In The Unaccountability Machine, Dan Davies argues that organizations form “accountability sinks,” structures that absorb or obscure the consequences of a decision such that no one can be held directly accountable for it. Here’s an example: a higher up at a hospitality company decides to reduce the size of its cleaning staff, because it improves the numbers on a balance sheet somewhere. Later, you are trying to check into a room, but it’s not ready and the clerk can’t tell you when it will be; they can offer a voucher, but what you need is a room. There’s no one to call to complain, no way to communicate back to that distant leader that they’ve scotched your plans. The accountability is swallowed up into a void, lost forever.

Davies proposes that: For an accountability sink to function, it has to break a link; it has to prevent the feedback of the person affected by the decision from affecting the operation of the system.

Another mechanism of accountability sinks is the way in which decisions themselves cascade and lose any sense of their origins. Davies gives the example of the case of Dominion Systems vs Fox News, in which Fox News repeatedly spread false stories about the election. No one at Fox seems to have explicitly made a decision to lie about voting machines; rather, there was an implicit understanding that they had to do whatever it took to keep their audience numbers up.

you could conclude that to be accountable for something you must have the power to change it and understand what you are trying to accomplish when you do. You need both the power and the story of how that power gets used.

an account is something that you tell. How did something happen, what were the conditions that led to it happening, what made the decision seem like a good one at the time? Who were all of the people involved in the decision or event? (It almost never comes down to only one person.)

Things seemed to go well at the beginning. Due to the pandemic and the objective challenges of the traditional Brick & Mortar business, the business operated by Nike Direct (the business unit in charge of DTC) was flying and justifying the important strategic decisions of the CEO. Then, once normality came back, things slowly but regularly, quarter by quarter, showed that the separation line between being ambitious or being wrong was very thin.

In 6 months, hundreds of colleagues were fired and together with them Nike lost a solid process and thousands of years of experience and expertise in running, football, basketball, fitness, training, sportwear, etc., built in decades of footwear leadership (and apparel too). Product engine became gender led: women, men, and kids (like Zara, GAP, H&M or any other generic fashion brand).

Consumers are not so elastic as some business leaders think or hope. And consumers are not so loyal as some business leaders think or hope. So, what happened? Simple. Many consumers - mainly occasional buyers - did not follow Nike (surprise, surprise) but continued shopping where they were shopping before the decision of the CEO and the President of the Brand. So, once they could not find Nike sneakers in “their” stores – because Nike wasn’t serving those stores any longer -, they simply opted for other brands.

Until late 2010s, Nike had been on a total offense mode (being #1 in every market, in every category, in every product BU, basically in every dimension), a sort of military occupation of the marketplace and a huge problem for competitors that did not know how to react under such a domination. The strategic focus was only one: win anywhere. The new strategy determined the end of the marketplace occupation. Nike opened unexpected spaces to competitors, small, medium, or large brands (with exception of the company based in Herzogenaurach, that – as they usually do - copied and pasted the Nike strategy and executed it in a milder format).

One of the empiric laws of business says that online, the main lever of competition is “price” (as the organic consumer funnel is built on price comparison). The proverbial ability of Nike to leverage the power of the brand to sell sneakers at 200$ began to be threatened by the online appetite for discounts and the search for a definitive solution to the inventory issue. Gross margin – because of that – instead of growing due to the growth of DTC business, showed a rapid decline due to a never-ending promotional attitude on Nike.com

Nike has been built for 50 years on a very simple foundation: brand, product, and marketplace. The DC Investment model, since Nike became a public company, has been always the same: invest at least one tenth of the revenues in demand creation and sports marketing. The brand model has been very simple as well: focus on innovation and inspiration, creativity and storytelling based on athletes-products synergy, leveraging the power of the emotions that sport can create, trying to inspire a growing number of athletes* (*if you have a body, you are an athlete) to play sport. That’s what made Nike the Nike we used to know, love, admire, professionally and emotionally.

What happened in 2020? Well, the brand team shifted from brand marketing to digital marketing and from brand enhancing to sales activation.

shift from CREATE DEMAND to SERVE AND RETAIN DEMAND, that meant that most of the investment were directed to those who were already Nike consumers

as of 2021, to drive traffic to Nike.com, Nike started investing in programmatic adv and performance marketing the double or more of the share of resources usually invested in the other brand activities

the former CMO was ignoring the growing academic literature around the inefficiencies of investment in performance marketing/programmatic advertising, due to frauds, rising costs of mediators and declining consumer response to those activities.

Because of that, Nike invested a material amount of dollars (billions) into something that was less effective but easier to be measured vs something that was more effective but less easy to be measured.

To feed the digital marketing ecosystem, one of the historic functions of the marketing team (brand communications) was “de facto” absorbed and marginalized by the brand design team, which took the leadership in marketing content production (together with the mar-tech “scientists”). Nike didn’t need brand creativity anymore, just a polished and never stopping supply chain of branded stuff.

He made “Nike.com” the center of everything and diverted focus and dollars to it. Due to all of that, Nike hasn’t made a history making brand campaign since 2018, as the Brand organization had to become a huge sales activation machine.

On top of that, INR has no spies abroad, no satellites in the sky, no bugs on any laptops. But it reads the same raw intel as everyone else, and in at least a few cases, was the only agency to get some key questions right.

Almost as soon as Avery arrived at INR in 1962, she and her supervisor Allen Whiting proved their mettle by predicting that China and India would engage in border clashes, then pause, then resume hostilities, then halt. All of that happened.But INR also had messages that the Kennedy and Johnson administrations of the time didn’t want to hear. In 1963, the bureau prepared a report of statistics on the war effort: the number of Viet Cong attacks and the number of prisoners, weapons, and defectors collected by the South. All of the trendlines were negative. The report prompted a furious protest from the Joint Chiefs of Staff, who argued that the South Vietnamese were succeeding.

The evidence that Hussein was reconstituting Iraq’s nuclear program — a contention that fueled Bush administration officials’ arguments for war, like national security adviser Condoleezza Rice’s famous quip, “We don’t want the smoking gun to be a mushroom cloud” — had two primary components. One was a finding that the Iraqi military had been purchasing a number of high-strength aluminum tubes, which the CIA and DIA thought could be used to build centrifuges for enriching uranium.On September 6, 2001, five days before the 9/11 attacks, INR issued a report disagreeing with that finding. For one thing, scientists at the Department of Energy had looked into the matter and found that Iraq had already disclosed in the past that it used aluminum tubes of the same specifications to manufacture artillery rockets, going back over a decade. Moreover, the new tubes were to be “anodized,” a treatment that renders them much less usable for centrifuges.

INR’s successful call on the 2022 Ukraine invasion reportedly came because OPN’s polling found that residents of eastern Ukraine were more anti-Russian and more eager to fight an invasion than previously suspected. The polling, Assistant Secretary Brett Holmgren says, has “allowed us to observe consistently, quarter over quarter, overwhelming Ukrainian will to fight across the board and willingness to continue to defend their territory and to take up arms against Russian aggression.”

While no single ingredient seems to explain its relative success, a few ingredients together might:INR analysts are true experts. They are heavily recruited from PhD programs and even professorships, and have been on their subject matter (a set of countries, or a thematic specialty like trade flows or terrorism) for an average of 14 years. CIA analysts typically switch assignments every two to three years.INR’s small size means that analyses are written by individuals, not by committee, and analysts have fewer editors and managers separating them from the policymakers they’re advising. That means less groupthink, and clearer individual perspectives.INR staff work alongside State Department policymakers, meaning they get regular feedback on what kind of information is most useful to them.

But the flat structure, combined with the agency’s tiny size, means analysts get a great deal of freedom. Vic Raphael, who retired in 2022 as INR’s deputy in charge of analysis, notes that analysts’ work “would only go through three or four layers before we released it. The analyst, his peers, the office director, the analytic review staff, I’d look at it, and boom it went.” Very little separates a rank-and-file analyst from their ultimate consumer, whether that’s an assistant secretary or even the secretary of state.

The bureau also stands out as unusually embedded with policymakers. Analysts at other agencies aren’t working side by side with diplomats actually implementing foreign policy; INR analysts are in the same building as their colleagues in State Department bureaus managing policy toward specific countries, or on nonproliferation or drug trafficking, or on human rights and democracy. Goldberg, who led INR under Secretary of State Hillary Clinton, notes that “we could respond much more quickly than farming it out to another part of the intelligence community, because on a day-to-day basis, we had an idea of what was on her mind.”

Fingar told me yet another favorite win. "The specific issue was, would Argentina send troops to the multinational force in Haiti?" in 1994, as the US assembled a coalition of nations, under the banner of the UN, to invade and restore Haiti’s democratically elected president to office. "Our embassy had reported they'd be there. Argentine embassy in Washington: they'll be there. The State Department, the Argentine desk: they'll be there. [The CIA]: they'll be there.” But, “INR said, no, they won't.” The undersecretary running the meeting, Peter Tarnoff, asked which analyst at INR believed this. He was told it was Jim Buchanan.At that point, as Fingar remembers it, Tarnoff ended the meeting, because Buchanan’s opinion settled the matter. That’s how good Buchanan’s, and INR’s, reputation was. And sure enough, Argentina backed out on its promise to send troops.

Despite being experienced managers, what they learned was eye-opening. One explained that “it was like the sun rose for the first time. … I saw the bigger picture.” They had never seen the pieces — the jobs, technologies, tools, and routines — connected in one place, and they realized that their prior view was narrow and fractured. A team member acknowledged, “I only thought of things in the context of my span of control.”

The maps of the organization generated by the project teams also showed that their organizations often lacked a purposeful, integrated design that was centrally monitored and managed. There may originally have been such a design, but as the organization grew, adapted to changing markets, brought on new leadership, added or subtracted divisions, and so on, this animating vision was lost. The original design had been eroded, patched, and overgrown with alternative plans. A manager explained, “Everything I see around here was developed because of specific issues that popped up, and it was all done ad hoc and added onto each other. It certainly wasn’t engineered.”

“They see problems, and the general approach, the human approach, is to try and fix them. … Functions have tried to put band-aids on every issue that comes up. It sounds good, but when they are layered one on top of the other they start to choke the organization. But they don’t see that because they are only seeing their own thing.”

Ultimately, the managers realized that what they had previously attributed to the direction and control of centralized, bureaucratic forces was actually the aggregation of the distributed work and uncoordinated decisions of people dispersed throughout the organization. Everyone was working on the part of the organization they were familiar with, assuming that another set of people were attending to the larger picture, coordinating the larger system to achieve goals and keeping the organization operating. Except no one was actually looking at how people’s work was connecting across the organization day-to-day.

as they felt a sense of empowerment about changing the organization, they felt a sense of alienation about returning to their central roles. “You really start understanding all of the waste and all of the redundancy and all of the people who are employed as what I call intervention resources,” one person told us.

In the end, a slight majority of the employees returned to their role to continue their career (25 cases). They either were promoted (7 cases), moved laterally (8 cases), or returned to their jobs (10 cases). However, 23 chose organizational change roles.

This study suggests that when companies undertake organizational change efforts, they should consider not only the implications for the organization, but also for the people tasked to do the work. Further, it highlights just how infrequently we recognize how poorly designed and managed many of our organizations really are. Not acknowledging the dysfunction of existing routines protects us from seeing how much of our work is not actually adding value, something that may lead simply to unsatisfying work, no less to larger questions about the nature of organizational design similar to those asked by the managers in my study. Knowledge of the systems we work in can be a source of power, yes. But when you realize you can’t affect the big changes your organization needs, it can also be a source of alienation.

inside look at how Perplexity builds product—which to me feels like what the future of product development will look like for many companies:AI-first: They’ve been asking AI questions about every step of the company-building process, including “How do I launch a product?” Employees are encouraged to ask AI before bothering colleagues.Organized like slime mold: They optimize for minimizing coordination costs by parallelizing as much of each project as possible.Small teams: Their typical team is two to three people. Their AI-generated (highly rated) podcast was built and is run by just one person.Few managers: They hire self-driven ICs and actively avoid hiring people who are strongest at guiding other people’s work.A prediction for the future: Johnny said, “If I had to guess, technical PMs or engineers with product taste will become the most valuable people at a company over time.”

Typical projects we work on only have one or two people on it. The hardest projects have three or four people, max. For example, our podcast is built by one person end to end. He’s a brand designer, but he does audio engineering and he’s doing all kinds of research to figure out how to build the most interactive and interesting podcast. I don’t think a PM has stepped into that process at any point.

We leverage product management most when there’s a really difficult decision that branches into many directions, and for more involved projects.

The hardest, and most important, part of the PM’s job is having taste around use cases. With AI, there are way too many possible use cases that you could work on. So the PM has to step in and make a branching qualitative decision based on the data, user research, and so on.

a big problem with AI is how you prioritize between more productivity-based use cases versus the engaging chatbot-type use cases.

we look foremost for flexibility and initiative. The ability to build constructively in a limited-resource environment (potentially having to wear several hats) is the most important to us.

We look for strong ICs with clear quantitative impacts on users rather than within their company. If I see the terms “Agile expert” or “scrum master” in the resume, it’s probably not going to be a great fit.

My goal is to structure teams around minimizing “coordination headwind,” as described by Alex Komoroske in this deck on seeing organizations as slime mold. The rough idea is that coordination costs (caused by uncertainty and disagreements) increase with scale, and adding managers doesn’t improve things. People’s incentives become misaligned. People tend to lie to their manager, who lies to their manager. And if you want to talk to someone in another part of the org, you have to go up two levels and down two levels, asking everyone along the way.

Instead, what you want to do is keep the overall goals aligned, and parallelize projects that point toward this goal by sharing reusable guides and processes.

Perplexity has existed for less than two years, and things are changing so quickly in AI that it’s hard to commit beyond that. We create quarterly plans. Within quarters, we try to keep plans stable within a product roadmap. The roadmap has a few large projects that everyone is aware of, along with small tasks that we shift around as priorities change.

Each week we have a kickoff meeting where everyone sets high-level expectations for their week. We have a culture of setting 75% weekly goals: everyone identifies their top priority for the week and tries to hit 75% of that by the end of the week. Just a few bullet points to make sure priorities are clear during the week.

All objectives are measurable, either in terms of quantifiable thresholds or Boolean “was X completed or not.” Our objectives are very aggressive, and often at the end of the quarter we only end up completing 70% in one direction or another. The remaining 30% helps identify gaps in prioritization and staffing.

At the beginning of each project, there is a quick kickoff for alignment, and afterward, iteration occurs in an asynchronous fashion, without constraints or review processes. When individuals feel ready for feedback on designs, implementation, or final product, they share it in Slack, and other members of the team give honest and constructive feedback. Iteration happens organically as needed, and the product doesn’t get launched until it gains internal traction via dogfooding.

all teams share common top-level metrics while A/B testing within their layer of the stack. Because the product can shift so quickly, we want to avoid political issues where anyone’s identity is bound to any given component of the product.

We’ve found that when teams don’t have a PM, team members take on the PM responsibilities, like adjusting scope, making user-facing decisions, and trusting their own taste.

What’s your primary tool for task management, and bug tracking?Linear. For AI products, the line between tasks, bugs, and projects becomes blurred, but we’ve found many concepts in Linear, like Leads, Triage, Sizing, etc., to be extremely important. A favorite feature of mine is auto-archiving—if a task hasn’t been mentioned in a while, chances are it’s not actually important.The primary tool we use to store sources of truth like roadmaps and milestone planning is Notion. We use Notion during development for design docs and RFCs, and afterward for documentation, postmortems, and historical records. Putting thoughts on paper (documenting chain-of-thought) leads to much clearer decision-making, and makes it easier to align async and avoid meetings.Unwrap.ai is a tool we’ve also recently introduced to consolidate, document, and quantify qualitative feedback. Because of the nature of AI, many issues are not always deterministic enough to classify as bugs. Unwrap groups individual pieces of feedback into more concrete themes and areas of improvement.

High-level objectives and directions come top-down, but a large amount of new ideas are floated bottom-up. We believe strongly that engineering and design should have ownership over ideas and details, especially for an AI product where the constraints are not known until ideas are turned into code and mock-ups.

Big challenges today revolve around scaling from our current size to the next level, both on the hiring side and in execution and planning. We don’t want to lose our core identity of working in a very flat and collaborative environment. Even small decisions, like how to organize Slack and Linear, can be tough to scale. Trying to stay transparent and scale the number of channels and projects without causing notifications to explode is something we’re currently trying to figure out.

The last three jobs I've held in the last 5 years have all felt like someone just handing me the keys to a sinking boat before they jump off. Every job is sold as having at least some greenfield development where you can "own" the domain and "lead" the direction of the project, but once you accept the offer and get on-boarded, you realize that the system is so brittle that any change will completely break and cause incidents, and there is a year's worth of backlog issues to address with duck-tape and glue before you could even consider fixing the fundamental problems.

Often the teams that built these systems are long gone, so there is nobody to ask for help when you're learning the rough edges, you're just on your own. The technology decisions are all completely set in stone because we could never justify the risk of making changes. There is so much tech debt and maintenance work, we don't really have time to do any new development with the current staffing levels. The job then becomes dominated by on-call responsibilities and fire-fighting. It's 90% toil, and almost zero actual system design and development work.

Being responsible for a whole system that you didn't build, that you know is brittle and broken, but which you cannot fix, is incredibly stressful. It's almost a hopeless situation.