Managing AI overwhelm

Moving from scarcity to abundance

This essay is for you if you’re the CTO of an early-stage startup (e.g. <20 engineers) who wants to make your team far more AI native, but feel too overwhelmed to make it happen. It’s for you if you’ve decided to go “all in” on AI, but are feeling substantial pressure from buzzwordy investors demanding you to use AI to deliver in weeks what used to take years.

It’s for you if you’ve accepted at least at a high-level that software engineering has changed forever, but haven’t yet figured out how to transform your team’s workflows. Perhaps you’ve used Cursor here and there, but whatever you’ve tried doesn’t seem to work for complex brownfield codebase problems. You know it’s possible to do these more complex things because you see other leaders/companies making it work. However, investing resources into this stuff is hard because the non-deterministic nature of LLMs introduces irreducible unpredictability and research overhead. This is compounded by the fact that you and your team feel like they’re constantly drowning to get the bare minimum done.

Consequently, it’s very natural for you or your team to feel stressed or defensive whenever anyone asks why you’re not using more AI to create value. In an ideal world, your team would have the ability to rapidly integrate each new AI innovation to create value proportional to the underlying improvements of LLM performance. But your team is trapped in a dilemma:

• Dive into AI (i.e. exploration) - Sounds nice, but you’re already underwater. There’s a risk you’ll do these experiments at the cost of “safer” work, the experiments won’t pan out and you’ll have nothing to show for it.

• Keep doing what you’re doing (i.e. exploitation) - AI is rapidly changing every quarter, so sticking with “safer” work also feels unsafe because you might suddenly find yourself irrelevant.

Locking into either choice feels stressful but so does deferring the choice, and this stress seems to grow every week as AI improves.

So what should you do?

First, notice that framing this dilemma as exploration vs exploitation misses the mark. In fact, there’s no “safe” choice available because both options tap into the same underlying scarcity mindset.

Nothing ever feels “enough” within a scarcity mindset. Exploration feels like theft from “real” work because it might not pan out, and exploitation feels like falling behind because the external world is changing so quickly.

This scarcity mindset then creates a vicious cycle:

1. Your sense of what’s truly “enough” isn’t sufficiently grounded in reality because fear, judgement and pain from your scarcity mindset distort your assessments. Perhaps each task must be done “perfectly” or you risk feeling like a failure. Perhaps there’s never enough time to do each task because everything feels urgent, despite the team’s overall prioritization.

2. This distortion of what’s enough amplifies your perception of scarcity.

3. This leads to you avoiding any “secondary” duties as a leader (e.g. talking to other leaders within the company to get context, running your own experiments with AI, etc.)

4. This avoidance increases the gap between overall organizational context and your assessment of it.

5. This increased gap makes it harder to understand what’s “enough” for yourself and your team.

6. Repeat step 1 and the vicious cycle repeats.

Building an organization that can fluidly absorb novel AI capabilities requires psychological space to experiment. This in turn requires clarity about what’s actually “enough”. So the deeper you fall into your scarcity mindset, the harder it is to create novel value with AI.

To be clear, possessing patterns around a scarcity mindset isn’t “bad” or “wrong”. They’re self-protective patterns that have genuine value within environments containing emotional or material scarcity. But it’s worth asking whether these patterns are still relevant, or whether they’re running on autopilot in your mind. Moreover, it’s unrealistic to flip a switch to transition from a scarcity to an abundance mindset precisely because they’re often so deep-seated.

Unlike the scarcity mindset, an abundance mindset is a different tone for relating to the world. It’s something like “I now have enough to start creating value for everyone else”. Where a scarcity mindset sees threat from change (e.g your company’s value chain, workflows, etc), an abundance mindset sees opportunity to create value.

Undergoing a shift from scarcity to abundance requires you simultaneously engage in internal and external work. External work involves changing your actions (e.g. behaviors, practices and habits). Internal work changes your being (e.g. noticing, accepting and healing self-protective patterns that generate scarcity).

External work without internal work is limiting because it’ll get quickly undermined by your un-healed internal resistances. Internal work without external work is limiting because you won’t build the external skills necessary to actually change your situation.

A core external practice for engaging in this shift is what my coach calls Bad/Good/Better/Best. This practice gradually improves your ability to track what’s enough, and creates space for you to increasingly create more value. Internal practices are far harder to describe given how contextual each person’s internal resistances are. I’d recommend seeing a therapist or executive coach to help with any resistances that arise as you attempt this practice.

Although this practice can be run as a team, it’s often easier to shift your team from scarcity to abundance if you yourself have made that shift first. The rest of this essay assumes that for now you’re the only one that’s working on this shift.

1. Forming tasks for the next planning cycle

Start by organizing your time around explicit planning cycles. You may initially keep these private as a tool for organizing your time. You may start with a one-week planning cycle given that you’re the CTO of an early-stage startup. It’s natural to experiment with shrinking or growing the duration of these cycles as your role’s volatility changes.

Next, start gathering tasks for yourself and classify each item according to the following:

• Bad (i.e. the red line): Getting everything done in this bucket is the true “minimum”. If it isn’t done, genuinely destructive things will happen. Completing these tasks keeps the lights on.

• Good: The actual value created beyond keeping the lights on.

• Better: Tasks that would create more value than Good.

• Best: The best-case outcome of the value you’d create within the planning cycle.

Tracking the red line is inherently nebulous because it depends on stakeholder moods, competitive dynamics, investor expectations, and other contextual factors that may shift weekly.

The skill of tracking the red line is the crux of an abundance mindset. It’s the mindset of forming an initial hypothesis of what’s enough, running experiments and course-correcting based on what you learn. Your approximations will improve over time with reflection.

Here are some internal distortions that may impair your ability to track the red line. All of these point to some sort of internal work that you may do with your therapist or executive coach:

• Anxiety about seeking clarity - You’re often worried about being seen as “weak” or “pessimistic” if you ask your boss hard questions about your team’s priorities and resourcing.

• Guilt about your work being merely “good enough” - If you’re someone that feels like failure if you consistently fail to exceed the “Good” standard.

• Absorbing others’ urgency - Stakeholders (especially people with more power than you) may regularly make polemical and emotional cases for why certain work should immediately get prioritized. Absorbing this urgency without reflection can be counter-productive.

• Getting dejected by a single failed AI workflow experiment - This is if you temporarily lose all motivation, self-confidence and willingness to experiment the moment any individual AI workflow experiment fails.

2. Reflecting on this cycle’s triage

Next examine your initial triage to inspect the distribution of Bad/Good/Better/Best tasks. Bad’s the bare minimum and should have the smallest number of tasks. Best should have the largest number of tasks. Deviances from this distribution aren’t “bad”. However, it may imply that either the organization is operating at capacity, you are operating at capacity or that you’re all deluding yourselves about what’s enough. The point isn’t to force yourself to fit some ideal distribution, but to use the distribution as a provocation to increase awareness.

It’s also worth noticing whether you can coherently articulate and justify your choice of where the red line is. If you can’t, it’s worth examining why not.

3. Maintaining a tasks backlog

You’re likely getting inundated with new tasks constantly. Many new tasks may create the competing pressure to either drop everything to react to it or to feel guilt and fear for ignoring it.

An alternative is to test each task against the red line. If they’re below the line, they get prioritized for action this cycle. Otherwise, they get added to a backlog for the next triage. It’s better to change the length of each planning cycle rather than breaking the integrity of the process.

Maintaining such a backlog is particularly relevant for filtering novel AI workflows from the “noise” on Twitter/Slack.

4. One right-sized AI experiment

It can be helpful to pre-specify a clear timebox for each experiment popped off the backlog, since LLMs are non-deterministic and may require an unpredictable amount of experimentation.

It’s crucial to prioritize experiments which if they succeed, you’ll use everyday. Over time, this will compound your capacity to create value with AI.

5. Celebrating both failures and successes as learning experiences

It’s worth taking a minute or two to celebrate the shift you’re making from scarcity to abundance irrespective of the outcome of each AI experiment. This gradually builds positive associations with an abundance mindset.

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The shift from scarcity to abundance isn’t a one-time event. Rather, it’s a set of habits you develop augmented by the inner work of overcoming various resistances and shadows.

Acknowledgements

Thank you to Brian Whetten for teaching me everything I’ve articulated in this essay.