Can Bakis· software engineering manager · ai lead
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CEO: “We'll be 20x Productive by Q4!”

The Gang Goes 20x Productive

·6 min read

The Gang Goes 20x Productive

For the full immersive experience, the theme song is here

The Chase for Productivity Is Real

By now, you've probably heard about the AI-first strategies and 10x productivity plans from leaders. I bet you're working toward a future where AI is the default way to build software. But how do you get there? How do you scale it across your organization?

Shopify, Duolingo, Fiverr, Meta, and others have publicly pushed AI-first strategies or expectations around employee AI use. Shopify famously went as far as requiring teams to demonstrate why AI couldn't accomplish work before asking for additional headcount.1

And here's a fascinating development from literally four days ago: Shopify CEO Tobi Lütke is now warning about what he calls "slop grenades"—employees using AI to produce large amounts of poorly considered work that then becomes somebody else's PR review nightmare. Shopify hasn't backed away from AI; Lütke says its internal agent may now handle as much as half of production-code PRs. It raises the question: "What happens to the rest of the organization when producing work becomes extremely cheap?"2

McKinsey's August 2026 survey shows the technology itself is already moving rapidly into organizations: 40% of respondents at large enterprises report scaling AI agents, and about 31% report scaling coding agents.3

On September 9, 2026, McKinsey published research specifically about AI and the operating model. Its conclusion is basically this:

Agentic AI requires organizations to rethink how work gets done, decisions are made, capabilities are developed, and value is created.

They explicitly argue there isn't one established "best" AI operating model yet.4

Deloitte's June 2026 study gives us the killer statistic:

Among 660+ technology executives, 81% said they could deploy and govern AI at scale today, while nearly 75% said their operating model would need to change within 12–18 months.

Deloitte literally calls this a "structural readiness gap."5 This is really an operating model problem.

You want adoption? YOU GET ADOPTION!

Companies are adopting AI much faster than they're redesigning work. Another 2026 Deloitte study says only 34% of organizations are truly reimagining the business around AI. More tellingly, companies have mostly responded to AI's workforce implications through training and AI fluency rather than redesigning roles and workflows. And only one in five organizations reports having mature governance for autonomous AI agents.6

Publicis Sapient's June survey of 1,550 AI decision-makers gives us another angle: 73% say AI is used regularly or across most business processes, but only 10% say AI is core to how the business actually operates.7

Well, we are in the messy middle of this AI adoption journey. Companies are figuring out how to drive AI adoption, and if you're in a churning business, that's a perfect recipe for mistakes.

Churn > Fast Decisions > AI-First > 20x Code > Layoffs > Too Many PRs > Rehiring

And guys, guys, listen: giving everyone Claude/Copilot/Codex isn't an operating model. 🤣

"Why Are Our Experienced Engineers Frustrated?"

DORA has almost hilariously perfect evidence. Their recent analysis says AI accelerates initial code generation, but engineers frequently spend part of the saved time on auditing and verification. They call this the "verification tax."8

Even better, their qualitative research explicitly identifies code review as a shifting burden. Individual developers can generate much larger changes faster, while reviewers still need to understand and verify the output. DORA reports higher AI adoption alongside both higher throughput and higher delivery instability.

Too Many PRs

The Operating Model Has To Change

And the funniest part is that management has spent ~15 years with a fairly comfortable library of answers:

Agile. Scrum. DevOps. Product teams. Platform engineering. CI/CD. UX research. Two-pizza teams. Spotify model. SAFe if you've committed some terrible sin in a previous life. 🤣

But now someone asks:

"How should a company operate when every employee has multiple semi-autonomous cognitive agents?"

Answer: 🤷

McKinsey says there isn't one best model yet, so we're genuinely in that experimentation period.4

Well, I have some ideas, check out my other article about AI Federations here. Until then, take care of yourselves, and don't get too caught up in the hype tornado. 😎

Footnotes

  1. Danielle Abril, “No AI, no job. These companies are requiring workers to use the tech”, The Washington Post, June 3, 2025. Reports on Shopify's April 2025 AI expectations and employee memo, alongside policies at Duolingo, Fiverr, Meta, and other companies. ↩

  2. Ben Shimkus, “Shopify CEO says employees' ‘slop grenades’ are making more work for everyone else”, Business Insider, September 17, 2026; based on Tobi Lütke's September 15 interview on The Knowledge Project. The article reports Lütke's description of unchecked AI output shifting review work to coworkers and says Shopify's internal agent River handles as much as half of production-code pull requests; that figure is his account, not an independent measurement. ↩

  3. McKinsey & Company, “The state of AI in 2026: On the road to ROI”, August 25, 2026. The online survey gathered 1,719 responses across 97 countries from May 4 to June 8, 2026. The adoption figures are respondents' reports; “large organizations” means more than $1 billion in annual revenue. ↩

  4. McKinsey & Company, “The key to AI value is hiding in plain sight: Your operating model”, September 9, 2026. Its findings draw on a panel survey of 701 executives and senior leaders fielded from December 2025 to February 2026. McKinsey says there is no single best operating model; the survey reflects individual respondents, not representative accounts of their organizations. ↩ ↩2

  5. Deloitte Insights, “Rewiring the enterprise operating model for AI scale”, June 29, 2026. In a survey of 662 senior technology leaders, 81% said they could deploy and govern AI at scale, while nearly 75% said their operating model would need to change within 12–18 months. These are leaders' reported confidence and expectations. ↩

  6. Deloitte, “The State of AI in the Enterprise: 2026 AI report”. The cited findings come from a survey of 3,235 senior leaders across 24 countries, fielded in August and September 2025. Deloitte reports that 34% are reimagining their business and one in five has mature governance for autonomous agents; this is a 2026 report based on 2025 fieldwork. ↩

  7. Publicis Sapient, “2026 Global Enterprise AI Report Reveals Gap Between AI Adoption and Enterprise Readiness”, June 17, 2026. Protégé surveyed 1,550 AI decision-makers from organizations with at least 500 employees and $100 million in annual revenue in six countries, from April 29 to May 14, 2026. The findings are self-reported. ↩

  8. DORA, “Balancing AI tensions: Moving from AI adoption to effective SDLC use”, March 10, 2026. DORA's analysis of enterprise software engineers describes verification and review burdens; its 2025 report reports an association between higher AI adoption, greater delivery throughput, and greater delivery instability. The association does not establish that AI caused either outcome. ↩

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