Coder Legion - Recent posts tagged engineeringleadership

AI Coding Tools Are Making Engineers 10x Faster. Here's the Catch.

Fri, 12 Jun 2026 11:22:20 +0000

Todd Fisher, CEO and co-founder of CTM, explains why speed is only part of the story.

The "10x engineer" idea has been around for a long time. It's the notion that the best developers don't just work harder — they work at a fundamentally different level than everyone else. AI coding tools have brought that concept back into sharp focus. But Todd Fisher, CEO and co-founder of CTM, thinks the framing needs an update.

"If an engineer becomes 10x more productive because of AI, their competitor's engineer is probably becoming 10x more productive too," Fisher said. "In a competitive market, that doesn't suddenly mean you need fewer engineers. It means you're competing at a different level."

That's a grounded take in an industry that tends toward hype. The baseline is shifting — not just for a few high-performing teams, but for everyone. And the organizations that adapt to that new reality fastest are the ones most likely to pull ahead.

Beyond Code Generation

Most developers have tried an AI coding assistant at this point. Many have also hit a familiar wall: the suggestions are decent, but they don't feel transformative. Fisher says the gap between "okay" and "genuinely useful" comes down to context.

"The more context you can provide, the more capable the system becomes," he said. "It can help navigate information, call additional tools, and surface answers that would otherwise take time to find."

The tools that are delivering real value, in his view, are the ones connected to the actual environment where work is happening — not standalone chat interfaces, but assistants embedded in the workflow itself.

At CTM, that plays out in error reporting. When a defect occurs, AI begins analyzing the issue immediately, traces it back to the relevant code, and suggests a potential fix — sometimes before a developer has even been notified.

"The reaction I sometimes have is, 'How did we survive without this?'" Fisher said. "It eliminates the repetitive work that used to slow teams down when something went wrong."

The Technical Debt Question

One concern that comes up consistently in developer conversations: does AI-assisted coding just accelerate the accumulation of technical debt? Write faster, break things faster.

Fisher sees it differently. As AI tools get more deeply integrated into software products, they develop a more detailed understanding of how those products actually work. That creates the conditions for better support, earlier defect detection, and higher-quality outcomes overall.

"Hopefully we'll see the quality of support go way up, and the number of defects go way down," he said.

That's not guaranteed, of course. But it points to a more optimistic trajectory than the "move fast, clean up later" pattern that has defined much of software development for the past decade.

What to Actually Measure

For engineering leaders trying to evaluate whether AI tools are paying off, Fisher recommends keeping the metrics simple.

Feature activation rates. Error rates. Time from defect detection to proposed fix. These are the numbers that reflect whether engineers are actually accomplishing their goals more effectively — not just shipping code faster.

"The aim is to measure whether people are accomplishing their goals more effectively and with fewer errors," he said.

Human Judgment Still Matters

The skills question is real. If AI can generate code, write tests, and debug errors, what does that mean for the engineers doing those tasks today?

Fisher draws a useful analogy: "A doctor might be able to evaluate possibilities faster with AI, but deciding which possibility is actually correct still requires judgment."

Engineering is no different. The ability to evaluate tradeoffs, understand context, and make decisions remains valuable. AI surfaces options — humans still have to choose.

The Bottleneck Nobody's Talking About

Here's where Fisher gets candid. AI tools can generate code fast. Very fast. But someone still has to review it.

"I have multiple projects that are nearly completed and largely written with AI assistance," he said. "But the bottleneck is finding enough time to individually review everything."

This is the underreported challenge of AI-assisted development. The creation bottleneck is largely solved. The review and validation bottleneck is not. Teams that figure out how to structure human oversight of AI-generated output — without slowing everything back down — will have a meaningful edge.

Where This Is All Headed

Fisher's final point is the one that should get the attention of anyone in the software industry.

"Some SaaS businesses will be disrupted because AI-assisted development makes it easier to build alternatives," he said. "We're going to see dollars move around the industry in ways that are difficult to predict."

That's a significant signal. AI coding tools aren't just changing how software gets built. They're changing the economics of building software at all — lowering the barrier to entry, accelerating feature development, and making it easier to challenge incumbents.

For developers who haven't fully committed to AI coding tools yet, Fisher's advice is practical: start using them and see where they fit naturally into your workflow. Treat them like having a knowledgeable colleague available at all times.

"You can ask questions, explore ideas, and get help working through problems," he said. "You don't have to treat them as a replacement for how you work today."

That's a reasonable starting point. The competitive landscape may eventually make it a necessity.

Is DevRel Dead or Just Disaggregated?

Thu, 07 May 2026 17:39:00 +0000

The "USB-C Moment" for Developer Experience

We’ve entered what I call the "USB-C Momentstrong text" for AI systems—a period of rapid standardization where the Model Context Protocol (MCP) is doing for AI tools what universal ports did for hardware. But this shift raises an existential question for those of us in leadership: If an LLM can write the boilerplate, explain the API, and debug the integration, is Developer Relations still relevant?

The short answer: The traditional "Tutorial-as-a-Service" model of DevRel is dead. The new model is just beginning.

From Documentation to Context-Provisioning

Historically, DevRel was the bridge between a complex product and a developer’s terminal. We spent our cycles on documentation, "Hello World" samples, and answering syntax questions. Today, developers are increasingly using agentic workflows to handle that "Glue Code".

In this era, DevRel's value prop shifts from content to context:

Context-First Engineering: Our job is no longer just writing docs for humans; it’s ensuring our systems are "model-ready." This means architecting MCP servers so that an AI agent can navigate our product's capabilities with high-reasoning synthesis.
The Auditor Role: We are moving into a "Forensic" era of advocacy. DevRel must act as the "Guardian," providing human-in-the-loop governance to ensure that synthetic code generated for our platforms is secure, ethical, and performant.
Systems Over Syntax: We aren't teaching people where the semicolon goes; we are teaching them how our tool fits into their "Enterprise Agentic Memory".

The Disaggregation of the Role

DevRel isn't disappearing; it’s being absorbed into the very fabric of the product.

Builders need us to provide high-integrity digital twins of our systems.
Technical Leaders need us to prove scalability and governance—the "Final Boss" of enterprise adoption.
Domain Operators need us to translate technical capabilities into resilient, real-world systems, like an "Agile Harvest" for agriculture or a "Sovereign Vault" for data.

The Verdict

If your DevRel strategy is still measured by the number of blog posts published, you’re trailing the curve. In the AI era, we are the Context Engineers. We provide the structured reality that prevents AI agents from hallucinating our product’s value.

What do you think?

Is your team still focused on "Developer" outreach, or have you pivoted to "Builder" relations? I’d love to hear how you're retooling your advocacy for 2026.

Your CTO can't tell you how much AI code you're writing—and that's a bigger problem than you think.

Tue, 16 Sep 2025 19:41:42 +0000

Why Your CTO Can't Tell You How Much AI Code You're Actually Writing

Span's new AI code detector reveals the measurement gap that's keeping engineering leaders in the dark about their AI transformation.

Engineering leaders are flying blind. They're making million-dollar decisions about AI coding tools based on guesswork, self-reported surveys, and vendor claims that don't match reality.

This disconnect became clear when I spoke with J Zac Stein and Henry Liu, co-founders of Span, about their new AI code detector. The tool can identify AI-assisted versus human-written code with over 95% accuracy—and it's revealing some uncomfortable truths about how we measure AI adoption.

The Numbers Don't Add Up

Google claims 25% of new code comes from AI. Microsoft says 30%. But when engineering teams try to verify these numbers internally, they hit a wall. Current measurement methods count lines of code suggested by AI tools, but they don't track what happens next. Was that code modified before commit? Deleted entirely? No one knows.

"Engineering leaders are facing immense pressure to demonstrate the value of AI investments, but they're making decisions based on anecdotal evidence," Henry Liu told me. This pressure comes from boards and executives who want concrete ROI metrics, not developer surveys.

The problem runs deeper than bad data. Companies are operating over a dozen different AI coding tools simultaneously—Copilot, Cursor, Claude, and more. Each tool provides different telemetry. Some provide none at all. And none of them can track the significant amount of AI-generated code that comes from developers copying and pasting from ChatGPT.

Beyond the Hype

The biggest misconception executives have about AI coding? That faster code writing automatically equals higher productivity. This assumption ignores critical factors like code review burden, technical debt, and defects in production.

"Writing code is not the only thing engineers do," Liu explained. "The longer the company, the less writing code is a bottleneck." Senior engineers spend more time on architecture decisions, coordination, and ensuring teams work on the right problems. A 30% speed increase in coding doesn't translate to 30% overall productivity gains.

This reality creates tension between believers and skeptics within engineering teams. Leadership pushes top-down AI adoption while senior individual contributors remain cautious. Without solid data, these debates rely on anecdotal evidence and personal preferences.

How the Detection Works

Span's solution centers on span-detect-1, a machine learning model trained on millions of code samples with known provenance. The model analyzes patterns in token sequences, syntax quirks, and stylistic regularities to determine AI versus human authorship.

The technical challenge was significant. When reasoning models like ChatGPT improved, the team had to dramatically increase their training data—by 10x—to maintain accuracy. Many research papers in this space don't properly evaluate their models, leading to inflated accuracy claims that don't hold up in real-world conditions.

Currently supporting Python, TypeScript, and JavaScript, the detector works across all AI coding tools. This tool-agnostic approach helps companies understand their true AI usage regardless of which specific tools their developers prefer.

What Companies Are Learning

Early customers are discovering adoption patterns that challenge assumptions. While senior engineers tend to be more reserved about AI tools, the differences aren't as stark as expected. Teams working on greenfield projects adopt AI more readily than those maintaining legacy codebases, but overall adoption is climbing across all segments.

For companies that have fully embraced AI coding tools, weekly usage rates average around 70%. Some engineers are spending hundreds of dollars daily on token costs. As Liu noted, "Token spend is going to be comparable to payroll spend in the future."

This shift transforms every engineer into a resource allocator. Understanding which AI tools provide the best ROI becomes critical for both productivity and cost management—similar to how companies had to learn cloud cost optimization.

The Bigger Picture

Span's AI detector represents just one piece of a broader developer intelligence platform. The company is building what Liu and Stein call "the tool we wish we'd had in our previous roles"—a comprehensive suite for understanding engineering productivity in the AI era.

Their approach goes beyond simple metrics. Span uses AI to understand developer productivity rather than relying on outdated measures like lines of code. The platform includes qualitative surveys to identify developer headwinds and tailwinds, cost capitalization tools, and upcoming features to compare defect rates between AI and human code.

"This type of tool was not possible three years ago," Liu said. "Span helps you lead your engineering organization through AI transformation with data, not hype."

As AI coding tools continue evolving rapidly, the need for objective measurement becomes more critical. Companies can't optimize what they can't measure. Span's detector provides the ground truth that engineering leaders need to make informed decisions about their AI strategy.

The future of software development is clearly AI-augmented. The question isn't whether to adopt these tools—it's how to do it intelligently, with data instead of hope.