Optimising for Trust: The Hidden Edge of High-Performing Software Engineering Teams

When Google launched Project Aristotle in 2012 to decode team success, it studied 180 teams and found a surprising truth: Psychological safety—a dynamic rooted in trust—outranked raw skill as the key to performance (Duhigg, 2016). In software engineering, where velocity often steals the spotlight, this finding challenges the norm. Teams sprint to ship features, squash bugs, and meet deadlines, their worth tallied in commits and releases. Yet, the best teams prioritise trust. It binds collaboration, sharpens communication, and sustains quality, paving the way for enduring success. Trust isn’t flawless—it’s slow to build, quick to break, and tough to balance. Here, I'd like to explore why trust is the ultimate optimisation target for engineering teams, unpacking its power and pitfalls with real-world stakes.

Trust begins with safety, a principle tied to high-performing teams by Amy Edmondson (2018). When engineers know their ideas won’t be ridiculed or their errors weaponised, they step up. At Pixar, the “Braintrust” meetings exemplify this: animators and directors pitch rough cuts, trusting peers to critique candidly, a process that transformed Toy Story from a shaky draft into a groundbreaking film (Catmull & Wallace, 2014). In software, trust plays a similar role. Microsoft’s handling of Windows Vista’s early bugs showed this: teams created safe spaces to flag issues without blame, reducing defect rates over time (Sinofsky & Iansiti, 2009). Low-trust teams, by contrast, bury problems, letting glitches compound. Trust doesn’t just lift morale—it drives innovation by embracing diverse angles.

Communication, software’s lifeline, hinges on trust too. Complex systems like microservices or real-time APIs demand flawless alignment on specs and trade-offs. Trust lets engineers speak freely. Amazon’s 2002 shift to microservices faltered when teams didn’t clarify dependencies upfront, delaying rollouts; once trust grew—via shared docs and candid syncs—progress smoothed out (Vogels, 2006). History underscores this: NASA’s 1999 Mars Climate Orbiter crashed because two teams, misaligned on units (metric vs. imperial), didn’t bridge the gap—a $327 million communication failure (NASA, 1999). Trust could’ve saved it. Today, it curbs technical debt, as Martin Fowler (2019) notes, by catching errors early. With product managers, trust ensures engineers build right, not just fast, solving real user needs.

Code quality follows suit. In high-trust teams, reviews and pairing become shared stewardship. Google’s study of its review process found that trust-driven feedback catches bugs early and keeps systems lean; when the Chromium team overhauled its rendering engine, trusted collaboration ensured stability despite massive changes (Sadowski et al., 2018). Low-trust setups falter differently. At Uber, a 2016 rush to ship a payment feature skipped reviews, triggering outages that took weeks to fix (Isaac, 2017). Fowler’s technical debt lens warns of this: haste breeds flaws that cripple later (Fowler, 2019). Trust makes code a team asset, not a solo burden.

So why isn’t trust universal? It’s a slow build. Consistency, openness, and empathy clash with “ship now” pressures. Patrick Lencioni (2002) calls trust the bedrock of teamwork; skip it, and cracks form. The Therac-25, a 1980s medical device, proved this darkly: engineers under deadline stress bypassed trust rituals, missing fatal bugs that killed patients (Leveson & Turner, 1993). Contrast that with SpaceX: pre-launch huddles and peer checks—trust practices—kept Falcon 1 flying after early failures (Vance, 2015). Short-term wins can cost talent dearly: replacing a skilled engineer can cost up to 6-9 months of their salary covering recruitment, onboarding, and productivity dips. In tech’s talent wars, that churn cripples momentum, as new hires take months to master complex codebases. Trust, though, builds staying power. It keeps people from jumping ship and fosters resilience—teams that trust each other rebound from setbacks and stick together under strain.

Fragility hits harder in 2025’s remote world. Digital tools like Slack dominate, but we know that virtual distance strains bonds. A harsh comment or broken promise stings more online. Keith Ferrazzi (2020) pushes deliberate trust moves—video syncs, clear behavioural norms, team rituals. Post-2022 layoffs, Twitter’s remote engineers floundered without structured check-ins, missing deadlines until async “pulse” surveys rebuilt rapport (Roose, 2022). At GitLab, an all-remote leader, handbook-first clarity and open forums keep trust alive across time zones (Sijbrandij 2021). Recovery is tough, demanding grit from all sides.

Then there’s complacency. Trust feels cozy, but without rigor, it softens edges. Knight Capital’s 2012 trading glitch is a stark lesson: a trusted but untested algorithm update, deployed without proper checks, lost $440 million in 45 minutes, tanking the firm (Securities and Exchange Commission, 2013). Lencioni (2002) pairs trust with responsibility for a reason: solo, it flops. At Volkswagen, the 2015 emissions scandal grew from a “trust culture” that dodged accountability—engineers fudged tests, unchallenged (Ewing, 2017). Trust shines when earned and tested, not presumed.

Velocity grabs headlines, but trust fuels the machine. As software engineering charges toward AI automation and global teamwork in 2025, trust isn’t optional—it’s the edge that splits good from great. It ignites breakthroughs, aligns voices, and fortifies code against entropy. Yes, it’s slow. Yes, it’s fragile. And yes, it needs accountability. Invest in it, and you get a team that thrives under fire, adapts fast, and outlasts rivals. In a field rife with burnout and churn, trust isn’t soft—it’s the toughest, most vital asset there is.

References

• Catmull, E., & Wallace, A. (2014). Creativity, Inc.: Overcoming the unseen forces that stand in the way of true inspiration. Random House.
• Duhigg, C. (2016). “What Google Learned From Its Quest to Build the Perfect Team.” The New York Times Magazine.
• Edmondson, A. (2018). The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth. Wiley.
• Ewing, J. (2017). Faster, higher, farther: The Volkswagen scandal. W.W. Norton & Company.
• Ferrazzi, K. (2014). “Getting Virtual Teams Right.” Harvard Business Review.
• Fowler, M. (2019). “Technical Debt.” martinfowler.com.
• Isaac, M. (2019). Super pumped: The battle for Uber. W.W. Norton & Company.
• Leveson, N. G., & Turner, C. S. (1993). An investigation of the Therac-25 accidents. Computer, 26(7), 18-41.
• Lencioni, P. (2002). The five dysfunctions of a team: A leadership fable. Jossey-Bass.
• NASA. (1999). Mars Climate Orbiter mishap investigation board report. National Aeronautics and Space Administration.
• Roose, K. (2022). A verified mess: Turmoil from Twitter to FTX. New York Times
• Sadowski, C., et al. (2018). “Modern Code Review: A Case Study at Google.” IEEE Software.
• Securities and Exchange Commission. (2013). In the matter of Knight Capital Americas LLC, File No. 3-15570.
• Sijbrandij, S. (2021). The GitLab handbook. GitLab.
• Sinofsky, S., & Iansiti, M. (2009). One strategy: Organization, planning, and decision making. Wiley.
• Vance, A. (2015). Elon Musk: Tesla, SpaceX, and the quest for a fantastic future. Ecco.
• Vogels, W. (2006). A conversation with Werner Vogels. ACM Queue, 4(4).

I want to thank Ben Paddock , Malcolm Landon , and Laura Unwin for their feedback on drafts of this piece.