Actions per minute
What StarCraft 2 taught me about B2B SaaS
One day in mid-March I saw the future.
I had just kicked off a half-dozen parallel Deep Research queries, Windsurf’s coding agent was working on a new frontend feature prototype for me in the background, and while these tasks ran I caught up on Slack and emails — tabbing over occasionally to check on the AIs’ progress.
Hey, I thought, all this clicking around reminds me of when I used to play StarCraft (bear with me here).
If you haven’t played StarCraft, it’s a competitive real-time strategy (RTS) game. Each player controls a mix of workers, combat units, and buildings. The objective of the game is to destroy all your opponent’s buildings.
StarCraft 2 (the latest iteration, released in 2010) looks like this1
A typical game of StarCraft 2 is a 1v1 that lasts about 20 minutes and progresses through a few phases:
Early game: Gathering resources with workers, constructing an initial set of buildings (a ‘base’), and fighting in small skirmishes
Midgame: Expanding to new bases, building an army, and pushing to control territory and resources on the map
Lategame: Large, potentially decisive, clashes with armies of powerful upgraded units
There are important strategic choices to make in a game, such as the buildings and specific army composition you choose to construct (there are rock-paper-scissors-like dynamics in combat where some units ‘counter’ others). But more important than strategy is each player’s execution skill, which players break down into two dimensions: ‘micro’ and ‘macro’.
Macro is economy management; how effectively each player uses their workers to gather resources, and how efficiently they spend those resources to construct buildings and combat units
Micro is the fine-grained control of units — focus firing, moving damaged units to safety, targeting abilities. In combat, quick repositioning can be decisive, and small forces controlled by a player with ‘good micro’ can beat much larger ones
A common piece of advice to new players is to practice macro before anything else; simply having more stuff than your opponent can overcome deficiencies in micro or strategy.
Both micro and macro are components of player activity. At the climax of a StarCraft game, a player might need to manage a substantial economy, a few dozen buildings, and hundreds of units spread all over the map.
Since the game space is continuous, the number of potential actions available to a player in any given instant are practically infinite. Think of this pool of potential actions as a resource; a player that can do more things per unit of time can exploit this latent resource and get a decisive advantage.
The standard measure of player activity is ‘Actions per Minute’ (APM). APM is simply the average number of productive commands a player issues in a minute — including moves, attacks, building orders, resource gathering tasks, etc. APM is also one of the most important metrics for evaluating player skill2. A beginner might have an APM of 50 or so, a strong amateur perhaps 200. A professional player might be as high as 400 (~7 actions per second!).
APM by in game rank, sorted from lowest rank on the left to highest. The graph is from a study where the developer of StarCraft (Blizzard) looked for factors that correlated with player skill (i.e. ingame ranks) and found that APM was the best of the metrics they looked at
While APM is an imperfect measure, it does correlate well with skill because to have a high APM you need to:
Identify actions you can take
Do them (fast)
To see why APM is a good skill metric it helps to view it as ‘load-handling capacity’. A core part of player skill is managing APM budget and focus; there is always something productive to do at any given moment3.
Strategy becomes decisive when players have comparable mechanical ability. Many effective strategies in StarCraft are really techniques to create imbalances in required activity — for instance, attacking multiple bases at once, or using distraction tactics so the opponent’s attention (and APM budget) is stretched thin by having to respond to multiple concurrent threats.
My favourite definition of strategy is from Michael Porter: “The essence of strategy is choosing what not to do.” Strategy is a filter that guides action selection, it is necessary because even the best humans are action-limited.
But if you could do everything, would you need strategy?
Back in 2019 DeepMind unveiled AlphaStar, a reinforcement learning model built to play StarCraft. AlphaStar was strong — it managed to beat professional players in exhibition matches.
While DeepMind capped AlphaStar’s average APM to ~300, the model actually managed to get around this limitation by conserving actions and bursting up to 900 to 1500 APM in short intervals during fights4. If you watch the exhibition games you get the impression that superior micro is a big part of the reason why AlphaStar was able to win: AlphaStar has a tendency to build relatively cheap yet highly maneuverable combat units (‘Stalkers’), take them into fights they shouldn’t win (based on unit composition), and outmaneuver the opponent to get incredible value per unit.
What AlphaStar showed, in my view, is that perfect mechanics can make strategy obsolete. Humans have to use strategy to prioritize actions. Computers, at least from our point of view, don’t need to prioritize — they can simply do everything at once. Even if you have the best strategy in the world it’s basically impossible to beat someone who takes two times, or ten times, as many productive actions as you. Actions dominate strategy.
So where am I going with this?
The reason why I was reminded of StarCraft was because it was the first time I felt like my own APM was an important factor in my own ability to get stuff done at work; the more processes I could kick off, the more useful outputs I could generate.
I suspect that the next few years will be quite strange for knowledge workers; AI labs are starting to roll out more agentic tools (e.g. Codex) that work in the background, and critically can be run in parallel.
These agents don’t even have to be particularly good to generate value, as long as they expand the capacity of the team. For instance, we’re trialing out Devin (“the AI software engineer”) at my company. It’s not a particularly good programmer — it fixes perhaps 10% of problems we throw at it and we have to reject most of its code suggestions. However, the problems it does fix might not have been ones that our team would have gotten to for a while, if at all. It’s relatively cheap and easy to throw a bunch of Devin agents at a problem and see what happens.
Assuming AI agents continue to get better, the knowledge worker’s job may shift away from planning and strategy, and towards triggering and overseeing large parallel jobs and gathering context to ‘feed the agents’. I think we’ll have an intermediate phase where high performers are those with high APM to trigger and manage automations or agents. But these action calling tasks will soon enough get abstracted away into new orchestration and context management platforms. Not long after, the AI will be triggering itself.
Companies today operate much closer to turn-based strategy. Their 'clock speed' is dictated by human decision-making cycles, reporting structures, and meeting cadences. The lower your clock speed, the more important choosing what to do with your action budget is. Skills like navigating bureaucracy, managing stakeholder alignment, and building consensus are valuable because the organizational APM is low. These are skills optimized for a low-frequency environment and are intended to increase the average value of a given action or decision.
There’s perhaps another type of organizational design that is viable in a world of many cheap and ‘good enough’ agents that prioritizes increasing the number of actions. We could see a bifurcation in how companies operate, based on whether they want to optimize for number of actions or average action quality.
But how are ‘AI agents’ different than current software or contractors that allow companies to do many ‘actions per minute’ already?
I see LLMs and their kin as tools to for knowledge workers (and their organizations) to boost ‘abstraction leverage’ (i.e. simple instructions that kick off complex behaviours).
Because actions are expensive today, you need to spend a lot of upfront time in planning mode (e.g. writing software specs, outlining SOPs for contractors to follow, system design). The promise of agents is that planning gets subsumed into action, and the AI can go off and iterate until it achieves some objective (“Action produces information”). This is much messier and error prone than the current way of doing things, but plausibly more likely to find the optimal solution5.
Finding an optimal course is a tree search problem, when action is expensive you need to plan more and use heuristics to prioritize how the tree gets explored. When actions are cheap and fast you just explore the whole tree.
So what remains for humans to do in this new type of organization?
Gathering rare context for the machine
Setting the ‘system prompt’ of the organization; taste and preferences
Managing by exception (out of context problems)
Maintaining relationships
Oversight e.g. visual dashboards of what is happening, checking value alignment
These new jobs will require new tools, processes, and decision interfaces. Even if this never comes to pass in practice, executives and capital are placing bets along these lines; I’m already talking to companies who are rethinking how they design their organizations to take advantage of the capabilities afforded by this technology. The risk of inaction is that a competitor will do to them what AlphaStar did to the professional StarCraft players: win by doing more stuff.
Are you a software developer interested in building agent orchestration platforms? Email me at alex@convoke.bio
Incidentally, I think RTS user interfaces are fertile inspiration for LLM agent management platforms
There’s also a concept of effective APM (eAPM), which attempts to remove repetitive (spammy) actions that don’t progress the game state, but this is hard to measure and in practice spam doesn’t help win so
Some players maintain that winning at RTS games boils down to ‘clicking buttons faster’ and ‘who can move around the mouse faster’, and that the genre lacks the truly refined skill found in turn-based strategy games, like chess.
Of course, not every task is amenable to a “move fast and break things” approach e.g. anything that touches patient wellbeing in pharmaceuticals and healthcare will continue to need a careful approach