August 17, 2021
Productivity Hacks Can’t Save Us
Co-founder and COO
Productivity is the holy grail of knowledge economy performance. In 1959, Peter Drucker — the father of modern management science — first coined the term “knowledge worker.” This set off our race to maximize workplace productivity — the race to do more faster.
Knowledge workers are unique in that they require autonomy to manage their own time and priorities (i.e. a manager can’t dictate the way for a developer to write the best line of code). Workflows and priorities can be set, but individuals themselves are left to determine the best ways to achieve the targeted outcomes. And so we each individually navigate the path towards the fastest/most productive versions of ourselves.
But human beings are creatures of habit. Once a process is determined to be successful, maximizing productivity generally boils down to optimizing what has worked before. So unlocking knowledge worker productivity over the last 50 years has predominantly focused on “hacks” or “microefficiencies” — small shifts and adjustments to improve the underlying processes in order to reach the maximum possible potential.
Search “productivity hack” on Google today and you’re flooded with millions tips and tricks to optimize what’s worked before. But focusing on productivity gains via hacks and microefficiencies alone is a trap.
A quick detour — the best analogy on the trap of pursuing microefficiencies comes from the auto-industry. For the last 150 years, the majority of automobiles produced have been powered by the internal combustion engine —a phenomenal feat of engineering that propels a vehicle with energy captured from tiny petroleum-fueled explosions.
Surprisingly (especially given its prevalence), the efficiency of the internal combustion engine is pretty poor — the maximum theoretical conversion of energy from fuel to kinetic motion peaks at about 37% (the rest of the useful energy is lost to heat and noise). Most internal combustion engines on the road today have a typical efficiency in the range of 20–35%.
The bulk of innovation in the automotive sector for the better part of the last century has been focused on fine-tuning the performance of a fundamentally limited technology. The difference between vehicles on the road running at 20% and the theoretical 37% largely comes down to microefficiencies — tiny modifications to all the inner workings of the engine that push the conversion of energy closer and closer to its theoretical maximum.
Now compare the internal combustion engine to an electric motor. Electric motors use an entirely different method of energy conversion — one that enables a theoretical efficiency of 85–90%. Unlocking these incredible efficiencies for electrically powered automobiles has taken time. Entirely new systems had to be developed to support the higher efficiency propulsion method (energy storage, control systems, charging infrastructure, etc.), but this step-function-increase in productive output makes it clear that in the long run, the new approach will win out over the traditional method. Macroefficiency > microefficiency.
Optimizing productivity work flows is the same as optimizing any new innovation. There is always a theoretical limit to what any process can achieve and it’s easy to get stuck optimizing the same familiar processes out of habit.
Every once in a while, there comes along a breakthrough technology that completely reimagines how a process can carried out. Slack is a great example here — by developing a novel approach to information sharing and collaboration, Slack was massively successful in creating a revolutionary product that changed the way we communicate with others.
But more often than not, technology tools in the productivity space develop innovation that simply refreshes old models of doing things. Superhuman recently raised $75M to make email faster. Calendly recently raised $350M to share your availability with as few clicks as possible. There’s no shortage of new tech promising to help us all “do X faster.” The market is flooded with new products delivering productivity hack after productivity hack in attempt to unlock the future.
Most of these technologies build on the same underlying hypothesis: productivity is best increased via optimizing microefficiency. Taking all the little tasks that make up routine work, making them each marginally faster, and the sum of all these savings represents a more efficient use of time.
Optimizing for productivity hacks assumes that all of those “little tasks making up a routine” are the best way to complete the underlying job to be done. And in some cases, speeding up an underlying process begins to expose fundamental flaws.
Consider the familiar process of scheduling a meeting with your co-workers. When most people schedule a meeting, the job to be done is “confirm a time when everyone can meet.” In its most basic form, this is solved by finding the next available time when everyone is free. There are lots of technology tools out there today that can do this quickly — identifying the blank space for all parties and arbitrarily picking a time within it. But take closer look at what optimizing this basic flow does:
- Organizer — Decide to meet
- Organizer — If possible, identify overlaps in free space for all parties
- Organizer — Select an option that seems to work best with what’s visible in most people’s schedules
- All — Meet
Overall its is a fairly efficient process, for the organizer at least. But what are the implications for everyone else?
- Attendee — Get invited to meet
- Attendee — Reorganize plans for that day to accommodate the change in schedule
- All — Meet
The trouble is that most scheduling tools make the assumption that empty space is free space — as if for all the times when someone is not in a meeting, they are sitting patiently waiting to be invited to a meeting. But the reality is that knowledge workers use that apparent “idle time” to work. Time spent on individual knowledge work is rarely blocked out as unavailable in our calendars. As a result, the implications of quickly scheduling a meeting have ripple effects across the workplace. There comes a point where speeding up the process to accomplish a goal for one party begins to have negative impacts for others with whom they collaborate.
Productivity tools focused on microefficiencies imply that the existing underlying processes being optimized are the right processes to optimize in the first place. We need to think critically about what the goal of each task is, and the trade-offs that need to be considered.
Returning to the example of scheduling a meeting internally, each participant has a unique view of their capacity for a new meeting as it balances out with all of their undocumented priorities. There’s a fundamental informational asymmetry in this process when one party attempts to pick a time for everyone. But rather than reducing the incomplete information on how individual priorities are reflected across everyone’s schedules, most tools are innovating microefficiencies to complete the old processes faster — regardless the cost.
At Mayday, our hypothesis on why recent innovation in productivity tools has failed to make us that much more productive is that we’re nearing the theoretical thresholds for existing workflows. Microefficiency after microefficiency has been employed to shave seconds off of each task, but to see real productivity gains, it’s the underlying systems themselves that need to evolve.
We’re not all feeling frustrated and looking for “productivity solutions” because it’s taking us 30 seconds instead of 2 to share our availability, we’re feeling frustrated because the we’re filling up our calendars with things that don’t reflect our priorities at any given moment in time.
The time has come for the productivity’s “electric vehicle moment” — a step function increase in output by changing underlying mechanics of how we get things done. By rethinking the models for how we plan and spend our time we can achieve the breakthroughs in performance that we’re all chasing.