BairesDev

05/29/2026

An engineer on your team now ships in a day what used to take a sprint. The gap between a good engineering team and a great one isn't really about talent or culture anymore. It's about how fast a team learns to work differently.

Most takes on this fall into two camps. One says AI rewrites the rules of high-performing teams. The other says the fundamentals are eternal, ignore the noise.

In his latest piece, our Fellow, Michael Goldstein, President and CTO of Balto, argues that neither camp tells the full story.

The four principles that have always defined high-performing engineering teams (ownership, commitment, low drama, and looking inward) still hold. They are the floor. They used to be the ceiling, too.

Teams pulling ahead right now are building two new disciplines on top of that foundation: treating AI fluency as a continuous core competency and orienting around step-function gains rather than incremental ones.

➡️ Read the full piece: https://bit.ly/4wZimEU

05/28/2026

79% of senior tech leaders say they're pressured to overstate AI progress. That's what our AI Ex*****on Gap survey found, based on responses from 501 U.S. decision-makers running active AI initiatives at their companies.

The higher up you sit, the more directly the pressure lands. 57% of C-level executives say it comes from the C-suite or board. Among directors, the figure drops to 42%.

When we asked the same leaders what's actually blocking AI ex*****on, the most frequently mentioned reasons were infrastructure and governance-related friction: security and compliance (51%), data readiness (46%), legacy systems (43%), and a lack of specialized talent or expertise (32%).

Read together, the two findings describe a structural pattern. 83% of respondents plan to increase AI spending over the next 12 months, while the foundations underneath aren't keeping pace. The friction doesn't go away when the reporting layer flattens it.

➡️ Read the full breakdown of what the data shows, and what the response actually looks like: https://bit.ly/4fI1nko

05/28/2026

Procurement isn't underperforming because it lacks talent or headcount. It's saturated. Buried in low-leverage work that consumes the capacity that should be going to strategy.

That's the reframe our Fellow, Keith McFarlane, CTO at Globality, argues for in his new piece. His diagnosis goes deeper than bureaucracy. Procurement sits at the intersection of fragmented data, human-heavy decisions, and constantly shifting constraints.

If saturation is the problem, then the typical responses (more headcount, more tools, tighter controls) don't fix it. They make a saturated system more brittle.

Keith's argument is that AI changes this only when it's architected to remove low-leverage work, not when it's layered on top as another interface.

➡️ Read the full piece: https://bit.ly/4u0jIws

05/20/2026

Some teams are getting 10x returns from AI coding tools. Most, though, are getting marginal gains or worse. What separates them is the state of the codebase that those tools are operating on.

Tech debt was a rational choice for twenty years. "We'll refactor later" reflected real constraints, not weak discipline. It was optimization under conditions that genuinely existed.

What has changed is that those constraints no longer hold. AI has inverted the math on both sides of the rigor equation.

The return on a clean codebase has gone up sharply because AI runs reliably on code structured for it and struggles on code that is not. And the cost of getting to a clean codebase has dropped because the cleanup work teams have always wanted to do is exactly the kind of work AI handles well.

Our Fellow, Bryon Jacob, Co-Founder and CTO of data.world, walks through the new economics in his latest article. After 27 years in software engineering, he argues that the investment leaders could never quite justify is now the highest-ROI move an engineering leader can make.

Read the full piece here: https://bit.ly/43k6waO

05/15/2026

You shipped an AI feature last week. It still works. This morning, with no code change, it returns a summary that drops the one detail that mattered. The customer was disputing a duplicate charge. The summary now simply reads "account support." No crash. No alert. Nothing in the logs.

This is the failure mode our Fellow, Rob Teegarden, CTO at clearer.io, opens his latest piece with. Traditional QA was built for deterministic systems. AI features aren't deterministic, and silent regressions are the cost of testing them as if they were.

His argument is that the fix isn't more manual QA. It's a discipline borrowed from classical software engineering: evaluations. Repeatable test suites that score whether behavior meets a quality bar, versioned like code, run as regression gates before every release.

Rob's reframe: stop asking whether the output is identical. Start asking whether it meets the bar.

➡️ Read the full piece: https://bit.ly/4eQfR1m

05/01/2026

BairesDev was named a Gold and Bronze The Stevie Awards winner in The 24th Annual American Business Awards®, selected from more than 3,700 nominations.

🥇 Gold went to CTO Justice Erolin for Technology Executive of the Year.

Under his leadership, AI adoption across BairesDev's engineering organization was built as a discipline: define quality inputs, question confident-sounding outputs, and retain human accountability at every step.

That approach extended to ~50 client-facing AI solutions, including projects that cut legal document preparation from hours to minutes and reduced cloud infrastructure costs by 80%.

🥉 Bronze recognized BairesDev's Human Plus AI operating model in the AI-Driven Culture of Innovation category.

The model processed 2.5M+ applicants through AI-powered vetting, kept turnover below 11% with ML-driven retention tooling, and grew its peer-led learning community, Circles, by 300% in 2025.

Both awards reflect the same underlying belief: AI adoption works when it's built around how people actually work, not when it's layered on top.

➡️ Learn more: https://bit.ly/4tKJeqh

04/28/2026

Most conversations about kids and screens are about setting limits. Fewer are about what good screen time actually looks like.

That's the framing Justice Erolin, BairesDev's CTO, brought to a recent Business Insider piece on how tech leaders are setting tech rules for their own families.

His household has a one-hour screen-time cap. But the harder line sits inside that hour: short-form content is out, because of its effect on attention span. He's more open to gaming.

An hour spent in a game can teach teamwork, reaction time, problem-solving, grit, and how to deal with defeat. An hour spent scrolling short-form video doesn't.

➡️ Read the full piece from Business Insider: How tech CEOs and leaders balance AI, gaming, and social media for their families https://bit.ly/4vXjVCN

Tech CEOs Peter Thiel, Mark Zuckerberg, and others focus on creative tech use for kids rather than screen time limits. Short-form video is a worry.

04/27/2026

Most enterprises invest in AI backwards.

The work of making AI pay off is roughly 70% people and process change, 20% tooling, and 10% models. Budget and attention tend to flow in the opposite direction.

That's the observation our Fellow, Arun Nandi, Chief Data & AI Officer at Carrier, lays out in his latest piece.

After two decades running data and AI programs at Fortune 500 companies, his read is direct: most AI failures trace back to process redesign, change management, and incentives. That work stays underfunded while budget chases models.

Research from MIT Sloan backs the pattern. 70 to 90% of AI initiatives fail to scale into recurring operations. The breakdown point is rarely technical.

If the model works in a POC but doesn't reach the P&L, the fix usually isn't more fine-tuning. It's better data pipelines, redesigned workflows, clear ownership, and the operating discipline to embed AI into how teams operate.

Read the full article: https://bit.ly/3ON7qZO

04/23/2026

56% of developers say critically evaluating AI-generated output is the most essential baseline skill for developers in 2026. 67% say their teams don't have the knowledge to do it.

That gap is where the next set of engineering challenges is showing up.

Not in the tools, but in the validation layer beneath them.

Our Q1 Dev Barometer asked how engineers are working with AI. 87% say they're comfortable using AI in their role, and the productivity gains are widely recognized.

What's still evolving is how teams take ownership of AI output, and how consistently they validate it. This requires context, judgment, and time allocated for review, and it's a muscle many teams are still building.

Justice Erolin, our CTO, wrote about this for DEVOPSdigest. His argument: as AI takes on more of the ex*****on, accountability for the outcome stays with the developer — which means validation, not acceleration, is where the next competitive edge will be built.

➡️ Read the full article: https://www.devopsdigest.com/ai-is-making-engineering-accountability-visible

04/16/2026

AI tools generate code far faster than any human reviewer can meaningfully process it, turning traditional code review into a bottleneck, not a quality gate.

And with less human translation in the middle softening a vague spec, a sloppy requirement doesn't produce a bug. It produces an entire feature built in the wrong direction, at machine speed.

Our Fellow Ayman Shoukry, CTO at Specright Inc, argues the fix isn't faster reviews or more reviewers, but restructuring where human judgment gets applied. He lays out a two-tier model: humans review intent, and agents review generated code.

The pattern has precedent. The industry built trust in compilers and code generators the same way.

What's new is the implication for engineering teams: specification quality is now directly measurable. Not in theory. In production, at scale.

The question worth asking might not be whether AI-generated code is trustworthy, but whether the requirements feeding it are precise enough to warrant that trust.

➡️ Read the full article: https://bit.ly/4sCp2Wc