PRDs for completeness. Translating Jira data into the financial language his CFO needs. Assessing whether a roadmap item maps to stated strategy.

With that time recovered, he spends more of his day reading the human signals that AI can’t parse: which PM hasn’t talked to their engineering counterpart this sprint, which team’s energy has shifted since the reorg, which handshake is three days late and why.

AI made him more of a people-reader, not less. The tool handles the artifacts. The ops leader handles the relationships.

The scrum master gap

The dedicated scrum master — the person who sits in every ceremony and tracks whether process is being followed — is a role in decline. He was blunt about it.

But the needs that role was created to serve are only growing. Tracking whether action items from a meeting actually get done. Making sure the agreement from Tuesday’s planning session survives contact with Thursday’s priorities. Facilitating the conversation between the PM and the tech lead who disagree on scope but haven’t said so directly.

Those functions matter more as organizations add cross-functional complexity. Right now they’re falling through the cracks. The scrum master title is going away. The coordination work it was supposed to do is piling up with nobody assigned to it.

The pattern across all of this is the same. The product development model, and specifically the coordination layer inside it, is the structural variable that determines whether a team ships well or ships late. Tools change. Team sizes change. Industries change. The coordination patterns repeat.

An ops leader who can see those patterns early stops being the auditor and becomes the person who makes the system work.

This is the gap Growth Wise instruments. Not the sprint velocity, not the delivery metrics — the coordination layer upstream of all of it. The signals that tell you a miss is coming before anyone has missed a deadline.

Anthropic published a research paper in March 2026 mapping AI’s theoretical capability against every occupation in the U.S. economy. The number for management occupations: 91.3%. That means large language models can theoretically handle over nine out of ten tasks that managers currently perform.

The instinctive response to that number is panic. Or dismissal. Both miss what’s actually happening.

The 91.3% figure describes management. It does not describe leadership. And the distinction between those two words, which most organizations use interchangeably, is about to become the most consequential sorting mechanism in corporate life.

Kotter drew the line in 1990

John Kotter’s framework is 36 years old and has never been more relevant. Management, he wrote, is about coping with complexity. It produces order. Planning, budgeting, organizing, staffing, controlling, problem-solving. These are systematic, repeatable activities that turn ambiguity into process.

Leadership is about coping with change. It produces movement. Setting direction, aligning people, motivating, inspiring. These are adaptive, relational activities that turn process into purpose.

Peter Drucker landed in the same place from a different angle. Management is doing things right. Leadership is doing the right things. In a knowledge economy, Drucker argued, the management function becomes almost irrelevant because any effective knowledge worker is exercising judgment, not following procedure.

Abraham Zaleznik pushed the distinction further. In a 1977 Harvard Business Review article that still gets assigned in business schools, he argued that managers and leaders are psychologically different types. Managers are “once-born,” people who followed a stable path, identify with the existing social order, and see themselves as conservators of the organization. They minimize risk and conflict. Leaders are “twice-born,” people shaped by some period of personal struggle or inner conflict that detached them from the status quo and gave them the ability to challenge it.

That psychological framing matters here because it predicts what happens when AI removes the mechanical work. The once-born manager, whose identity is wrapped up in maintaining order, loses the activity that defined the role. The twice-born leader, whose instinct is to challenge and redirect, gains time and headroom that the coordination overhead used to consume.

All three — Kotter, Drucker, Zaleznik — were writing before AI existed as a practical tool. They were making a conceptual argument. AI is turning it into an operational one.

What management actually consists of

Break the management function into its component tasks and the automation case becomes obvious.

• Planning and organizing. Setting objectives, creating schedules, allocating resources. A system that can read every meeting transcript, track every commitment, and measure whether decisions closed with enough structural specificity to execute has more planning visibility than any human manager. It doesn’t forget. It doesn’t get distracted by the loudest voice in the room. It measures whether the planning session produced durable outcomes or just the feeling of progress.

• Directing and coordinating. Assigning work, managing handoffs, tracking dependencies. This is the core of middle management: making sure the right people are doing the right things in the right sequence. A system that measures Delegation Flow (the probability that delegated work will actually execute, based on whether the delegation left the room with an owner, a next step, and a deadline) replaces the manager’s “checking in” function with a structural signal that’s available immediately after the meeting.

• Controlling and monitoring. Tracking progress, measuring performance, course-correcting. The status meeting exists because managers need to know what’s happening. A coordination observability system makes the status meeting redundant: it shows which decisions are holding, which are generating rework risk, which topics keep resurfacing as zombie items, and which escalations left without a carrier. The manager’s role as information aggregator gets replaced by a dashboard that’s more accurate and more timely than any human summary.

• Reporting and communication. Status updates, stakeholder alignment, upward reporting. Most of what managers report to their managers is a curated summary of coordination state. The system produces that summary automatically, with structural evidence instead of narrative spin.

Growth Wise, the decision reliability platform I’m building, already does most of this. It scans meeting transcripts for closure signals (was a decision explicitly stated, was an owner named, was a next step defined, was a deadline set). It grades every outcome as Achieved, Partial, or Absent. It tracks Delegation Flow, Rework Risk, Coordination Debt, and Decision Churn across every meeting. It tells you which meetings are producing real coordination and which are performative theater. These are management functions. And a system handles them better than a person because a system doesn’t have selective memory, doesn’t avoid uncomfortable truths, and doesn’t optimize for its own career survival when reporting upward.

What leadership actually requires

Now subtract all of that from the manager’s job description. What’s left?

Deciding what matters. Choosing direction when the data is ambiguous. Aligning people who disagree on where to go. Building trust in an environment where trust has eroded. Having the conversation that everyone is avoiding. Knowing when the process is wrong and having the courage to override it. Developing people by understanding what they need, not what they say they need. Holding the tension between urgency and sustainability.

None of these are systematic. None are repeatable in the procedural sense. All of them require judgment, empathy, relational skill, and the willingness to be wrong in public.

Kotter would call this the leadership function. Drucker would call this doing the right things. AI can’t do any of it. AI can tell you that Delegation Flow dropped from 80% to 38% across the last three planning sessions. It cannot tell you that the drop happened because two senior engineers lost trust in the product direction after the reorg, and the way to fix it is a 45-minute conversation over coffee where you listen more than you talk.

The thought experiment

Here’s the provocation. If a system can handle 91.3% of management tasks, and the remaining tasks are all leadership tasks, then the manager role as traditionally defined is a transitional form. It exists because organizations needed a human to do both the mechanical coordination work and the adaptive leadership work, and there was no way to separate them.

AI separates them.

The coordination work (planning, monitoring, directing, reporting) gets absorbed by systems that do it faster, more consistently, and without political distortion. The leadership work (direction-setting, alignment, trust, development) stays with humans, but now those humans are freed from the mechanical overhead that used to consume 80% of their week.

This changes who should be in the role. The current selection criteria for managers over-index on organizational skills, process discipline, and stakeholder management. These are management skills. The people who get promoted are often the people who are best at the mechanical function. The new selection criteria would over-index on judgment, relational intelligence, and the ability to create psychological safety. These are leadership skills. They’re rarer, harder to develop, and historically undervalued because they were bundled with the mechanical work that organizations actually needed to keep running.

What this means for Growth Wise specifically

A friend recently pointed out that Growth Wise, taken to its logical extension, becomes a mechanism for eliminating the management function entirely. He’s right, and I’ve been thinking about that observation since.

Growth Wise instruments the coordination layer. It makes visible whether decisions are closing, whether delegations are landing, whether the same unresolved topics are cycling back week after week. Every one of those visibility functions used to live inside a human manager’s head. The manager sat in the meetings, tracked the commitments in a notebook or a spreadsheet, followed up with people who hadn’t delivered, and reported the state of play to the next level up. Growth Wise does all of that, structurally, with evidence, without the cognitive load.

But Growth Wise doesn’t tell you whether you’re building the right product. It won’t surface the fact that your best engineer is disengaged because she feels her technical judgment has been overridden three times in a row. It can’t read the room and realize that the cross-team conflict isn’t about scope; it’s about two leaders who fundamentally disagree on company direction and are using the planning process as a proxy war. And it will never tap you on the shoulder and say: slow down, stop optimizing for velocity, and listen.

The tool absorbs the management function. The human who used to perform that function is now freed to do the leadership work that was always more important, was always harder, and was always getting squeezed out by the coordination overhead.

The apprenticeship question

There’s a legitimate objection to all of this. If AI absorbs the coordination work that junior managers used to cut their teeth on, where do future leaders learn? The traditional development path runs through the mechanical function: you track commitments, run standups, manage a small project, and gradually develop judgment by handling progressively larger coordination challenges. Remove the mechanical rung of the ladder and you risk what some researchers call “experience starvation” — a generation of senior people with no pipeline of developed successors behind them.

But this assumes the only way to develop leadership is through management. There’s another path, and organizations that figure it out early will have a serious advantage.

The highest-value leadership skill in a world where AI handles coordination mechanics is the ability to hold a high-quality conversation. Not present at one. Hold one.

Facilitation: the discipline of structuring collective sense-making so that the right people reach the right conclusions together. Setting the agenda so it surfaces the real tensions. Managing turn-taking so the quiet expert gets heard before the loud executive closes the discussion. Keeping the group on the question that matters instead of the question that’s comfortable. Knowing when the room needs five more minutes of silence instead of another slide.

Junior leaders can learn this by being in the room during the moments that matter — not as note-takers, but as process holders. A senior leadership team working through a product direction disagreement or a reorg decision needs someone holding the conversational structure while they concentrate on the substance. That’s a real job, a hard job, and a training ground for exactly the skills that will define leadership once the coordination layer is automated.

The AI system tracks whether decisions closed. The junior facilitator ensures the conversation had the structural conditions to produce real closure in the first place. The senior leaders focus entirely on the dialogue. Three layers, each developing the next one upward.

The uncomfortable implication

If management is a function and leadership is a skill, and AI absorbs the function, then the uncomfortable question is: how many current managers are actually leaders?

Not many. Most organizations have promoted people into management because they were good at the mechanical work. They’re organized, reliable, responsive, detail-oriented. They can run a planning process and keep a project on track. These are valuable competencies, and they’re about to be automated.

The managers who will thrive are the ones who, once freed from the mechanical overhead, turn out to have the leadership skills that were always there but never had room to develop. They’ll use tools like Growth Wise to handle the coordination layer and spend their newly available time on the work that actually requires a human: building alignment, developing people, making judgment calls in ambiguous situations, and having the hard conversations that no dashboard can have for you.

The managers who won’t thrive are the ones whose entire value proposition was the mechanical function. They kept things organized. They tracked things. They reported things. The system does that now. And the leadership skills that would justify their role were never developed because the mechanical work was all-consuming.

This isn’t a prediction about the distant future. Anthropic’s data shows 91.3% theoretical capability today. Gartner predicts that by 2026, 20% of organizations will use AI to flatten their structures, eliminating more than half of current middle management positions. Deloitte’s research on the future of middle management found that by end of 2024, advertisements for middle management roles had already dropped 42% from their 2022 peak. The reorganization of what “manager” means is already underway, and the job market is pricing it in before most organizations have noticed.

The organizations that recognize the management-leadership split and restructure accordingly will develop leaders. The ones that don’t will keep promoting coordinators into roles that coordination software is about to make redundant.

Growth Wise is a Decision Reliability platform. It instruments the human coordination layer so organizations can see which decisions are closing, which delegations are landing, and where coordination is breaking down. The management function, in other words. The leadership function is yours.

A senior leader has five meetings on Tuesday afternoon. Three are informational syncs. She sends a bot to those three. It captures what happened, generates a five-minute highlight reel for each, and she reviews all three in fifteen minutes.

This is no longer hypothetical. Recently, both the Klarna CEO and Zoom CEO Eric Yuan used AI avatars on their quarterly earnings calls. Yuan’s avatar, deployed via Zoom Clips, opened with: “I am proud to be among the first CEOs to use an avatar in an earnings call.” The CEO of Otter is reportedly training his own avatar to share the workload.

The productivity math is obvious. The instinct is right. But most of the conversation stops there — at whether to send the bot, not which meetings can actually survive the swap.

Two types of meetings. One type of bot.

The AI meeting tools on the market right now — Otter, Fireflies, Teams Facilitator, Fellow — are information processors. They record, compress, retrieve, and route. That’s genuinely useful. But every single one of them does only that.

None of them do coordination work. They don’t challenge an assumption mid-discussion. They don’t synthesize competing perspectives into something a group can act on. They don’t read tension in the room. They don’t force commitment. And they cannot bind their human to a decision.

That distinction is the whole ballgame.

At Growth Wise, we classify meetings into types we call Arenas. For example, a Status Sync exists to move information from the people who have it to the people who need it. No stress-testing, no mental model to align, no decision to close. For that Arena, a bot is a structural improvement — because everything that matters in a Status Sync is information, and the bot captures information well.

An earnings call where a CEO delivers prepared remarks to investors? That’s a Status Sync. The avatar is fine.

A decision forum where a cross-functional team has to commit to a roadmap date, stress-test assumptions, and actually close? That’s a different Arena entirely.

Where the bot breaks

When a bot replaces a human in a meeting that requires coordination, three things break.

First, alignment becomes fragile. The absent person receives a summary — a compression of what happened, not participation in it. The result is divergent interpretations that look like agreement until they don’t.

Second, the meeting drifts. Meetings shift Arenas mid-conversation. A Status Sync becomes a Decision Forum when someone raises a real issue. A human pivots with it. A bot continues in information-processing mode. The coordination function just goes missing.

Third, the decision doesn’t close. A bot cannot bind its human to anything. The group believes they decided; the absent person reviews a summary and quietly retains veto power. The decision reopens on Thursday.

When bots start acting, not just attending

Yuan has said Zoom aims to create digital twins of users — avatars that eventually do more than deliver scripted remarks. That next version is bots that don’t just observe meetings but act in them: approving budgets, committing to roadmap dates. When that happens, governance matters in ways the current conversation hasn’t caught up to.

The industry answer is “Governance as Code” — an immutable audit trail proving a decision was recorded. Necessary, but not sufficient. An audit trail tells you what happened. It doesn’t tell you whether the decision was structurally valid.

The Growth Wise model addresses this through Arena adherence. A bot approving a budget during a Decision Forum where that function was invited is doing legitimate work. A bot forcing a roadmap commitment during an Ideation Session — an Arena whose rules suppress convergence — is structurally invalid. Same action, different Arena, different legitimacy.

There’s a second distinction worth making: whether the bot is exercising judgment or transmitting a decision already made. “Budget approved per policy X” is enforcement, not judgment. The governance question for each is different, and conflating them produces frameworks that are either too restrictive or too permissive to be useful.

And then there’s the alignment gap. A bot can achieve action closure perfectly — log the decision, assign the owner, record the timestamp. It cannot achieve aligned closure. It cannot verify that the humans in the room understood the trade-offs. Any governance framework that doesn’t flag that distinction is measuring the wrong thing.

The question that actually needs answering

Yuan’s avatar opened an earnings call. Klarna’s CEO addressed investors through a digital proxy. These are Status Syncs — prepared remarks, one-directional, no coordination required. The bot belongs there.

Organizations that understand that distinction will delegate the right meetings, protect the right meetings, and build governance that knows the difference between an Arena-valid bot action and one that broke the coordination contract. Their decisions will stick.

Organizations that treat all meetings as interchangeable will send bots into Decision Forums and spend Thursday re-litigating what the avatar attended on Tuesday.

The question was never “should we send bots to meetings?” Yuan and Klarna already answered that. The question is which meetings are structurally safe for delegation — and when bots do act, whether they respected the Arena they were operating in.

Read the full article

Most leaders assume teams get better at working together over time. Experience builds intuition. Repetition builds cohesion. Eventually you get something greater than the sum of individual parts.

Research from the Collective Intelligence Labs at the Stockholm School of Economics says that’s wrong. And the failure mode it creates is worse than most people realize.

Researchers Philip Runsten and Andreas Werr studied 50 knowledge-intensive teams across 22 organizations over two months. Half got a structured digital debrief designed to make their coordination visible. The other half just kept working as normal.

The control teams didn’t hold steady. They declined. Observer-rated performance dropped roughly 6.5%. Self-rated performance dropped 4.6%. No disruption, no crisis. Just the quiet erosion of coordination quality that nobody was watching.

The intervention teams improved by 15-21%.

The default trajectory for a team left to its own devices isn’t improvement through experience. It’s decay through inattention.

That’s already uncomfortable. But here’s the part that really got me.

When the researchers segmented teams by performance, a specific cluster emerged: teams ranked just below the top. The “second best.” When the intervention came in and made their coordination visible, they didn’t improve. They got worse. And they resisted the whole process.

Their profile before the intervention told the story. Weaker task understanding than the top performers. Lower integration behaviors — less peer coaching, fewer real challenges to each other’s thinking. Lower psychological safety. And they consistently rated their own performance higher than outside observers did, by a margin that didn’t budge before or after the intervention.

One team member summarized it: “My strongest impression is that we as a team already are very good at many of the things this study wants to demonstrate, which is positive!”

Another said the intervention “created some discontent and irritation.”

The self-overrating gap — 0.36 units between self-assessment and observer rating — was exactly the same before and after. The feedback landed and changed nothing.

Compare that to the top-performing teams, who actually rated themselves lower than observers did. The best teams were humble about their coordination. The second-best teams were confident about theirs. The confidence was unfounded.

This is the catch-22. The teams most at risk are the ones least likely to seek help, least likely to accept feedback, and most likely to believe they don’t have a problem. They’re performing well enough that no one intervenes. They’re confident enough that they resist when someone does.

And they’re succeeding on familiar terrain. Output looks fine because the tasks are within range — routine enough that individual competence carries the performance, even as the actual quality of collaboration thins out underneath.

The moment the task becomes genuinely ambiguous — a novel strategic decision, a cross-functional tradeoff with no clear precedent, a situation where no single person holds enough context — those teams have nothing to fall back on. The integration behaviors were never practiced. The psychological safety to say “I don’t understand” or “I disagree” was never built.

They’ll walk into a high-stakes moment with the same confidence they’ve always had. And the coordination was never there.

This is how organizations produce decisions that, in retrospect, seem inexplicable. Not from obviously dysfunctional teams. From capable teams whose coordination was never made visible.

One more finding worth sitting with: the structured digital debrief achieved performance improvements comparable to facilitator-led interventions that typically show 20-25% gains.

You don’t need a consultant in the room. You need a reliable mechanism for making coordination visible to the team itself.

Read the full article

Reference

Runsten, P. and Werr, A. (2020). Knowledge Integration and Team Performance — The Effect of a Digitally Supported Team Debrief. Collective Intelligence Labs, Stockholm School of Economics.

You’ve been in that meeting. The one where something actually happened. Nobody was performing. Nobody was waiting for their turn to talk. Ideas were building on each other in real time.

What group flow actually is

Researchers define it as an emergent property of a social system, when members are so in sync that the group operates as a single unit and produces outcomes that exceed what any individual could have reached alone. Mihaly Csikszentmihalyi first mapped the individual version: total absorption, loss of self-consciousness, time distortion, intrinsic reward. Keith Sawyer extended it to groups, studying jazz ensembles, improv theater, and business teams to identify exactly what makes the collective state possible.

Sawyer identified ten specific conditions that have to be present for a group to enter that state:

1. The Paradox of the Group Goal: A compelling shared mission, open-ended enough for emergent creativity to surface

2. Deep Listening and Presence: Responding spontaneously to what’s actually being said, not rehearsing your next point while others speak

3. Complete Concentration and Environmental Boundaries: A clear boundary, physical or virtual, that eliminates distraction and shifts the group into a performance state

4. The Autonomy and Control Paradox: Each member feels individual agency while remaining willing to surrender to the collective direction

5. Blending Egos into a Group Mind: No one needs to be the center of attention; ideas build as if the group is a single unit

6. Equilibrium of Skills and Equal Participation: Everyone contributes their expertise at the moment it’s most relevant; no one is spectating

7. Familiarity and the Common Language: A shared knowledge base and unspoken communication style that reduce friction and speed up decision-making

8. Constant Communication and Immediate Feedback: Synchronous, spontaneous, real-time exchange that lets the group course-correct without breaking momentum

9. Momentum through the “Yes, And” Principle: Every contribution gets accepted and built on; negating ideas kills collective momentum

10. The Necessity of Potential Failure: Shared skin in the game — mental, social, or physical risk — creates the high-stakes environment that triggers intense focus

The neuroscience backs it up. When groups achieve genuine flow, their brains literally synchronize, with measurable alignment in neural oscillations, particularly in the beta and gamma frequency bands. Inter-brain synchrony peaks during collaboration and drops during conflict or stress. When jazz musicians improvise together, synchronization across temporal, parietal, and occipital regions increases significantly. The group is not a metaphor. It’s a measurable biological state.

How meetings kill every condition that makes it possible

Every single condition required for group flow is systematically dismantled by the way most organizations run meetings.

Sawyer’s triggers require deep presence: nobody writing their next point while someone else is still talking. Status meetings reward prepared remarks, polished updates, the performance of having things under control. Group flow requires ego dissolution, the willingness to let the group’s direction supersede your individual agenda. Most meetings are structured around individual agendas. Group flow requires equal participation and shared risk. Most meetings concentrate both speaking and accountability at the top of the hierarchy.

Why the system stays broken

The research on why this happens is not flattering. Deci and Ryan’s Self-Determination Theory identifies three non-negotiable human needs: competence, autonomy, and relatedness.

• When deep work is impossible because the day is fragmented, people can’t demonstrate competence through output, so they perform it in meetings instead.

• When calendars are controlled by other people’s invitations, calling a meeting becomes one of the few available acts of autonomy.

• When teams are too large for genuine collaboration, being in the room becomes a proxy for belonging, and being excluded triggers the same threat response as social exclusion.

A McKinsey survey of 1,200 executives found that fewer than half report their decisions are timely, and 61% say at least half the time they spend making decisions is ineffective. Most organizations never fix this because the people with the authority to change the system are the same people whose needs it serves.

Atlassian surveyed 5000 knowledge workers and found even more compelling evidence of the complex:

• Meetings are ineffective 72% of the time

• 78% of people we surveyed say they’re expected to attend so many meetings, it’s hard to get their work done.

• 77% of meetings end with a decision to schedule another meeting

• 54% of workers leave meetings without knowing what to do next or who owns the tasks

• 75% find meetings ineffective for making collaborative decisions

• 72% find meetings ineffective for creating goal clarity
  
  

  wrote a great piece on this recently.

Seeing The System

The Meeting-Industrial Complex

In 2018, Microsoft faced a crisis: surveys showed that entire groups of high-performing employees in their Surface and xBox divisions were miserable. These employees had specialized skills and would be hard to replace if they started quitting. Microsoft was finally making headway in hardware and the wrong employees leaving risked undoing their progress…

Read more

3 months ago · 4 likes · 4 comments · Ryan Moser

The direct conflict

Group flow requires the psychological safety to say “I don’t know,” to build on someone else’s idea rather than compete with it, to fail in front of the group without career consequence. The meetings industrial complex makes those things feel dangerous. You don’t lose self-consciousness in a room where your competence is under constant evaluation. You don’t listen deeply when you’re calculating whether speaking up will help or hurt you.

We’ve known for decades what genuine collective thinking requires. We’ve built organizational systems almost perfectly calibrated to prevent it.

Meetings stay broken because the people who could fix them don’t experience them as broken. And group flow stays rare because we keep trying to get it from a room designed for something else entirely.

Sources: Csikszentmihalyi, Flow; Sawyer, Group Genius; Deci & Ryan, Self-Determination Theory; McKinsey time management research

In 1865, William Stanley Jevons noticed something uncomfortable about steam engines: as they got more efficient at burning coal, total coal consumption went up. The cheaper something is to use, the more of it gets consumed. Efficiency doesn’t reduce demand. It unlocks it.

AI is doing this to cognitive work right now.

The productivity story is real. Drafts that took hours take minutes. Analysis that needed a team now needs a prompt. Execution costs are collapsing. But Jevons predicts what comes next: when doing becomes cheap, you don’t do less. You do dramatically more. When a document takes five minutes instead of fifty, you don’t produce one and reclaim the time. You produce ten. When analysis costs nothing, you run twenty.

Every new output creates a decision that didn’t exist before.

A Harvard Business Review study tracked an eight-month period at a 200-person tech company after they rolled out AI tools. Workers weren’t asked to do more. They just did more — because doing more felt possible. Product managers started writing code. Researchers took on engineering tasks. Individuals absorbed work that would have previously justified extra headcount.

One engineer summed it up: “You had thought that maybe, because you could be more productive with AI, you save some time, you work less. But then really, you don’t work less. You just work the same amount or even more.”

The workday doesn’t get shorter. It gets denser.

AI didn’t reduce cognitive load. It moved it from execution to evaluation, and from producing to deciding. The Harness-Uplevel study found developers using AI tools complete 21% more tasks, but code review times jump 91%. The work didn’t go away. It migrated to the approval layer. Senior engineers now spend more time vetting AI-generated code than they spent writing code before. And the natural pauses in the day — formatting a doc, waiting for a file to load — were also cognitive resets. When AI eliminates the pauses, what’s left is an unbroken sequence of decisions.

And here’s what compounds that: when more things run in parallel, coordination breaks down quietly. Agreements go unrestated. Ownership stays implicit. Context shifts between conversations and nobody marks the change. No one decides to create confusion, it just accumulates. I call this coordination entropy, and it exists in every organization. The difference is pace. When execution was slow, small ambiguities got caught before they spread. A vague decision affected one project. An unclear ownership assumption surfaced in a week. AI removes that buffer. Now the same ambiguity scales across twelve workstreams before anyone notices.

The part that frustrates me: none of this makes meetings less important. It makes them more important. Meetings are where coordination actually happens — where someone finally says “wait, I thought we decided X” and it either gets resolved or gets buried. That signal doesn’t live in Slack, in the task tracker, or in the doc. But most meetings aren’t built for deciding. They’re built for sharing. Status updates, decks, information transfers. As if getting everyone informed is still the hard part.It isn’t.

The hard part now is getting a group of people to evaluate what AI produced, decide what to do with it, and actually close on something. And we are, broadly speaking, terrible at that. Sensemaking as a group is hard. Holding a process while also participating in it is hard.

And the meetings industrial complex has made it worse — turning these moments where real value should get generated into performance. Another hour where everyone demonstrates their presence and nothing actually closes.

Work is going to get more complex, more uncertain, and more cognitively demanding. The HBR researchers call it a “self-reinforcing cycle” — AI accelerates tasks, which raises expectations for speed, which makes workers more reliant on AI, which widens the scope of what they attempt, which expands the quantity and density of work. Nobody mandated it. It just happened.

Structural complexity from AI is coming regardless. Coordination entropy isn’t. That part is a choice — but only for organizations that treat their coordination layer as something worth designing, not just scheduling.

The cost of doing is collapsing. The cost of deciding is just getting started.

This is the problem Growth Wise is built to instrument. Full piece at the link.

Read the full article

I’ve spent 15 years as a GTM and marketing strategist for startup and scaleup SaaS. And for 15 years I’ve encouraged founders to have a bold point of view — something that resonates with some people and alienates others. A position, not a positioning statement.

Now I am that founder, and I need to take my own advice.

With the caveat that I’m always open to changing my mind. This is where I stand today.

The cost of execution is approaching zero. The cost of knowledge is already there. Everyone will soon have the same facts, the same procedural know-how, the same ability to ship fast. Execution efficiency becomes table stakes. What can’t be commoditized is judgment: the ability to look at genuinely ambiguous situations, weigh competing perspectives, and decide.

That’s where humans still have a real shot. But only if we’re willing to do the hard part.

Dave Snowden’s Cynefin framework distinguishes between problems that are complicated and problems that are complex. Complicated problems have right answers, they just require expertise to find them. AI is genuinely good here. Complex problems are different. Cause and effect can only be understood in retrospect. There are no right answers, only bets. The right management approach is to probe, sense, and respond: run experiments, read what emerges, and adapt. This is the domain where most consequential organizational decisions live, and it’s the domain where most teams are least equipped to operate.

When the problem is uncertain, the tradeoffs are real, and the path forward requires actual perspective-taking and debate, AI struggles. It lacks what philosopher and cognitive scientist John Vervaeke calls perspectival knowing and participatory knowing: ways of understanding the world anchored in having a body, a history, a stake in the outcome. They’re social in nature. You can’t train them out of a corpus.

What makes complex problems hard isn’t a lack of information. It’s that getting to a decision requires groups to move through what facilitator Sam Kaner calls the Groan Zone: the uncomfortable stretch between divergent thinking (lots of ideas, competing frames, no consensus) and convergent thinking (shared understanding, a direction, a decision). It’s messy. People get confused and impatient. Conversations seem to go in circles. Someone usually tries to short-circuit it by calling for a vote, or defaulting to HiPPO (highest paid person’s opinion) in the room.

Most groups will do almost anything to avoid the Groan Zone. Anything that lets them feel like progress happened without sitting in the discomfort of not yet knowing.

The conditions for navigating that discomfort are getting worse, not better.

More white-collar work is now done remotely. And while remote work gets framed as a coordination problem, the deeper loss is perceptual. In a room, you’re reading the whole space. Who shifts in their seat when a topic comes up. Who makes eye contact with whom. The pause before someone speaks. Video strips most of that out. You get a grid of faces, degraded audio, and none of the spatial and temporal cues that tell a group how the room is actually feeling or who’s about to speak. The brain works harder to compensate, and still misses things.

At the same time, the teams most likely to produce something genuinely new are diverse ones. Diverse teams make better decisions than homogeneous ones. They also find it harder to reach consensus. More friction, more misreading, more moments where different communication norms create confusion. The Groan Zone is longer and more treacherous when the people in the room don’t share context.

So we’re asking more of our collaboration — more complexity, more diversity, more distributed — while giving it less to work with. Remote removes the cues. Diversity increases the friction. And the communication skills required to navigate all of it are getting weaker.

So we schedule another meeting. And another. Not because we’re inefficient —because the work is genuinely hard and we haven’t finished it yet. Add the politics layered on top: the very human instinct to avoid being the person who made the wrong call, to stay aligned with the group, to not be the one holding up the decision.

Uncertainty is uncomfortable. Social exclusion is worse.

The meeting becomes a place to keep moving without committing, to feel like you’re still in the room. Communication has eclipsed creativity as the central skill of white-collar work — and we did it to ourselves. No wonder we’re drowning in alignment meetings that don’t actually align anything.

Fewer meetings is part of the answer. The bigger part is making the ones we keep count for far more than they currently do. Right now most of them leave no legible record of what actually happened.

Meetings multiply because decisions don’t close. When a decision doesn’t close, the same conversation reconvenes. When ownership isn’t assigned, people keep gathering to re-establish it. Often what’s really happening is that the group hit the Groan Zone and retreated — scheduled another meeting rather than push through to something real. The calendar is downstream of that failure, not the cause of it.

The meeting is where negotiation actually happens. Where someone says “wait, I thought we were doing X” and it either gets resolved or gets papered over. Where accountability is accepted in real time or quietly avoided. Where the group’s actual coordination logic becomes visible. That signal doesn’t exist anywhere else. Not in the Slack thread, not in the task tracker, not in the shared Google doc.

That’s what I’m building as founder Growth Wise. I’m treating meetings as the diagnostic signal they actually are.

I hold this view for a reason that has nothing to do with research.

I’ve spent most of my career being hired to drive growth with virtually no resources, bringing to market products no one had seen before, from brands no one had heard of. With cash runway of maybe six to twelve months. Sometimes less. Whatever choice you made had to work, because there wasn’t a second one.

In those conditions you can’t relitigate decisions. You can’t ask for more data. You can’t wait for full alignment before moving. You have to get a room full of people — many of whom never saw the problem as theirs to solve, engineers who had never thought about go-to-market, product managers who figured sales was someone else’s department — and somehow get them to think together, decide with conviction, and move.

That was never a meeting problem, it was a coordination problem under fire.

The best solutions I’ve ever been part of came from groups that managed to do exactly that. Not because they had more information or more time, but because they got into the Groan Zone together and came out the other side with something real. An idea nobody walked in with. A decision everyone actually owned.

We did it in the time and with the resources most large companies would spend trying to align everyone’s calendar for the meeting and ordering the snacks.

That’s fifteen years of being taught, expensively, that the quality of how a group thinks together is not a soft skill. It’s the variable that determines whether a company survives long enough to find out if it had a good idea.

You know what observability is.

Logs, metrics, traces. You build it into every system you ship because without it you’re flying blind — you find out something broke when someone files a ticket, not when the error rate spikes at 2am. The whole point is to make failure patterns visible before they cascade.

Now think about how your organization runs its decision layer.

Every week, dozens of cross-functional decisions happen across your teams. Scope gets defined, trade-offs get negotiated, commitments get made. Some of those decisions close cleanly: explicit owner, captured rationale, everyone aligned. A lot of them don’t. The meeting ends, people leave with different interpretations of what was agreed, and two weeks later you’re debugging a scope conflict that was baked in at the kickoff.

You have no instrumentation for any of this. You can see that the meetings happened. You can’t see whether the decisions in them actually closed.

The same failure modes. Different layer.

The patterns that show up when you have no visibility into your coordination layer are familiar. Decisions get relitigated across multiple meetings because nobody explicitly closed them the first time. Scope creep accumulates because an assumption changed but nobody propagated the update. Two teams build in conflicting directions because each thought the other was adapting to the decision made three weeks ago.

These are structural failure modes — the kind you’d recognize immediately in a distributed system. Ambiguous state. Missing acknowledgments. Divergent replicas.

In SRE terms: you have observability on your compute layer, observability on your storage layer, and zero observability on the coordination layer that connects your people to your strategy.

What instrumentation on the coordination layer actually measures

The question it answers is not “are people meeting?” It’s “are the decisions that come out of those meetings actually holding?”

Did the decision close with an explicit statement of what was decided? Does it have a named owner? Was dissent surfaced in the room or suppressed? Is the rationale captured well enough that someone who wasn’t in the meeting can reconstruct why?

These are the signals that determine whether a decision propagates cleanly through the organization or unravels the first time a dependency shifts. When decisions consistently fail on one or more of those signals, you get decision churn — the same questions relitigated across meeting after meeting, alignment that looks achieved but dissolves under pressure.

The missing layer

There are tools for tracking what your teams are building. Tools for how they communicate. Tools for who made which calls and when. None of them answer whether the decisions that created those tasks actually closed, or whether the same scope question is cycling for the third time.

Decision Reliability Infrastructure is the layer that sits between your communication tooling and your execution tooling — making visible the coordination work that happens in between.

You wouldn’t ship production code without instrumenting it.

→ Read the full piece

Their team lead had already stepped in. They wrote: “I’m also not entirely sure that this is what I imagined a senior engineer role to be like. I expected to stay in my lane — backend engineering — but just do more of it.”

The top comment got 77 upvotes. It named the thing clearly:

“The DRI concept for engineers is gaining more and more traction in the industry… For work that is small to large, you assign a mature engineer — ideally someone who has shown a track record of successfully coordinating and delivering on smaller things — as a DRI to take the role of a mini-TPM.”

Mini-TPM. There it is. That’s what the DRI role actually is, and most orgs never say that out loud when they hand you the title.

A TPM’s job is coordination: driving cross-team decisions to closure, tracking dependencies, managing escalation paths, holding alignment across teams that report to different people and have competing incentives. When an engineer is assigned as DRI on a cross-functional feature, they’re being asked to do that job with borrowed authority and no dedicated role.

The engineer in that Reddit post said explicitly that they had been doing the technical work successfully. What was hard was the coordination surface: estimates across teams they didn’t manage, progress tracking for work they weren’t doing, alignment across codebases moving at different speeds on different teams’ schedules.

That is the DRI job. The technical contribution is real, but it’s not the primary accountability.

The commenter was honest about why the role exists at all:

“Because you can’t assign a TPM, PM or EM on every single initiative that’s going on. You’ll run into a bottleneck.”

The engineering DRI is a resource constraint dressed up as a role definition. Most large engineering orgs have more cross-functional initiatives running than they have coordination capacity to staff with dedicated program managers. So they push coordination accountability down to engineers on the initiatives that don’t justify a full-time PM, give those engineers some borrowed authority from whoever put the initiative on the priority list, and trust that a “mature engineer” can figure out the rest.

The commenter, who was skeptical: “I think it’s also a clear indicator that you’re doing too many projects in parallel as an org.”

The DRI absorbs the coordination tax that would otherwise be visible on a TPM’s calendar.

What does coordination actually require in a role like this? The commenter put it in a specific order: “You coordinate, delegate, and contribute. In that order.”

The sequencing is the thing. Contribution — the actual technical work — comes last. Coordination comes first. For an engineer who expected more senior technical work, this inversion is what creates the identity dissonance. You were told you’d be doing harder engineering. You’re doing a different job.

In practice, coordination means making sure decisions actually close in the meetings where they’re discussed. Not just discussed. Closed. An explicit statement of what was decided, a named owner from each affected team, the rationale captured well enough that someone who wasn’t in the room can understand it without a fifteen-minute briefing.

Most DRI failures I see aren’t caused by the DRI being incompetent. They’re caused by the meetings failing before the work even starts. The kickoff. The first alignment sessions. The early cross-team syncs. These are where scope interpretations diverge, where concerns get acknowledged but not resolved, where alignment looks achieved but isn’t structurally complete.

A decision gets discussed. The group appears to converge. No one explicitly states what was decided. No one names who owns the next step on the iOS side versus the backend side. The dissent — “I think this approach will cause problems with the migration timeline” — gets noted, the meeting moves on, and two weeks later the iOS team builds in a direction that conflicts with what the backend team assumed was agreed.

The DRI is now debugging a scope conflict that was baked into the kickoff before they ever wrote a line of code.

The coordination mechanics that prevent this are specific and learnable:

An explicit decision statement before the meeting ends — not just what was discussed, what was decided. Named owners for every action across every team, including ones the DRI doesn’t manage. Surfaced dissent inside the meeting rather than through back channels afterward, because a concern raised after the meeting dissolves becomes a relitigated decision rather than a resolved one. Captured rationale, documented well enough that someone who missed the meeting can understand why the decision went the way it did.

When these are present, a DRI can hand off context, track dependencies with confidence, escalate blockers with a paper trail. When they’re absent, the DRI spends most of their coordination time reconstructing what was previously agreed. Decision churn — the same scope questions relitigated in meeting after meeting — isn’t a team dynamic problem. It’s a structural one. The decisions that should have closed in the early meetings didn’t close, and everything downstream inherits that instability.

The engineer who posted asked whether what they were experiencing was normal.

The answer is yes. And the normality is the problem.

Being assigned coordination accountability for a six-team feature without explicit preparation for the coordination work is a common pattern. Discovering mid-delivery that the role is fundamentally different from what was implied is common. The commenter who explained the role best was also honest about its limits: “It’s a lot harder than it looks to be a good DRI. People should be able to volunteer as DRIs, but it’s not a viable process in my experience.”

What the DRI role reveals, when you look at how it actually operates, is where the organization’s coordination capacity runs out. When a DRI succeeds, the initiative was scoped right, the DRI had relevant coordination experience, and the early meetings produced structurally complete decisions. When a DRI fails, at least one of those three things was missing.

The first two are visible on a spreadsheet. The third one — whether the decisions that came out of the kickoff actually closed, or just looked like they did — is almost never instrumented. Which is why DRI failures tend to get categorized as people problems rather than structural ones.

They’re not.

Every ops leader I’ve seen try to fix meetings follows the same path.

First they add transcription. Otter, Fireflies, Fathom, etc. Now there’s a record of what was said. The problem doesn’t go away.

So they add action tracking. Fellow, Grain, Tactiq. Now there are documented next steps. The problem still doesn’t go away.

Some go further and pull in calendar analytics. Viva Insights, Worklytics, Clockwise. Now they can see how overloaded people are. The problem still… you know where this is going.

Here’s what’s actually happening. The meeting analytics market operates across four distinct layers, and most organizations stop at layers one or two. Not because the tools are bad. Because they’re solving the wrong problem.

• Layer 1 answers: what was said.

• Layer 2 answers: what should we do next.

• Layer 3 answers: how much time are we spending.

• Layer 4 answers: what’s structurally going wrong.

Almost every ops team I’ve watched is buying Layer 1 and 2 tools to solve a Layer 4 problem. That’s why nothing changes.

The structural problem — the one that actually explains why teams have the same conversation twice, why decisions collapse in execution, why alignment reached in a meeting dissolves by Thursday — isn’t captured in a transcript. It’s not in the action items and it’s not in the calendar data.

It’s in the coordination layer. Whether decisions actually closed. Whether the “agreement” in the room was genuine or just the least-confrontational path to ending the meeting. Whether the person who said “I’m aligned” had any intention of following through, or just didn’t want to relitigate.

A transcript cannot tell you that. A task tracker cannot tell you that. A calendar heatmap definitely cannot tell you that.

The gap isn’t tool failure. It’s architectural. The meeting problem isn’t that people forget what was discussed — that’s a Layer 1 problem, and it’s mostly solved. The meeting problem is that nothing gets decided. Or something gets “decided” but doesn’t hold. Or the same conflict surfaces three meetings later because no one actually resolved it the first time.

That’s a structural failure. It requires structural instrumentation.

Layer 4 doesn’t replace the other layers. Transcription is table stakes. Action tracking matters. Calendar analytics can tell you when a team is drowning. But none of them show you where the coordination broke down — which conversation never closed, which decision had no owner, which moment of false agreement set up three weeks of rework.

That’s the layer most organizations are missing. And it’s the one that explains why adding another tool hasn’t fixed anything.

I wrote a full breakdown comparing the tools across all four layers, which ones do what, where they fall short, and what to look for if you're actually trying to fix the coordination problem.

We invest in bottom-of-funnel performance marketing because it’s worked before. It’s predictable, reliable. It feeds the funnel and keeps sales moving.

But over time, something subtle (and expensive) starts to happen.

To keep hitting lead volume targets under rising CPA thresholds, marketing casts a wider net.

🕸️ 1. That net brings in more noise

👥 2. SDR headcount increases to qualify it

🤝 3. Sales starts chasing - often smaller - transactional deals, not strategic ones

🧯 4. CS gets flooded with more customers - many of whom were never the right fit.

And then there’s 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝗳𝗮𝘁𝗶𝗴𝘂𝗲.

👀 Audiences become numb.

🚫 Banner blindness sets in.

🎨 Now you need more creative. Better creative.

💸 Higher production costs, tighter turnaround cycles, and growing pressure on already-stretched teams.

📣 𝗔𝗻𝗱 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁 𝗴𝗲𝘁𝘀 𝗲𝘃𝗲𝗻 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝗱:

Functional silos and misaligned KPIs 𝗮𝗺𝗽𝗹𝗶𝗳𝘆 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.

Even when marketing improves lead quality, reducing noise and improving conversion rates, sales might not care.

Because when they’re behind on targets, the only thing that matters is volume.

Booked meetings. Pipeline at any cost.

Turning the tap harder always feels safer than questioning the faucet itself.

- So marketing tries to optimize signal.

- Sales optimizes for speed.

- Customer Success tries to hold everything together.

👉 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗶𝘀 “𝗱𝗼𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝗷𝗼𝗯”—𝗮𝗻𝗱 𝘀𝘁𝗶𝗹𝗹, 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘀𝘁𝗮𝗿𝘁𝘀 𝘁𝗼 𝗯𝗿𝗲𝗮𝗸.

It 𝑠𝑡𝑖𝑙𝑙 𝑤𝑜𝑟𝑘𝑠… but the cost of that “working” is a bloated, overstretched growth engine with declining strategic clarity.

The worst part?

📈 Because lead volume and revenue are still growing, no one wants to question the tactic.

But here’s the uncomfortable truth:

𝗝𝘂𝘀𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗲𝗮𝗻 𝗶𝘁’𝘀 𝘀𝘁𝗶𝗹𝗹 𝘃𝗮𝗹𝗶𝗱.

This is the difference between:

• 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: “This tactic has worked before.”

• 𝗩𝗮𝗹𝗶𝗱𝗶𝘁𝘆: “This tactic is still the right one for how the world works now.”

And the bridge between the two?

🧠 𝗔𝗯𝗱𝘂𝗰𝘁𝗶𝘃𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴.

The ability to step back, ask 𝑤ℎ𝑦 something worked, and whether the conditions that made it effective still hold true.

You don’t have to abandon performance marketing, but you 𝑑𝑜 need to challenge the assumptions it was built on.

But over time, the pressure to keep it working can quietly erode both your margins, and your team’s ability to focus on what really matters

Have you seen this play out in your org?

👇 I’d love to hear your experience.

Seriously.

There’s a myth that the best ideas come from tight-knit teams who’ve worked together forever.

It feels safe. Efficient. Harmonious.

But if you’re only surrounding yourself with familiar faces and trusted collaborators…

You’re not building an innovative team.

You’re building an echo chamber.

Research on 4M+ patents and 23M+ publications found:

The most creative, high-impact work comes from teams with weak ties. People who haven’t worked together before, who come from different disciplines, and who think differently.

In open-source dev?

It’s the casual collaborators, not the inner circle, who spark the most novel ideas.

What are weak ties?

They’re:

• The colleague you rarely loop in

• The voice outside your function or field

• The person who makes you rethink your assumptions

• The one who makes you a little uncomfortable

And that’s the point.

Because discomfort isn’t the enemy of innovation.

Comfort is.

Weak ties bring creative tension.

But tension alone won’t get you there.

Psychological safety is what turns tension into breakthrough.

It’s the container that allows radically different minds to:

✨ Trust each other

✨ Speak up

✨ Take risks

✨ Co-create something new

At Growth Wise, this is our jam.

We help teams lean into difference.

Not just with theory, but with real tools to help navigate hard conversations, find shared ground, and stay connected when it gets messy.

So here’s your invitation:

If you’re building something bold:

✖ Don’t default to sameness

✖ Don’t seek comfort

✅ Seek difference.

✅ Create safety.

✅ And watch what becomes possible when brave minds collide, with respect.

Join the waitlist: growthwise.team

Sources:

On weak ties & impact

On open-source collaboration

That means strong collaboration skills are going to be a huge differentiator for anyone who wants to grow their career.

The real question is: can we make all this teaming actually feel good?

Like, can intense collaboration actually be fulfilling, and not trigger existential dread over egos, overtalkers, or the one teammate who mysteriously vanishes when shit hits the fan?

Maybe it’s time to retire the term “IC” - individual contributor.

Feels like the future belongs to “TC” - Team Contributors

• Product development is about building new core value.

• It asks: what should we make next?

• It leans on frameworks like design thinking, lean startup, agile, all that good stuff.

• It solves problems by creating new things.

GROWTH IS DIFFERENT....

• Growth is about connecting people to the value that already exists.

• It asks: how do we get more of the right people to experience this thing we've already built?

• It works within the constraints of what’s already live—product, brand, team dynamics, timelines.

• It solves problems by shifting messaging, positioning, pricing, timing, UI/UX, onboarding, sales materials, email automations... sometimes all of the above. Sometimes none of the above. It’s messy.

• It’s also way harder to define and often harder to execute.

• You can’t just A/B test your way to clarity. It’s not clean. It’s not predictable.

• It’s about making smart bets in complex, ever-changing environments and learning fast with limited resources.