A Game-Theoretic View of High-Skill Tech Hiring
Why recruiting researchers at labs like Midjourney looks less like a job board and more like a repeated auction with hidden values.
When people think about hiring researchers in cutting-edge AI labs, they often imagine a simple contest: find the smartest person and offer them the highest salary. But the real strategic environment looks much more like a repeated, incomplete-information game than a straightforward job market.
Consider a company like Midjourney, which competes with well-funded labs for a limited pool of top research talent. Each candidate has a “type” — their true skill, creativity, and fit with the lab’s culture — but firms see only noisy signals: a publication record, a GitHub repository or portfolio, a few hours of interviews, and some references. At the same time, candidates observe only fragments of each firm’s working style, career prospects, and research freedom. Both sides are guessing about the other, and those guesses shape strategy.
The hiring game as a simple matrix
From the firm’s perspective, making an offer is a bit like bidding in an auction. Bid too aggressively and you risk overpaying for someone whose contribution is uncertain. Bid too conservatively and you repeatedly lose strong candidates to rivals. Even this stylized 2×2 view is enough to see why “just pay more” is not a sustainable strategy.
A simple hiring game
Imagine Midjourney choosing between aggressive and conservative offers, while rival labs do the same. Click a scenario to see how the outcome changes.
Understanding the payoff matrix
This 2×2 matrix represents a simplified version of the hiring game. Each cell shows the outcome when Midjourney and rivals choose a particular combination of strategies. In a full game-theoretic treatment, each cell would contain numerical payoffs for both players, and we'd look for Nash equilibria—combinations where neither player wants to unilaterally deviate.
The key insight is that there's no single "best" strategy that works in all scenarios. When rivals are conservative, being aggressive can win you candidates at reasonable cost. When rivals are aggressive, matching their aggression leads to overpaying (the "winner's curse"), but being too conservative means losing talent. This is why mixed strategies often emerge in practice.
The real world, of course, has many more strategies than "aggressive" versus "conservative": labs can differ on salary, equity, freedom to publish, compute budgets, and how closely work ties to the product. But the core trade-off is similar. You want to reserve your most aggressive bids for the candidates who make the biggest difference inside your specific environment — the ones whose type is especially well matched to what you are building.
Information and salary dispersion
A useful way to see this is through information. In markets where no one really knows what others are paying, offers for similar roles can be all over the place. Some firms underpay and quietly lose good people. Others overpay without realizing it, and only discover the mistake later when performance reviews arrive or budgets tighten.
As labs get better data — from salary benchmarking tools, ex-employees, or just repeated interaction with the market — those guesses get sharper. Offers converge toward a common range, and what used to look like “random” wage dispersion turns out to be the result of uncertainty rather than deep differences in worker value.
How information changes salaries
Drag the slider to move from a world of very noisy information about salaries to one with precise benchmarks. The dots represent offers for similar researchers.
In the low-uncertainty world, everyone can roughly see the same going rate. At that point, the game shifts. It is no longer about whether you can exploit pockets of ignorance in the market. Instead, it becomes about whether you can offer the best bundle of salary, equity, autonomy, and mission for the particular researchers you care most about.
Reputation in a repeated game
Hiring is not a one-shot game. The way a lab treats researchers today quietly broadcasts signals to tomorrow’s candidates. That makes reputation a strategic asset: something you can invest in, squander, and rebuild — but not cheaply.
Reputation as a long-run strategy
Click through the simple timeline below to see how the same lab can move through very different equilibria depending on how it behaves.
A lab that consistently honours what it promises — around autonomy, promotions, or the chance to ship impactful work — can sometimes pay slightly less cash than its rivals and still win the candidates it cares most about. A lab that burns trust, on the other hand, ends up paying a “reputation tax” in higher salaries or weaker pipelines for years.
Once you start thinking in this way, it is natural to ask what the “resting points” of the game look like. In game theory language those resting points are Nash equilibria .
How the game settles: Nash equilibria
Which hiring regime are you in?
Think of the broader environment as defined by two things: how uncertain salaries are, and how strong your lab’s reputation is. Different combinations give rise to different stable patterns of behaviour. Use the sliders to describe your world; the card below shows the kind of equilibrium you are likely sitting in.
High uncertainty and weak reputation put you in a noisy arms race: everyone bids aggressively, overpaying is common, and small shocks move the market a lot.
- Offers swing widely for similar candidates.
- Labs feel pressure to "match whatever others are paying," even without good data.
- It is easy to burn budget without clearly improving the team.
For readers who like the formal game-theory view
Formally, you can think of each "regime" as a region of parameter space where a particular pattern of strategies is a Nash equilibrium or close to it. When uncertainty is high and reputation is weak, aggressive bidding by most labs is self-reinforcing: any lab that unilaterally switches to cautious offers loses candidates.
When information is precise and reputation is strong, a calmer profile of offers becomes self-reinforcing instead: deviating to constant overbidding is costly, because it no longer buys you much extra talent. The grid is not meant to pin down a single "true" equilibrium, but to show how small shifts in information or reputation can move you between regions where very different behaviours are stable.
Candidates as strategists
None of this is one-sided. Candidates also play a game. They choose how much to reveal, when to surface competing offers, and whether to present themselves as “pure researcher,” “staff engineer,” or something in between. Done skillfully, this is a form of signalling: shaping the lab’s beliefs about their type and nudging the equilibrium toward higher offers or more freedom.
The best outcome for both sides is not maximum drama at the negotiating table. It is a matching process where information flows clearly enough that aggressive bids are reserved for genuinely exceptional fits — people who will thrive in the lab's specific environment and push the frontier forward rather than simply moving headcount numbers.
This brings us to an interesting prediction from game theory: in many strategic situations, the optimal approach is not to always play one strategy, but to mix strategies. A lab might bid aggressively for some candidates and conservatively for others, or even mix approaches for the same candidate type depending on what rivals are doing. The question is: when does each strategy make sense?
Mixed strategies in practice
To see how this works, we need to define what we mean by "payoff." In this simplified model, payoff represents the net value to Midjourney from a hiring decision: the benefit of getting a strong candidate (or avoiding a bad fit) minus the cost of the offer and the risk of overpaying. Positive payoffs mean the strategy is working well; negative payoffs mean it's costly or ineffective.
The payoff depends on both Midjourney's choice and what rivals do. Here's the logic behind each scenario:
- Midjourney aggressive, rivals conservative: Midjourney wins the candidate with a strong offer, and the cost is justified because rivals weren't bidding hard. Payoff: +3
- Both conservative: Salaries stay reasonable, but Midjourney might miss some candidates. Payoff: +1
- Midjourney conservative, rivals aggressive: Midjourney loses good candidates to rivals who outbid them. Payoff: -1
- Both aggressive: Everyone bids high, leading to overpaying and winner's curse. Payoff: -2
The chart below shows Midjourney's expected payoff from each strategy as we vary how often rivals play aggressively. When rivals are rarely aggressive (left side), aggressive bidding pays off. When rivals are often aggressive (right side), conservative bidding avoids the worst outcomes. Move the slider to see how the optimal strategy changes.
At this level of rival aggression, the payoffs tell us which strategy is better. When the lines cross, both strategies are equally good—this is the mixed-strategy equilibrium point. At other levels, one strategy clearly dominates, but the optimal mix depends on reading the competitive landscape.
For readers who want the algebra
Let q be the probability that rivals play aggressively. The expected payoff when Midjourney plays aggressively is:
E[Aggressive] = 3 · (1 − q) + (−2) · q = 3 − 5q
The expected payoff when Midjourney plays conservatively is:
E[Conservative] = 1 · (1 − q) + (−1) · q = 1 − 2q
The mixed-strategy equilibrium is at the point where Midjourney is indifferent between the two strategies, so we set the payoffs equal:
3 − 5q = 1 − 2q, which implies q = 2⁄3.
At this point, Midjourney cannot improve its expected payoff by switching entirely to aggressive or conservative bidding, given what rivals are doing. That is exactly the sense in which a mixed strategy is an equilibrium: each pure strategy has the same expected value, so randomising between them is sustainable.
Note: This is a simplified model. In reality, payoffs depend on many factors: candidate quality, market conditions, budget constraints, and long-term reputation effects. The negative payoff when playing conservative against aggressive rivals reflects the cost of consistently losing strong candidates—not just the immediate loss, but the opportunity cost of missing talent that could drive the lab forward.
From theory to practice: turning the game into tools
Taken together, the frictions above explain why labs overbid, underbid, or oscillate between the two. The formal results point to a simple fact: hiring outcomes change when you change the information structure, the payoff structure, or the repeated-game incentives. A lab doesn’t need to solve the full equilibrium; it just needs to shift the game enough so that the better equilibrium becomes the natural resting point. Three practical levers matter most.
1. Replace one-size-fits-all bidding with match-weighted segmentation
Theory link: In both the matrix game and the mixed-strategy discussion, aggressive bids only make sense when the expected payoff is high relative to rivals’ behavior and candidate type. Without segmentation, labs misallocate aggression and drift into the “aggressive–aggressive” loser’s equilibrium.
Low-hanging fruit:
- Build a simple two-axis tagging system for candidates:
fit with research agenda × expected marginal impact on product. - Use these tags to create three buckets: priority fits, promising, misaligned-but-strong.
- Pre-commit to aggressive offers for the priority group and conservative baselines for the others.
- Review the bucketing once per quarter, not case-by-case (keeps managers from emotional overbidding).
Outcome: The lab systematically reserves scarce bidding power for candidates where it has an actual comparative advantage, mirroring the optimal mixed strategy.
2. Turn rejections and near-misses into a Bayesian update loop
Theory link: On the incomplete-information side of the model, the biggest inefficiency comes from labs not knowing the distribution of rival offers or candidate preferences. Payoffs improve as uncertainty drops. A rejection is a noisy but informative signal.
One approach:
- Record a four-line post-mortem for every declined offer:
- What the candidate said they valued most.
- What they felt the offer lacked.
- Whether rivals outbid, out-signaled, or out-reputed.
- Whether a different package could have plausibly changed the outcome.
- Aggregate these monthly into a “belief update” doc with recommended adjustments to offer ranges or autonomy promises.
- Use this to maintain a rolling expected market rate for each candidate segment.
Outcome: The lab converges toward the low-uncertainty regime where offers cluster rationally and the equilibrium stabilizes at fit-and-reputation rather than pure cash escalation.
3. Manage reputation like an asset with depreciation and compounding
Theory link: In the repeated-game view, reputation shifts future payoffs by reducing what the lab must bid to win. A shock—broken promise, chaotic re-org—pushes the game into worse equilibria with systematic overpaying: the “reputation tax.”
This could be cool to implement:
- Create a promise ledger: autonomy, research freedom expectations, compute access, promotion timeline, (hiring speed!). Track how often commitments slip.
- Collect exit reasons with a consistent template.
- Build a quarterly reputation forecast:
What promises did we keep? Which did we break? Are recent alumni net-positive or net-negative signalers? - Assign a simple internal metric (green/yellow/red) that determines how much “reputation discount” you can apply safely to offers.
Outcome: The lab actively maintains the conditions of the high-reputation equilibrium, where candidates accept slightly lower cash for clarity, autonomy, and trust— exactly what the model predicts.
These tools share a structure: a game-theoretic insight, the friction it explains, and a mechanism that shifts the equilibrium. They turn abstract models into practical levers for reducing wasted bids, sharpening beliefs, and steering the hiring game toward states where match quality and trust dominate blind escalation.
Why this matters for labs like Midjourney
What recruiting teams often treat as "operations" are, in practice, mechanisms that reshape incentives, clarify information, and strengthen the repeated-game dynamics that make hiring more predictable. When those mechanisms are in place, the system drifts toward the calmer, high-reputation, fit-driven equilibrium rather than the expensive, noisy one. That is the real payoff of seeing hiring as a strategic game: it reveals exactly which levers move outcomes, and which simply raise costs. The sooner a lab starts operating in those terms, the less it will need to rely on luck—or spiraling salaries—to assemble the team it actually wants.
Thanks for reading!
© Peter Flo