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Don’t Go Off Vibes Part 2 Signals a Hard Reset for AI Productivity Narratives
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Don’t Go Off Vibes Part 2 Signals a Hard Reset for AI Productivity Narratives

Enrich Events returns with “Don’t Go Off Vibes Part 2,” a data-focused conversation examining how AI is actually changing software development workflows.

Artificial intelligence has officially entered the phase where executive enthusiasm is colliding with operational reality. Boards want measurable returns. Founders want velocity. Engineering leaders want proof that AI copilots are improving software delivery instead of just flooding GitHub with cleaner-looking chaos. That tension is exactly why “Don’t Go Off Vibes Part 2: Data on how AI is changing software development” matters right now.

Hosted virtually by Enrich Events on May 28, the session continues a growing conversation around one of the most uncomfortable questions in enterprise AI adoption: what happens when AI increases activity metrics without clearly improving business outcomes? The event follows an earlier Enrich discussion featuring Span Field CTO Stephen Poletto, whose observations around AI-assisted software development cut directly against the dopamine-heavy narrative dominating much of the AI market. More pull requests. More generated code. More throughput. But also more review cycles, more rework, and more deployment churn. The software industry spent years treating speed as a synonym for value. AI is exposing how fragile that assumption actually was.

About “Don’t Go Off Vibes Part 2”

“Don’t Go Off Vibes Part 2: Data on how AI is changing software development” is positioned as a continuation of Enrich Events’ ongoing exploration into AI productivity measurement inside engineering organizations. The event itself is relatively small by design. Virtual. Curated. That matters more than it sounds.

Technology events have increasingly split into two categories over the past 18 months. One side sells spectacle: massive conferences, polished demos, synthetic optimism, and enough AI branding to make a GPU overheat. The other side operates more like a closed-door operator briefing with smaller rooms, sharper conversations, less theater, and more accountability. Enrich sits closer to the second category.

The organization describes itself as a private network for ambitious, growth-minded leaders investing in themselves and their companies. That framing aligns with the audience this topic naturally attracts: CTOs, VP Engineering leaders, platform executives, product operators, and senior engineers trying to separate measurable gains from executive hallucinations. Right now, nearly every engineering organization is facing the same pressure at slightly different scales: deploy AI faster, reduce costs faster, ship faster, and explain the ROI faster.

The problem is that software development does not magically become simpler because code generation accelerates. In many organizations, the friction simply migrates downstream into code review, integration, testing, governance, deployment, and long-term maintainability. AI removed typing friction. It did not remove organizational complexity. That distinction is becoming economically important.

Why AI Productivity Metrics Are Under Pressure

One of the central ideas emerging from the original “Don’t go off vibes” discussion featuring Stephen Poletto is that traditional software productivity metrics are becoming increasingly unreliable in the AI era. That sounds technical. It is actually political.

For years, engineering organizations leaned heavily on metrics like pull request volume, commits, sprint velocity, and story points because they were relatively easy to quantify. AI coding assistants now inflate many of those signals almost automatically. More code is being produced, but that does not necessarily mean more value is being created.

This creates a dangerous reporting environment inside companies where executives are under pressure to demonstrate successful AI adoption. A dashboard can look phenomenal while the underlying software lifecycle quietly becomes more fragile. The industry is beginning to realize that AI-generated acceleration can introduce hidden operational debt. Faster code generation may increase downstream review overhead. Larger pull requests can increase merge complexity. AI-generated abstractions can create maintainability risks that only surface months later.

That does not mean AI tooling lacks value. Far from it. It means the market is entering a more mature phase where operators are being forced to distinguish between productivity theater and durable execution improvement. That is a much harder conversation than posting screenshots of a chatbot writing boilerplate.

Why Enrich Events Matters in This Cycle

The AI conference ecosystem is becoming crowded with recycled narratives. Nearly every event promises transformation. Very few spend meaningful time discussing second-order operational consequences. That is what gives this Enrich session relevance.

“Don’t Go Off Vibes Part 2” is not centered on speculative AGI discourse, consumer chatbot hype, or abstract future-of-work monologues. The conversation lives in the uncomfortable middle layer where real engineering organizations are trying to make deployment decisions under uncertainty. That layer matters because infrastructure decisions made today shape hiring strategies, engineering org design, platform investments, and developer workflows for years.

The people paying attention to this discussion are not casual observers. They are the operators who eventually influence procurement decisions, internal AI governance, engineering policy, and enterprise tooling adoption across broader technology ecosystems. That makes these smaller, data-oriented forums disproportionately influential relative to their public visibility.

The software industry historically rewards optimism first and accountability later. AI is compressing that timeline.

What This Signals About Enterprise AI Adoption

The broader significance of “Don’t Go Off Vibes Part 2” extends well beyond software development metrics. The event reflects a larger shift happening across enterprise AI adoption: organizations are moving from experimentation toward operational scrutiny.

During the first wave of generative AI adoption, simply deploying AI tooling was often treated as strategic progress. That phase is ending. Companies are now being forced to answer harder questions: Does AI materially improve delivery quality? Does it reduce costs long term? Does it improve customer outcomes? Does it create maintainability risks? Does it alter team structure economics?

Those questions are no longer theoretical. They are budget questions. Hiring questions. Governance questions. Board questions. The market is slowly realizing that AI adoption without measurement discipline becomes corporate astrology remarkably fast. That realization is reshaping how sophisticated operators evaluate tooling, workflows, and engineering performance itself.

Frequently Asked Questions

What is “Don’t Go Off Vibes Part 2”?

“Don’t Go Off Vibes Part 2: Data on how AI is changing software development” is a virtual event hosted by Enrich Events focused on data-driven analysis of AI’s impact on software engineering workflows.

Who is hosting the event?

The event is hosted by Enrich Events, a private leadership-focused network for growth-minded operators and executives.

Is Stephen Poletto confirmed for Part 2?

Stephen Poletto, Field CTO at Span, was featured in the original “Don’t go off vibes” discussion. Public event materials for Part 2 do not currently confirm additional speakers.

Why does this event matter for engineering leaders?

The session focuses on measurable operational impact from AI coding tools, including productivity metrics, software quality, review cycles, and organizational workflow changes.

What broader trend does this reflect?

The event reflects a larger enterprise AI shift from experimentation and hype toward operational accountability, governance, and measurable business outcomes.

Who should pay attention to this event?

CTOs, VP Engineering leaders, platform teams, product executives, founders, and senior engineers evaluating AI-assisted development workflows should closely monitor discussions emerging from this event.