How AI systems accumulate technical debt differently
AI systems accumulate technical debt through mechanisms that traditional software engineering does not prepare you for. The debt is real, it compounds, and it shows up in ways that are harder to detect and more expensive to resolve than the technical debt most engineers are used to managing.
How to think about testing AI systems
Testing AI systems requires a different mental model than testing traditional software. The outputs are probabilistic, the failure modes are subtle, and the standard unit-test-to-integration-test pyramid does not map cleanly onto AI. Here is what a more useful testing framework looks like.
How to design AI systems that degrade gracefully
AI systems fail in ways that traditional software does not. The outputs are probabilistic, the failure modes are subtle, and the degradation is often invisible until it becomes a problem. Designing for graceful degradation is not optional for AI systems in production.
Why AI systems need version control for prompts
Prompts are the most frequently changed component of most AI systems, and most teams track them worse than any other code. No history, no rollback, no understanding of what changed between the version that worked and the version that does not. This is a solvable problem with known solutions.
How to run an AI proof of concept that actually transfers to production
Most AI proofs of concept succeed and most AI production deployments disappoint. The gap is not a mystery: POCs and production systems are built under different conditions, measured by different criteria, and staffed by different people. Closing the gap requires designing the POC differently from the start.
The AI infrastructure decisions that age poorly
Some infrastructure choices that seem reasonable when you are building your first AI feature become serious liabilities as the system matures. The teams that have been building production AI long enough have learned which decisions these are. Most teams learn them the hard way.
How to think about AI latency in product design
AI latency is not a single number and it does not behave like traditional API latency. The teams that design good AI-powered products understand what makes latency feel acceptable, what makes it feel broken, and how to design around the constraints that cannot be engineered away.
What good AI observability looks like
Traditional observability tells you if your system is up and how fast it is. AI systems need a second layer: is the output quality good, is it degrading, and why? The teams shipping reliable AI have built this layer. Most have not.
AI in regulated industries: what actually changes
The conversation about AI in regulated industries is usually framed as a conflict between innovation and compliance. That framing is wrong. The real constraint is not regulation but the specific requirements that regulation imposes, which are more tractable than they appear and sometimes work in AI's favor.
The quiet default: why most AI projects choose the safe option
Most AI projects make a conservative choice somewhere that limits what they can accomplish. The choice is rarely announced as conservative. It is presented as sensible, pragmatic, or appropriately scoped. Understanding why this happens is the first step toward making decisions that are actually right rather than merely defensible.
The AI talent market: what companies are actually competing for
The AI talent shortage most companies experience has almost nothing to do with AI researchers and everything to do with engineers who can ship AI products reliably. Understanding the actual shape of the talent market changes how you hire, how you retain, and where you invest in developing internal capability.
What makes an AI integration actually stick
Most AI integrations get adopted initially and abandoned quietly. The ones that stick share a set of properties that have less to do with AI quality and more to do with how the integration fits the workflow, builds trust, and earns a place in how people actually work.
How to evaluate AI tools before you buy
AI tools perform well in vendor demos. They perform less well when you run them on your actual data, your actual use cases, and your actual failure modes. The gap between demo quality and production quality is where most regrettable AI purchases originate.
What AI means for technical documentation
Technical documentation has a new audience: AI systems that consume it to answer questions, generate code, and assist with operations. That changes what good documentation looks like, which parts of the investment pay off, and where human writing still has no substitute.
How AI is changing software testing
AI tools are reshaping software testing in ways that go beyond generating test boilerplate. The more interesting changes are in what gets tested, who finds the gaps, and how teams decide what 'enough coverage' means.
What the best AI teams actually do differently
Most organizations that struggle with AI adoption are doing the obvious things. They have access to the same models, the same tools, and the same information. The differences that matter are almost never the ones that get written about.
How AI changes hiring in technical roles
The skills that distinguish strong technical candidates are shifting. Hiring processes that optimize for what candidates can build from scratch are increasingly misaligned with what makes a technical professional valuable when AI tools are available.
Why AI teams need a culture of evaluation
Evaluation infrastructure is a tooling problem. Evaluation culture is an organizational problem. Teams that build the tooling without changing how they make decisions discover that the tooling goes unused. The harder work is building the norms.
What to prioritize in your AI roadmap for 2027
Most AI roadmaps list capabilities the team wants to build. The ones that actually deliver value are organized around a different set of questions: where is the current system falling short, what infrastructure enables multiple use cases, and what can the organization realistically absorb?
The state of AI in 2026: what changed and what did not
2026 was a year of real progress in AI capability and significant noise about what that progress means. Here is an honest accounting of what actually shifted, what stayed stuck, and what that implies for the year ahead.
How to run an AI retrospective
Standard retrospective formats were designed for software development cycles, not AI systems. An effective AI retrospective reviews different dimensions, requires different data, and produces different outputs than a typical sprint retro.
The real cost of AI technical debt
AI technical debt accumulates differently than traditional technical debt and is harder to see until the costs become unavoidable. The shortcuts that look expedient in early AI deployments create compounding costs that most organizations are underestimating.
How to build AI adoption habits in a team
Most teams plateau at occasional AI use rather than reliable integration. The difference between sporadic adoption and habitual use comes down to where learning accumulates, how friction gets removed, and whether failure is processed or ignored.
Why AI features need different success metrics
Organizations routinely measure AI feature success using the same metrics they apply to traditional software features. The mismatch produces misleading signal, misallocated investment, and AI systems that optimize for the wrong outcomes.
The case for boring AI
The organizations getting the most value from AI are not the ones deploying the most sophisticated systems. They are the ones deploying narrow, reliable systems that handle specific tasks predictably and at scale.
The AI skills gap is not what you think it is
The conventional narrative says companies need more ML engineers and data scientists. The actual shortage is different: domain experts who can evaluate AI outputs, and organizations that know what they are trying to accomplish before they start hiring.
What AI systems need from product managers
AI features have requirements that traditional product management frameworks do not address well. The gap between PM practice and AI system needs produces poorly specified features, misaligned success metrics, and avoidable production failures.
Prompt engineering is not a substitute for system design
Prompt engineering is a real skill with real leverage. It is also the most commonly misused tool in AI development: applied to problems that require system redesign, not better prompts.
How to measure AI feature success
AI features fail in ways that standard product metrics miss. The user can be unhappy without the error rate going up. Quality can degrade without session length changing. Measuring AI feature success requires a different instrumentation strategy.
Evaluating LLMs for production: what benchmarks don't tell you
Public benchmarks measure what models can do under controlled conditions. Production performance depends on how models behave on your data, in your context, against your quality criteria. Here is how to build an evaluation that actually predicts production outcomes.
How to structure an AI team
There is no single correct structure for an AI team. There are structures that work for specific organizational contexts and ones that create predictable failure modes. Here is how to tell the difference.
Why AI systems drift without contracts
AI systems degrade silently over time. Not because the model changes, but because the assumptions baked into the system (about inputs, outputs, and behavior) are never made explicit enough to enforce.
The observability debt in AI systems
AI systems accumulate observability debt faster than traditional software because the failures are probabilistic, latent, and compound before they surface. Here is what that costs when you actually pay it.
RAG vs fine-tuning: why the comparison mostly doesn't make sense
RAG and fine-tuning solve different problems. Treating them as cost-quality tradeoffs for the same job leads to expensive models that still don't know what they need to know.
Why your AI proof of concept works but your product doesn't
AI proofs of concept are optimized to demonstrate capability under conditions that don't hold in production. Here is what changes when the demo environment goes away.
The token budget problem: why your production agents run out of room
Context windows are finite. Production workloads are not. Here is what actually breaks when an agent exhausts its budget, and three patterns that prevent it.
AI workflow automation vs RPA: what actually changes
RPA automates what a human clicks. AI automation handles what a human decides. The distinction determines which workflows you can hand off and which ones will break.
Why long-running AI agents fail silently
Agents degrade before they break. Context pressure accumulates, attention patterns shift, and outputs drift, all without an error code. Here's how to detect it and what to do about it.
The case for structured outputs in production AI
Most AI systems in production are parsing prose from LLMs when they should be requesting structured JSON. The cost and reliability gap is larger than teams expect.