Ever since I first published the Product Management Triangle in 2014, people have asked for versions they can use for their own purposes.

I finally made this a lot easier! I updated the triangle and made it available as a Keynote template. Click here to download it. Edit the diagram however you want. I’d love to see what you do with it.

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And here’s an expanded version that shows things that are both internal and external to your company.

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Technology entrepreneurs like to say that they’re “making the world a better place” but they usually have little idea about what will happen if their creations are adopted. They often can’t even draw a clear line from what they’re doing to a better world.

This phenomenon was mocked beautifully in one of my favorite sequences from HBO’s Silicon Valley. In that episode, startup founders make claims like these:

  • “We’re making the world a better place through paxos algorithms for consensus protocols.”
  • “We’re making the world a better place though software defined data centers for cloud computing.”
  • “We’re making the world a better place through canonical data models to communicate between end points.”
  • “We’re making the world a better place through scalable, fault tolerant distributed databases with acid transactions.”

The sequence exposes the bullshit nature of many startup visions.

Even when you do have plausible narrative for achieving a big goal, your vision drifts from reality when your tech hits the real world. Your customers will use your product in unforeseen ways. The second- and third-order social impacts are impossible to predict. The creators of online social networks didn’t anticipate Trump.

To make an ambitious vision come true, you must perpetually adjust the path to your vision based on how the world is responding to your tech.

This is relatively easy for small startups. The same people with the vision are the ones doing the front-line work. If something unexpected happens, small startups can adjust plans with minimal communication. That said, many startups will quickly forget their original visions when they see a different path to survival (which can either be good or bad).

For larger companies, maintaining a narrative that connects their vision with reality is much harder. Without self-awareness, a company can start doings lots of things that have nothing to do with their intentions.

To illustrate this point, consider this diagram:

Innovation Accountability Infographic@2x (1)

Imagine that each cube represents a unit of engineering work. In aggregate, the cubes are the things your company shipped in a given week.

At a big company, only a small number of people are able to understand the contents of each particular cube. Their trail of cryptic code commit notifications does little to explain the work.

To help “manage” all the work being done, companies install project management tools and practices. Each blue box in the diagram represents a set of engineering work captured in the form of issues, stories, bugs, or features.

Project management tools make the engineering work more intelligible to the people working on each project. But just because something is represented in project management software doesn’t mean it maps to a company goal. Project management software, alone, helps you be a better feature factory, but it doesn’t keep you in sync with your vision.

I’m building Double-Loop to help companies achieve, as the top part of the diagram says, complete alignment across strategy, project management, and engineering.

My vision with Double-Loop is to capture every unit of engineering work that your company produces through integrations with tools like GitHub. Each unit of engineering is like a transaction your company makes with the world. And, like transactions in the financial sense, they should be accounted for and reconciled with your company goals. You should know how much you’re “spending” on each goal and the return on investment.

With Double-Loop, as new engineering work is deployed, you’re prompted to map it to your company goals and capture the results achieved after launch. When something doesn’t reconcile easily, it means either your goals need to shift or the work needs to cease.

Today, most companies don’t know how much of their output maps or doesn’t map to their goals. When a company’s work is invisible to the folks steering the ship, it means they’ve created a monster with potential to wreak havoc on the world around them.


Baby Learns How To Grab 2

Babies are born with exogenous attention.  This means that the external world dictates what they pay attention to. A baby could be playing with the best toy ever, but when another toy drops next to them, their attention uncontrollably shifts to the new shiny object. In The Philosophical Baby, Alison Gopnik says that babies can “become captivated by interesting things that they don’t care for, like an unusually bright light or loud noise. They cry and fuss but seem unable to look away, like adults watching a horror movie.”

Gopnik explains that as children grow older, they develop endogenous attention, the ability to control their own attention. They become able to keep their focus on a ball even if a gorilla walks into the room. Or they can choose to give up the beloved ball, if they are persuaded through bribery, threat, or some other measure.

Maintaining endogenous attention is critical in all challenging jobs. A pilot must know where to focus their attention even if a loud alarm is going off in the cock pit. In bounded domains, there is a clear set of rules for where to focus. A pilot is trained where to look for potential danger.

But in unbounded domains where there is no rule book, learning how to control your own attention is an endeavor in itself. When your playing field has no clear parameters and the future is ripe with surprises, the allure of responding reactively to your environment is especially strong.

Kids make cognitive development look easy. Adults, when navigating unbounded domains, must work hard to develop the brain functions they take for granted in other aspects of their life.

Many of companies behave more like children than adults. They chase shiny objects and squirrels instead of staying focused on creating differentiated value. Maintaining your company’s ability to control its own attention, I believe, is a chief meta-responsibility of product management. And the obstacles in doing so are insidious.

Here are some ways PMs can manage the attention of their organization.

1. Resist shiny objects and squirrels.

Building differentiated value requires relentlessly iterating towards a product that fits customer, business, and technical parameters.  It’s a long grind, and often takes longer than people think.

When company leaders get bored waiting for actual progress, making a shiny object or chasing a squirrel is an endorphin rush and might reduce pressure in the short term.

A shiny object is a feature or prototype that excites executives or investors, but doesn’t deliver actual value. As a PM, I like to make shiny objects to inspire the company around a direction I feel the company should go, even knowing that the shiny object itself is problematic in its form. But if a PM makes shiny objects to gain political points without a larger purpose, they are enabling the baby-like distractibility of their executive team.

Companies must be able to quickly pivot their attention when the situation demands it. However, you’re “chasing a squirrel” when you reach for an opportunity that does not build off your core competency, like signing a big partnership that forces your to create one-off custom work.

If you’re lucky, you’ll miss catching the squirrel. If you do catch it, it will fragment your focus and muddle your position in the marketplace.

2. Manage the tension between changing the world and adapting to the world.

Steering your company clear of obvious distractions is the first step to maintaining endogenous attention, but it gets more nuanced from here.

For good reason, companies aspire to be “customer-driven.” Adapting your product based on user feedback is critical. In this sense, having your attention impacted by your external environment is necessary.

But customer behavior and market dynamics are not immutable. Great products change user behavior and create new markets. By over-reacting to external feedback, you end up building a faster horse instead of a car, as they say.

Product-driven companies listen to market signal, process it, and then build things beyond the imagination of their customers. The key is to process the external input, filter it, and make a bet that reflects comprehensive situational awareness. This is how you can reorganize the world around your product.

3. Earn the confidence to place your own bets.

As I described in The Product Management Triangle, product managers sit at the intersection between business, technology, and customers. Consequently,  PMs can apply “full-triangle” thinking to optimally drive their product forward. Thus, PMs, or other folks with a full understanding of their company, should sit in the drivers seat crafting initiatives.

Yet, a PM’s orientation is shaped by input from their cross-functional partners. PMs rely on their teammates and stakeholders to understand customer, market, and technical conditions.

The easy way for product managers to gain the confidence of their peers is to do what they ask for. PMs often build roadmaps that diplomatically mirror the product requests of their stakeholders.

But stakeholders are less qualified to make product decisions than the PM, or at least it should be that way. Each stakeholder may know more than the PM about a facet of the business, but they know less about how the full puzzle comes together. To build impactful products, product teams must call their own shots.

Consequently, a PM needs to earn confidence, not just in their responsiveness to the needs of their organization, but in their ability to make the best bets. Just as you should build stuff beyond the imagination of your customers, you should transcend the imagination of your stakeholders.

I created Double-Loop, in part, to help PMs earn the confidence of their organization. PMs use Double-Loop to share the narrative of their iteration process. PMs are scholars of how the world reacts to product changes, and Double-Loop expresses that. When stakeholders can follow the process of building a continually improving product, they viscerally feel the expertise of the people driving it. This makes stakeholders less likely to prescribe how the product should change. It gives the product team the freedom to experiment at the cutting edge of their knowledge; that is, operate like an adult with full control of their own attention.

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Extracting every ounce of learning from your actions is critical to solving hard problems. Every time you poke at the world is an opportunity to discover something new about the dynamics of a problem space.

Even for a small team, maximizing learning is hard. It requires discipline to routinely loop back to your previous endeavors to analyze what worked and what didn’t. It’s much easier to leave the past behind and blast forward to the next enticing plan.

For small teams, at least, it’s easier for contributors to remember the outcomes of their previous attempts and share insights. A startup’s ability to learn enables nimble pivots en route to the promised land.

Multi-team companies face larger obstacles in the learning process:

  1. It takes energy for contributors to communicate their learnings widely.
  2. Knowledge doesn’t easily translate across departments or cross-functional roles.
  3. Knowledge walks out the door with attrition.
  4. For new employees, the learning process starts from scratch.

Today, maximizing learning at scale is almost impossible which takes away some advantages that bigger companies should have:

  1. Companies with longer histories should have accumulated more learning to make solving future problems easier.
  2. Companies with more people should be able to create a network effect of knowledge transfer across teams.

The Double-Loop master plan is to remove the obstacles that prevent learning at scale. Here’s how we’ll do it.

Record keeping

The foundation of learning at scale is recording launches and results. Much of the data already exists in project management, deployment, code versioning, and analytics tools. Humans must add context such as strategies, goals, hypotheses, pictures, and results summaries.

Everyone in the company should be able to create, access, search the history of launches and results.


In realtime, every contributor should be able to follow the actions of other contributors that relate to their own work.

At the bare minimum, this can be accomplished by autogenerating high-level summaries, distributed by Slack or email, based on the record of launches and results.

But true learning at scales requires granular notifications. Teams should be able to subscribe to targeted facets of the launch record. For example, for a particular product change, customer support might need to know the details the UI while the sales team is more interested in the impact on the overall value proposition. Similarly, an engineer working on SEO should be able to see what other teams have done in the domain, what’s worked, not worked, etc..

Machine-amplified learning

While record keeping and communication provide the building blocks of learning at scale, there is potential for software to play a new role to amplify memory and learning. Here are a few ideas.

  1. Software can automatically generate a timeline of product launches based on deployments and project management software. Given the trend towards high-frequency, small deployments. Tools are needed to separate the signal from the noise.
  2. Based on the above, algorithms can guide team members to communicate or analyze the most important launches and retrospectively analyze results. Imagine a feed of product changes, consumed by data scientists, ranked by their propensity to impact business metrics.
  3. Software can learn which launches across a big company you care about to create real-time feeds of knowledge cross-pollination.
  4. Based on defined key results, software could (A) automatically classify the success level of product changes based on app analytics and (B) train a system to predict success likelihood in advanced of engineering commitment based on the structure of plans in project management tools.

I believe we’ve only scratched the surface of systematically cultivating learning in the innovation process.

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Innovation requires a combination of work and meta-work. By “work” I mean doing things that tangibly impact your product; designing, coding, marketing, etc.. “Meta-work,” in contrast, involves improving how you’re working. This often looks like meetings to synchronize the activities between teams or applying frameworks like OKRs or lean startup.

Over-emphasizing work, while neglecting meta-work, turns you into a feature factory where you’re producing in high quantity, but the stuff you’re making is of questionable value.

Conversely, spending too much time on meta-work often entails a day full of meetings and bureaucratic processes when no work is actually done. Large companies have no choice but to spend lots of time on meta-work since they need to coordinate the work of many specialized contributors. They must maintain safeguards to prevent the loss of market share already won. This in part explains why big companies are so prone to get disrupted — they don’t have time to actually work.

Growth stage companies, however, are in the predicament of having to consciously decide how to balance work and meta-work.

Relentlessly working without pausing to reflect and communicate might succeed with a small team of founders, but it doesn’t scale to new employees and multi-team orgs. Meta-work is required to prioritize the work necessary for solving future problems that can’t be felt viscerally at the present moment.

But speed matters too. If you don’t “move fast and break things,” it’s harder to sneak up and capture a new market or catch an incumbent player flat-footed.

The tension between work and meta-work, in part, is why we’re seeing a rise of tools focused on meta-work. While there are tons of tools for getting work done, project management tools, dev tools, design tools, etc., there are fewer tools that change how we work. Now we’re seeing a growing market of product management tools that help teams communicate roadmaps or track their progress against OKRs. My project, Double-Loop, helps teams learn and communicate by recording a timeline of hypotheses and results. This is faster than the meta-work task of sending a product launch email or slides.

These tools remove the tension between work and meta-work by making the meta-work more efficient. If you can spend less time getting the benefits of meta-work, you have more time for the actual work. You can optimize for speed and value.

Stand-up bots, like geekbot, are the most literal version of this. Instead of spending the time on synchronous meetings to synchronize, you can synchronize asynchronously; fewer people have to break their flow to attend an in-person meeting. Slack is powerful because it’s a tool for completing the necessities of work with meta-work automation mixed in.

Automating meta-work is a little bit like automating the role of the manager. It’s the manager’s job to preserve team harmony, create accountability, and set strategy. Meta-work tools allow teams to do this bottom-up.

However, elements of meta-work resist automation. As the head of product for a growth stage company, just because I ask my team to use meta-work tools doesn’t mean they actually will. It’s hard to prioritize meta-work tools that can feel like distractions from pressing tasks.

I discovered that meta-work tools, ironically, require meetings to drive usage. I can ask my team to use our new OKR tool, Gtmhub, but no one would use it if we didn’t have a meeting to review the data we enter into the tool. Similarly, my team wasn’t using Double-Loop until we structured meetings around looking at Double-Loop to look at product launches and results.

The designers of meta-work tools should prioritize making screens that are specifically suited to be projected on the wall during meetings. Meetings are the lifeblood of these tools. It’s not the job of meta-work tools to completely kill meetings. However, it is their job to make meetings less frequent and more efficient.

The best meta-work tools minimize duplicate data entry. Meetings are especially costly if you need to spend an hour updating a spreadsheet before the meeting. As much as possible, the data in meta-work tools should be aggregated from the actual work tools.

In Double-Loop, we’re building a timeline of product launches that can be understood outside of the tech team, yet the building blocks of each launch event come from Jira. The team still needs to add new information for context, but I believe the Jira bootstrapping will be key to driving adoption.

Teams, in general, are getting more thoughtful about designing their toolchains, and companies like Segment, Zapier, and Unito make this easier. To take this one step further, I believe companies need to think beyond tools chains and design meeting+tool-chains optimized release the tension between work and meta-work.