Career advice in 2025. @ Irrational Exuberance

Hi folks,

This is the weekly digest for my blog, Irrational Exuberance. Reach out with thoughts on Twitter at @lethain, or reply to this email.

Posts from this week:

- Career advice in 2025.
- Setting policy for strategy.

Career advice in 2025.

Yesterday, the tj-actions repository, a popular tool used with Github Actions was compromised (for more background read one of these two articles). Watching the infrastructure and security engineering teams at Carta respond, it highlighted to me just how much LLMs can’t meaningfully replace many essential roles of software professionals. However, I’m also reading Jennifer Palkha’s Recoding America, which makes an important point: decision-makers can remain irrational longer than you can remain solvent. (Or, in this context, remain employed.)

I’ve been thinking about this a lot lately, as I’ve ended up having more “2025 is not much fun”-themed career discussions with prior colleagues navigating the current job market. I’ve tried to pull together my points from those conversations here:

1. Many people who first entered senior roles in 2010-2020 are finding current roles a lot less fun. There are a number of reasons for this. First, managers were generally evaluated in that period based on their ability to hire, retain and motivate teams. The current market doesn’t value those skills particularly highly, but instead prioritizes a different set of skills: working in the details, pushing pace, and navigating the technology transition to foundational models / LLMs.

   This means many members of the current crop of senior leaders are either worse at the skills they currently need to succeed, or are less motivated by those activities. Either way, they’re having less fun.

   Similarly, the would-be senior leaders from 2010-2020 era who excelled at working in the details, pushing pace and so on, are viewed as stagnate in their careers so are still finding it difficult to move into senior roles. This means that many folks feel like the current market has left them behind. This is, of course, not universal. It is a general experience that many people are having. Many people are not having this experience.

2. The technology transition to Foundational models / LLMs as a core product and development tool is causing many senior leaders’ hard-earned playbooks to be invalidated. Many companies that were stable, durable market leaders are now in tenuous positions because foundational models threaten to erode their advantage. Whether or not their advantage is truly eroded is uncertain, but it is clear that usefully adopting foundational models into a product requires more than simply shoving an OpenAI/Anthropic API call in somewhere.

   Instead, you have to figure out how to design with progressive validation, with critical data validated via human-in-the-loop techniques before it is used in a critical workflow. It also requires designing for a rapidly improving toolkit: many workflows that were laughably bad in 2023 work surprisingly well with the latest reasoning models. Effective product design requires architecting for both massive improvement, and no improvement at all, of models in 2026-2027.

   This is equally true of writing software itself. There’s so much noise about how to write software, and much of it’s clearly propaganda–this blog’s opening anecdote regarding the tj-actions repository prove that expertise remains essential–but parts of it aren’t. I spent a few weeks in the evenings working on a new side project via Cursor in January, and I was surprised at how much my workflow changed even through Cursor itself was far from perfect. Even since then, Claude has advanced from 3.5 to 3.7 with extended thinking. Again, initial application development might easily be radically different in 2027, or it might be largely unchanged after the scaffolding step in complex codebases. (I’m also curious to see if context window limitations drive another flight from monolithic architectures.)

   Sitting out this transition, when we are relearning how to develop software, feels like a high risk proposition. Your well-honed skills in team development are already devalued today relative to three years ago, and now your other skills are at risk of being devalued as well.

3. Valuations and funding are relatively less accessible to non-AI companies than they were three years ago. Certainly elite companies are doing alright, whether or not they have a clear AI angle, but the cutoff for remaining elite has risen. Simultaneously, the public markets are challenged, which means less willingness for both individuals and companies to purchase products, which slows revenue growth, further challenging valuations and funding.

   The consequence of this if you’re at a private, non-AI company, is that you’re likely to hire less, promote less, see less movement in pay bands, and experience a less predictable path to liquidity. It also means fewer open roles at other companies, so there’s more competition when attempting to trade up into a larger, higher compensated role at another company.

   The major exception to this is joining an AI company, but generally those companies are in extremely competitive markets and are priced more appropriately for investors managing a basket of investments than for employees trying to deliver a predictable return. If you join one of these companies today, you’re probably joining a bit late to experience a big pop, your equity might go to zero, and you’ll be working extremely hard for the next five to seven years. This is the classic startup contract, but not necessarily the contract that folks have expected over the past decade as maximum compensation has generally come from joining a later-stage company or member of the Magnificent Seven.

4. As companies respond to the reduced valuations and funding, they are pushing their teams harder to find growth with their existing team. In the right environment, this can be motivating, but people may have opted into to a more relaxed experience that has become markedly less relaxed without their consent.

If you pull all those things together, you’re essentially in a market where profit and pace are fixed, and you have to figure out how you personally want to optimize between people, prestige and learning. Whereas a few years ago, I think these variables were much more decoupled, that is not what I hear from folks today, even if their jobs were quite cozy a few years ago.

Going a bit further, I know folks who are good at their jobs, and have been struggling to find something meaningful for six-plus months. I know folks who are exceptionally strong candidates, who can find reasonably good jobs, but even they are finding that the sorts of jobs they want simply don’t exist right now. I know folks who are strong candidates but with some oddities in their profile, maybe too many short stints, who are now being filtered out because hiring managers need some way to filter through the higher volume of candidates.

I can’t give advice on what you should do, but if you’re finding this job market difficult, it’s certainly not personal. My sense is that’s basically the experience that everyone is having when searching for new roles right now. If you are in a role today that’s frustrating you, my advice is to try harder than usual to find a way to make it a rewarding experience, even if it’s not perfect. I also wouldn’t personally try to sit this cycle out unless you’re comfortable with a small risk that reentry is quite difficult: I think it’s more likely that the ecosystem is meaningfully different in five years than that it’s largely unchanged.

Altogether, this hasn’t really been the advice that anyone wanted when they chatted with me, but it seems to generally have resonated with them as a realistic appraisal of the current markets. Hopefully there’s something useful for you in here as well.

Setting policy for strategy.

This book’s introduction started by defining strategy as “making decisions.” Then we dug into exploration, diagnosis, and refinement: three chapters where you could argue that we didn’t decide anything at all. Clarifying the problem to be solved is the prerequisite of effective decision making, but eventually decisions do have to be made. Here in this chapter on policy, and the following chapter on operations, we finally start to actually make some decisions.

In this chapter, we’ll dig into:

• How we define policy, and how setting policy differs from operating policy as discussed in the next chapter
• The structured steps for setting policy
• How many policies should you set? Is it preferable to have one policy, many policies, or does it not matter much either way?
• Recurring kinds of policies that appear frequently in strategies
• Why it’s valuable to be intentional about your strategy’s altitude, and how engineers and executives generally maintain different altitudes in their strategies
• Criteria to use for evaluating whether your policies are likely to be impactful
• How to develop novel policies, and why it’s rare
• Why having multiple bundles of alternative policies is generally a phase in strategy development that indicates a gap in your diagnosis
• How policies that ignore constraints sound inspirational, but accomplish little
• Dealing with ambiguity and uncertainty created by missing strategies from cross-functional stakeholders

By the end, you’ll be ready to evaluate why an existing strategy’s policies are struggling to make an impact, and to start iterating on policies for strategy of your own.

What is policy?

Policy is interpreting your diagnosis into a concrete plan. That plan will be a collection of decisions, tradeoffs, and approaches. They’ll range from coding practices, to hiring mandates, to architectural decisions, to guidance about how choices are made within your organization.

An effective policy solves the entirety of the strategy’s diagnosis, although the diagnosis itself is encouraged to specify which aspects can be ignored. For example, the strategy for working with private equity ownership acknowledges in its diagnosis that they don’t have clear guidance on what kind of reduction to expect:

Based on general practice, it seems likely that our new Private Equity ownership will expect us to reduce R&D headcount costs through a reduction. However, we don’t have any concrete details to make a structured decision on this, and our approach would vary significantly depending on the size of the reduction.

Faced with that uncertainty, the policy simply acknowledges the ambiguity and commits to reconsider when more information becomes available:

We believe our new ownership will provide a specific target for Research and Development (R&D) operating expenses during the upcoming financial year planning. We will revise these policies again once we have explicit targets, and will delay planning around reductions until we have those numbers to avoid running two overlapping processes.

There are two frequent points of confusion when creating policies that are worth addressing directly:

1. Policy is a subset of strategy, rather than the entirety of strategy, because policy is only meaningful in the context of the strategy’s diagnosis. For example, the “N-1 backfill policy” makes sense in the context of new, private equity ownership. The policy wouldn’t work well in a rapidly expanding organization.

   Any strategy without a policy is useless, but you’ll also find policies without context aren’t worth much either. This is particularly unfortunate, because so often strategies are communicated without those critical sections.

2. Policy describes how tradeoffs should be made, but it doesn’t verify how the tradeoffs are actually being made in practice. The next chapter on operations covers how to inspect an organization’s behavior to ensure policies are followed.

   When reworking a strategy to be more readable, it often makes sense to merge policy and operation sections together. However, when drafting strategy it’s valuable to keep them separate. Yes, you might use a weekly meeting to review whether the policy is being followed, but whether it’s an effective policy is independent of having such a meeting, and what operational mechanisms you use will vary depending on the number of policies you intend to implement.

With this definition in mind, now we can move onto the more interesting discussion of how to set policy.

How to set policy

Every part of writing a strategy feels hard when you’re doing it, but I personally find that writing policy either feels uncomfortably easy or painfully challenging. It’s never a happy medium. Fortunately, the exploration and diagnosis usually come together to make writing your policy simple: although sometimes that simple conclusion may be a difficult one to swallow.

The steps I follow to write a strategy’s policy are:

1. Review diagnosis to ensure it captures the most important themes. It doesn’t need to be perfect, but it shouldn’t have omissions so obvious that you can immediately identify them.

2. Select policies that address the diagnosis. Explicitly match each policy to one or more diagnoses that it addresses. Continue adding policies until every diagnosis is covered.

   This is a broad instruction, but it’s simpler than it sounds because you’ll typically select from policies identified during your exploration phase. However, there certainly is space to tweak those policies, and to reapply familiar policies to new circumstances.

   If you do find yourself developing a novel policy, there’s a later section in this chapter, Developing novel policies, that addresses that topic in more detail.

3. Consolidate policies in cases where they overlap or adjoin. For example, two policies about specific teams might be generalized into a policy about all teams in the engineering organization.

4. Backtest policy against recent decisions you’ve made. This is particularly effective if you maintain a decision log in your organization.

5. Mine for conflict once again, much as you did in developing your diagnosis. Emphasize feedback from teams and individuals with a different perspective than your own, but don’t wholly eliminate those that you agree with. Just as it’s easy to crowd out opposing views in diagnosis if you don’t solicit their input, it’s possible to accidentally crowd out your own perspective if you anchor too much on others’ perspectives.

6. Consider refinement if you finish writing, and you just aren’t sure your approach works – that’s fine! Return to the refinement phase by deploying one of the refinement techniques to increase your conviction. Remember that we talk about strategy like it’s done in one pass, but almost all real strategy takes many refinement passes.

The steps of writing policy are relatively pedestrian, largely because you’ve done so much of the work already in the exploration, diagnosis, and refinement steps. If you skip those phases, you’d likely follow the above steps for writing policy, but the expected quality of the policy itself would be far lower.

How many policies?

Addressing the entirety of the diagnosis is often complex, which is why most strategies feature a set of policies rather than just one. The strategy for decomposing a monolithic application is not one policy deciding not to decompose, but a series of four policies:

1. Business units should always operate in their own code repository and monolith.
2. New integrations across business unit monoliths should be done using gRPC.
3. Except for new business unit monoliths, we don’t allow new services.
4. Merge existing services into business-unit monoliths where you can.

Four isn’t universally the right number either. It’s simply the number that was required to solve that strategy’s diagnosis. With an excellent diagnosis, your policies will often feel inevitable, and perhaps even boring. That’s great: what makes a policy good is that it’s effective, not that it’s novel or inspiring.

Kinds of policies

While there are so many policies you can write, I’ve found they generally fall into one of four major categories: approvals, allocations, direction, and guidance. This section introduces those categories.

Approvals define the process for making a recurring decision. This might require invoking an architecture advice process, or it might require involving an authority figure like an executive.

In the Index post-acquisition integration strategy, there were a number of complex decisions to be made, and the approval mechanism was:

Escalations come to paired leads: given our limited shared context across teams, all escalations must come to both Stripe’s Head of Traffic Engineering and Index’s Head of Engineering.

This allowed the acquired and acquiring teams to start building trust between each other by ensuring both were consulted before any decision was finalized. On the other hand, the user data access strategy’s approval strategy was more focused on managing corporate risk:

Exceptions must be granted in writing by CISO. While our overarching Engineering Strategy states that we follow an advisory architecture process as described in Facilitating Software Architecture, the customer data access policy is an exception and must be explicitly approved, with documentation, by the CISO. Start that process in the #ciso channel.

These two different approval processes had different goals, so they made tradeoffs differently. There are so many ways to tweak approval, allowing for many different tradeoffs between safety, productivity, and trust.

Allocations describe how resources are split across multiple potential investments. Allocations are the most concrete statement of organizational priority, and also articulate the organization’s belief about how productivity happens in teams. Some companies believe you go fast by swarming more people onto critical problems. Other companies believe you go fast by forcing teams to solve problems without additional headcount. Both can work, and teach you something important about the company’s beliefs.

The strategy on Uber’s service migration has two concrete examples of allocation policies. The first describes the Infrastructure engineering team’s allocation between manual provision tasks and investing into creating a self-service provisioning platform:

Constrain manual provisioning allocation to maximize investment in self-service provisioning. The service provisioning team will maintain a fixed allocation of one full time engineer on manual service provisioning tasks. We will move the remaining engineers to work on automation to speed up future service provisioning. This will degrade manual provisioning in the short term, but the alternative is permanently degrading provisioning by the influx of new service requests from newly hired product engineers.

The second allocation policy is implicitly noted in this strategy’s diagnosis, where it describes the allocation policy in the Engineering organization’s higher altitude strategy:

Within infrastructure engineering, there is a team of four engineers responsible for service provisioning today. While our organization is growing at a similar rate as product engineering, none of that additional headcount is being allocated directly to the team working on service provisioning. We do not anticipate this changing.

Allocation policies often create a surprising amount of clarity for the team, and I include them in almost every policy I write either explicitly, or implicitly in a higher altitude strategy.

Direction provides explicit instruction on how a decision must be made. This is the right tool when you know where you want to go, and exactly the way that you want to get there. Direction is appropriate for problems you understand clearly, and you value consistency more than empowering individual judgment.

Direction works well when you need an unambiguous policy that doesn’t leave room for interpretation. For example, Calm’s policy for working in the monolith:

We write all code in the monolith. It has been ambiguous if new code (especially new application code) should be written in our JavaScript monolith, or if all new code must be written in a new service outside of the monolith. This is no longer ambiguous: all new code must be written in the monolith.

In the rare case that there is a functional requirement that makes writing in the monolith implausible, then you should seek an exception as described below.

In that case, the team couldn’t agree on what should go into the monolith. Individuals would often make incompatible decisions, so creating consistency required removing personal judgment from the equation.

Sometimes judgment is the issue, and sometimes consistency is difficult due to misaligned incentives. A good example of this comes in strategy on working with new Private Equity ownership:

We will move to an “N-1” backfill policy, where departures are backfilled with a less senior level. We will also institute a strict maximum of one Principal Engineer per business unit.

It’s likely that hiring managers would simply ignore this backfill policy if it was stated more softly, although sometimes less forceful policies are useful.

Guidance provides a recommendation about how a decision should be made. Guidance is useful when there’s enough nuance, ambiguity, or complexity that you can explain the desired destination, but you can’t mandate the path to reaching it.

One example of guidance comes from the Index acquisition integration strategy:

Minimize changes to tokenization environment: because point-of-sale devices directly work with customer payment details, the API that directly supports the point-of-sale device must live within our secured environment where payment details are stored.

However, any other functionality must not be added to our tokenization environment.

This might read like direction, but it’s clarifying the desired outcome of avoiding unnecessary complexity in the tokenization environment. However, it’s not able to articulate what complexity is necessary, so ultimately it’s guidance because it requires significant judgment to interpret.

A second example of guidance comes in the strategy on decomposing a monolithic codebase:

Merge existing services into business-unit monoliths where you can. We believe that each choice to move existing services back into a monolith should be made “in the details” rather than from a top-down strategy perspective. Consequently, we generally encourage teams to wind down their existing services outside of their business unit’s monolith, but defer to teams to make the right decision for their local context.

This is another case of knowing the desired outcome, but encountering too much uncertainty to direct the team on how to get there. If you ask five engineers about whether it’s possible to merge a given service back into a monolithic codebase, they’ll probably disagree. That’s fine, and highlights the value of guidance: it makes it possible to make incremental progress in areas where more concrete direction would cause confusion.

When you’re working on a strategy’s policy section, it’s important to consider all of these categories. Which feel most natural to use will vary depending on your team and role, but they’re all usable:

• If you’re a developer productivity team, you might have to lean heavily on guidance in your policies and increased support for that guidance within the details of your platform.
• If you’re an executive, you might lean heavily on direction. Indeed, you might lean too heavily on direction, where guidance often works better for areas where you understand the direction but not the path.
• If you’re a product engineering organization, you might have to narrow the scope of your direction to the engineers within that organization to deal with the realities of complex cross-organization dynamics.

Finally, if you have a clear approach you want to take that doesn’t fit cleanly into any of these categories, then don’t let this framework dissuade you. Give it a try, and adapt if it doesn’t initially work out.

Maintaining strategy altitude

The chapter on when to write engineering strategy introduced the concept of strategy altitude, which is being deliberate about where certain kinds of policies are created within your organization.

Without repeating that section in its entirety, it’s particularly relevant when you set policy to consider how your new policies eliminate flexibility within your organization. Consider these two somewhat opposing strategies:

• Stripe’s Sorbet strategy only worked in an organization that enforced the use of a single programming language across (essentially) all teams
• Uber’s service migration strategy worked well in an organization that was unwilling to enforce consistent programming language adoption across teams

Stripe’s organization-altitude policy took away the freedom of individual teams to select their preferred technology stack. In return, they unlocked the ability to centralize investment in a powerful way. Uber went the opposite way, unlocking the ability of teams to pick their preferred technology stack, while significantly reducing their centralized teams’ leverage.

Both altitudes make sense. Both have consequences.

Criteria for effective policies

In The Engineering Executive’s Primer’s chapter on engineering strategy, I introduced three criteria for evaluating policies. They ought to be applicable, enforced, and create leverage. Defining those a bit:

1. Applicable: it can be used to navigate complex, real scenarios, particularly when making tradeoffs.
2. Enforced: teams will be held accountable for following the guiding policy.
3. Create Leverage: create compounding or multiplicative impact.

The last of these three, create leverage, made sense in the context of a book about engineering executives, but probably doesn’t make as much sense here. Some policies certainly should create leverage (e.g. empower developer experience team by restricting new services), but others might not (e.g. moving to an N-1 backfill policy). Outside the executive context, what’s important isn’t necessarily creating leverage, but that a policy solves for part of the diagnosis.

That leaves the other two–being applicable and enforced–both of which are necessary for a policy to actually address the diagnosis. Any policy which you can’t determine how to apply, or aren’t willing to enforce, simply won’t be useful.

Let’s apply these criteria to a handful of potential policies. First let’s think about policies we might write to improve the talent density of our engineering team:

• “We only hire world-class engineers.” This isn’t applicable, because it’s unclear what a world-class engineer means. Because there’s no mutually agreeable definition in this policy, it’s also not consistently enforceable.
• “We only hire engineers that get at least one ‘strong yes’ in scorecards.” This is applicable, because there’s a clear definition. This is enforceable, depending on the willingness of the organization to reject seemingly good candidates who don’t happen to get a strong yes.

Next, let’s think about a policy regarding code reuse within a codebase:

• “We follow a strict Don’t Repeat Yourself policy in our codebase.” There’s room for debate within a team about whether two pieces of code are truly duplicative, but this is generally applicable. Because there’s room for debate, it’s a very context specific determination to decide how to enforce a decision.

• “Code authors are responsible for determining if their contributions violate Don’t Repeat Yourself, and rewriting them if they do.” This is much more applicable, because now there’s only a single person’s judgment to assess the potential repetition. In some ways, this policy is also more enforceable, because there’s no longer any ambiguity around who is deciding whether a piece of code is a repetition.

  The challenge is that enforceability now depends on one individual, and making this policy effective will require holding individuals accountable for the quality of their judgement. An organization that’s unwilling to distinguish between good and bad judgment won’t get any value out of the policy. This is a good example of how a good policy in one organization might become a poor policy in another.

If you ever find yourself wanting to include a policy that for some reason either can’t be applied or can’t be enforced, stop to ask yourself what you’re trying to accomplish and ponder if there’s a different policy that might be better suited to that goal.

Developing novel policies

My experience is that there are vanishingly few truly novel policies to write. There’s almost always someone else has already done something similar to your intended approach. Calm’s engineering strategy is such a case: the details are particular to the company, but the general approach is common across the industry.

The most likely place to find truly novel policies is during the adoption phase of a new widespread technology, such as the rise of ubiquitous mobile phones, cloud computing, or large language models. Even then, as explored in the strategy for adopting large-language models, the new technology can be engaged with as a generic technology:

Develop an LLM-backed process for reactivating departed and suspended drivers in mature markets. Through modeling our driver lifecycle, we determined that improving onboarding time will have little impact on the total number of active drivers. Instead, we are focusing on mechanisms to reactivate departed and suspended drivers, which is the only opportunity to meaningfully impact active drivers.

You could simply replace “LLM” with “data-driven” and it would be equally readable. In this way, policy can generally sidestep areas of uncertainty by being a bit abstract. This avoids being overly specific about topics you simply don’t know much about.

However, even if your policy isn’t novel to the industry, it might still be novel to you or your organization. The steps that I’ve found useful to debug novel policies are the same steps as running a condensed version of the strategy process, with a focus on exploration and refinement:

1. Collect a number of similar policies, with a focus on how those policies differ from the policy you are creating
2. Create a systems model to articulate how this policy will work, and also how it will differ from the similar policies you’re considering
3. Run a strategy testing cycle for your proto-policy to discover any unknown-unknowns about how it works in practice

Whether you run into this scenario is largely a function of the extent of your, and your organization’s, experience. Early in my career, I found myself doing novel (for me) strategy work very frequently, and these days I rarely find myself doing novel work, instead focusing on adaptation of well-known policies to new circumstances.

Are competing policy proposals an anti-pattern?

When creating policy, you’ll often have to engage with the question of whether you should develop one preferred policy or a series of potential strategies to pick from. Developing these is a useful stage of setting policy, but rather than helping you refine your policy, I’d encourage you to think of this as exposing gaps in your diagnosis.

For example, when Stripe developed the Sorbet ruby-typing tooling, there was debate between two policies:

1. Should we build a ruby-typing tool to allow a centralized team to gradually migrate the company to a typed codebase?
2. Should we migrate the codebase to a preexisting strongly typed language like Golang or Java?

These were, initially, equally valid hypotheses. It was only by clarifying our diagnosis around resourcing that it became clear that incurring the bulk of costs in a centralized team was clearly preferable to spreading the costs across many teams. Specifically, recognizing that we wanted to prioritize short-term product engineering velocity, even if it led to a longer migration overall.

If you do develop multiple policy options, I encourage you to move the alternatives into an appendix rather than including them in the core of your strategy document. This will make it easier for readers of your final version to understand how to follow your policies, and they are the most important long-term user of your written strategy.

Recognizing constraints

A similar problem to competing solutions is developing a policy that you cannot possibly fund. It’s easy to get enamored with policies that you can’t meaningfully enforce, but that’s bad policy, even if it would work in an alternate universe where it was possible to enforce or resource it.

To consider a few examples:

• The strategy for controlling access to user data might have proposed requiring manual approval by a second party of every access to customer data. However, that would have gone nowhere.
• Our approach to Uber’s service migration might have required more staffing for the infrastructure engineering team, but we knew that wasn’t going to happen, so it was a meaningless policy proposal to make.
• The strategy for navigating private equity ownership might have argued that new ownership should not hold engineering accountable to a new standard on spending. But they would have just invalidated that strategy in the next financial planning period.

If you find a policy that contemplates an impractical approach, it doesn’t only indicate that the policy is a poor one, it also suggests your policy is missing an important pillar. Rather than debating the policy options, the fastest path to resolution is to align on the diagnosis that would invalidate potential paths forward.

In cases where aligning on the diagnosis isn’t possible, for example because you simply don’t understand the possibilities of a new technology as encountered in the strategy for adopting LLMs, then you’ve typically found a valuable opportunity to use strategy refinement to build alignment.

Dealing with missing strategies

At a recent company offsite, we were debating which policies we might adopt to deal with annual plans that kept getting derailed after less than a month. Someone remarked that this would be much easier if we could get the executive team to commit to a clearer, written strategy about which business units we were prioritizing.

They were, of course, right. It would be much easier. Unfortunately, it goes back to the problem we discussed in the diagnosis chapter about reframing blockers into diagnosis. If a strategy from the company or a peer function is missing, the empowering thing to do is to include the absence in your diagnosis and move forward.

Sometimes, even when you do this, it’s easy to fall back into the belief that you cannot set a policy because a peer function might set a conflicting policy in the future. Whether you’re an executive or an engineer, you’ll never have the details you want to make the ideal policy. Meaningful leadership requires taking meaningful risks, which is never something that gets comfortable.

Summary

After working through this chapter, you know how to develop policy, how to assemble policies to solve your diagnosis, and how to avoid a number of the frequent challenges that policy writers encounter. At this point, there’s only one phase of strategy left to dig into, operating the policies you’ve created.

That's all for now! Hope to hear your thoughts on Twitter at @lethain!

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