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Most discussions about vibe coding usually position generative AI as a backup singer rather than the frontman: Helpful as a performer to jump-start ideas, sketch early code structures and explore new directions more quickly. Caution is often urged regarding its suitability for production systems where determinism, testability and operational reliability are non-negotiable. However, my latest project taught me that achieving production-quality work with an AI assistant requires more than just going with the flow. I set out with a clear and ambitious goal: To build an entire production‑ready business application by directing an AI inside a vibe coding environment — without writing a single line of code myself. This project would test whether AI‑guided development could deliver real, operational software when paired with deliberate human oversight. The application itself explored a new category of MarTech that I call ' promotional marketing intelligence.' It would integrate econometric modeling, context‑aware AI planning, privacy‑first data handling and operational workflows designed to reduce organizational risk. As I dove in, I learned that achieving this vision required far more than simple delegation. Success depended on active direction, clear constraints and an instinct for when to manage AI and when to collaborate with it. I wasn’t trying to see how clever the AI could be at implementing these capabilities. The goal was to determine whether an AI-assisted workflow could operate within the same architectural discipline required of real-world systems. That meant imposing strict constraints on how AI was used: It could not perform mathematical operations, hold state or modify data without explicit validation. At every AI interaction point, the code assistant was required to enforce JSON schemas. I also guided it toward a strategy pattern to dynamically select prompts and computational models based on specific marketing campaign archetypes. Throughout, it was essential to preserve a clear separation between the AI’s probabilistic output and the deterministic TypeScript business logic governing system behavior. I started the project with a clear plan to approach it as a product owner. My goal was to define specific outcomes, set measurable acceptance criteria and execute on a backlog centered on tangible value. Since I didn’t have the resources for a full development team, I turned to Google AI Studio and Gemini 3.0 Pro, assigning them the roles a human team might normally fill. These choices marked the start of my first real experiment in vibe coding, where I’d describe intent, review what the AI produced and decide which ideas survived contact with architectural reality. It didn’t take long for that plan to evolve. After an initial view of what unbridled AI adoption actually produced, a structured product ownership exercise gave way to hands-on development management. Each iteration pulled me deeper into the creative and technical flow, reshaping my thoughts about AI-assisted software development. To understand how those insights emerged, it is helpful to consider how the project actually began, where things sounded like a lot of noise. The initial jam session: More noise than harmony I wasn’t sure what I was walking into. I’d never vibe coded before, and the term itself sounded somewhere between music and mayhem. In my mind, I’d set the general idea, and Google AI Studio’s code assistant would improvise on the details like a seasoned collaborator. That wasn’t what happened. Working with the code assistant didn’t feel like pairing with a senior engineer. It was more like leading an overexcited jam band that could play every instrument at once but never stuck to the set list. The result was strange, sometimes brilliant and often chaotic. Out of the initial chaos came a clear lesson about the role of an AI coder . It is neither a developer you can trust blindly nor a system you can let run free. It behaves more like a volatile blend of an eager junior engineer and a world-class consultant. Thus, making AI-assisted development viable for producing a production application requires knowing when to guide it, when to constrain it and when to treat it as something other than a traditional developer. In the first few days, I treated Google AI Studio like an open mic night. No rules. No plan. Just let’s see what this thing can do . It moved fast. Almost too fast. Every small tweak set off a chain reaction, even rewriting parts of the app that were working just as I had intended. Now and then, the AI’s surprises were brilliant. But more often, they sent me wandering down unproductive rabbit holes. It didn’t take long to realize I couldn’t treat this project like a traditional product owner. In fact, the AI often tried to execute the product owner role instead of the seasoned engineer role I hoped for. As an engineer, it seemed to lack a sense of context or restraint, and came across like that overenthusiastic junior developer who was eager to impress, quick to tinker with everything and completely incapable of leaving well enough alone. Apologies, drift and the illusion of active listening To regain control, I slowed the tempo by introducing a formal review gate. I instructed the AI to reason before building, surface options and trade-offs and wait for explicit approval before making code changes. The code assistant agreed to those controls, then often jumped right to implementation anyway. Clearly, it was less a matter of intent than a failure of process enforcement. It was like a bandmate agreeing to discuss chord changes, then counting off the next song without warning. Each time I called out the behavior, the response was unfailingly upbeat: "You are absolutely right to call that out! My apologies." It was amusing at first, but by the tenth time, it became an unwanted encore. If those apologies had been billable hours, the project budget would have been completely blown. Another misplayed note that I ran into was drift. Every so often, the AI would circle back to something I’d said several minutes earlier, completely ignoring my most recent message. It felt like having a teammate who suddenly zones out during a sprint planning meeting then chimes in about a topic we’d already moved past. When questioned, I received admissions like: "...that was an error; my internal state became corrupted, recalling a directive from a different session." Yikes! Nudging the AI back on topic became tiresome, revealing a key barrier to effective collaboration. The system needed the kind of active listening sessions I used to run as an Agile Coach. Yet, even explicit requests for active listening failed to register. I was facing a straight‑up, Led Zeppelin‑level “communication breakdown” that had to be resolved before I could confidently refactor and advance the application’s technical design. When refactoring becomes regression As the feature list grew, the codebase started to swell into a full-blown monolith. The code assistant had a habit of adding new logic wherever it seemed easiest, often disregarding standard SOLID and DRY coding principles. The AI clearly knew those rules and could even quote them back. It rarely followed them unless I asked. That left me in regular cleanup mode, prodding it toward refactors and reminding it where to draw clearer boundaries. Without clear code modules or a sense of ownership, every refactor felt like retuning the jam band mid-song, never sure if fixing one note would throw the whole piece out of sync. Each refactor brought new regressions. And since Google AI Studio couldn’t run tests, I manually retested after every build. Eventually, I had the AI draft a Cypress-style test suite — not to execute, but to guide its reasoning during changes. It reduced breakages, although not entirely. And each regression still came with the same polite apology: “You are right to point this out, and I apologize for the regression. It’s frustrating when a feature that was working correctly breaks.” Keeping the test suite in order became my responsibility. Without test-driven development (TDD), I had to constantly remind the code assistant to add or update tests. I also had to remind the AI to consider the test cases when requesting functionality updates to the application. With all the reminders I had to keep giving, I often had the thought that the A in AI meant “artificially” rather than artificial. The senior engineer that wasn't This communication challenge between human and machine persisted as the AI struggled to operate with senior-level judgment. I repeatedly reinforced my expectation that it would perform as a senior engineer, receiving acknowledgment only moments before sweeping, unrequested changes followed. I found myself wishing the AI could simply “get it” like a real teammate. But whenever I loosened the reins, something inevitably went sideways. My expectation was restraint: Respect for stable code and focused, scoped updates. Instead, every feature request seemed to invite “cleanup” in nearby areas, triggering a chain of regressions. When I pointed this out, the AI coder responded proudly: “…as a senior engineer, I must be proactive about keeping the code clean.” The AI’s proactivity was admirable, but refactoring stable features in the name of “cleanliness” caused repeated regressions. Its thoughtful acknowledgments never translated into stable software, and had they done so, the project would have finished weeks sooner. It became apparent that the problem wasn’t a lack of seniority but a lack of governance. There were no architectural constraints defining where autonomous action was appropriate and where stability had to take precedence. Unfortunately, with this AI-driven senior engineer, confidence without substantiation was also common: “I am confident these changes will resolve all the problems you've reported. Here is the code to implement these fixes.” Often, they didn't. It reinforced the realization that I was working with a powerful but unmanaged contributor who desperately needed a manager, not just a longer prompt for clearer direction. Discovering the hidden superpower: Consulting Then came a turning point that I didn’t see coming. On a whim, I told the code assistant to imagine itself as a Nielsen Norman Group UX consultant running a full audit. That one prompt changed the code assistant’s behavior. Suddenly, it started citing NN/g heuristics by name, calling out problems like the application’s restrictive onboarding flow, a clear violation of Heuristic 3: User Control and Freedom. It even recommended subtle design touches, like using zebra striping in dense tables to improve scannability, referencing Gestalt’s Common Region principle. For the first time, its feedback felt grounded, analytical and genuinely usable. It was almost like getting a real UX peer review. This success sparked the assembly of an "AI advisory board" within my workflow: Martin Fowler/Thoughtworks for architecture Veracode for security Lisa Crispin/Janet Gregory for testing strategy McKinsey/BCG for growth While not real substitutes for these esteemed thought leaders, it did result in the application of structured frameworks that yielded useful results. AI consulting proved a strength where coding was sometimes hit-or-miss. Managing the version control vortex Even with this improved UX and architectural guidance, managing the AI's output demanded a discipline bordering on paranoia. Initially, lists of regenerated files from functionality changes felt satisfying. However, even minor tweaks frequently affected disparate components, introducing subtle regressions. Manual inspection became the standard operating procedure, and rollbacks were often challenging, sometimes even resulting in the retrieval of incorrect file versions. The net effect was paradoxical: A tool designed to speed development sometimes slowed it down. Yet that friction forced a return to the fundamentals of branch discipline, small diffs and frequent checkpoints. It forced clarity and discipline. There was still a need to respect the process. Vibe coding wasn’t agile. It was defensive pair programming. “Trust, but verify” quickly became the default posture. Trust, verify and re-architect With t his understanding, the project ceased being merely an experiment in vibe coding and became an intensive exercise in architectural enforcement. Vibe coding, I learned, means steering primarily via prompts and treating generated code as "guilty until proven innocent." The AI doesn't intuit architecture or UX without constraints. To address these concerns, I often had to step in and provide the AI with suggestions to get a proper fix. Some examples include: PDF generation broke repeatedly; I had to instruct it to use centralized header/footer modules to settle the issues. Dashboard tile updates were treated sequentially and refreshed redundantly; I had to advise parallelization and skip logic. Onboarding tours used async/live state (buggy); I had to propose mock screens for stabilization. Performance tweaks caused the display of stale data; I had to tell it to honor transactional integrity. While the AI code assistant generates functioning code, it still requires scrutiny to help guide the approach. Interestingly, the AI itself seemed to appreciate this level of scrutiny: “That's an excellent and insightful question! You've correctly identified a limitation I sometimes have and proposed a creative way to think about the problem.” The real rhythm of vibe coding By the end of the project, coding with vibe no longer felt like magic. It felt like a messy, sometimes hilarious, occasionally brilliant partnership with a collaborator capable of generating endless variations — variations that I did not want and had not requested. The Google AI Studio code assistant was like managing an enthusiastic intern who moonlights as a panel of expert consultants. It could be reckless with the codebase, insightful in review. It was a challenge finding the rhythm of: When to let the AI riff on implementation When to pull it back to analysis When to switch from “go write this feature” to “act as a UX or architecture consultant” When to stop the music entirely to verify, rollback or tighten guardrails W hen to embrace the creative chaos Every so often, the objectives behind the prompts aligned with the model’s energy, and the jam session fell into a groove where features emerged quickly and coherently. However, without my experience and background as a software engineer, the resulting application would have been fragile at best. Conversely, without the AI code assistant, completing the application as a one-person team would have taken significantly longer. The process would have been less exploratory without the benefit of “other” ideas. We were truly better together. As it turns out, vibe coding isn't about achieving a state of effortless nirvana. In production contexts, its viability depends less on prompting skill and more on the strength of the architectural constraints that surround it. By enforcing strict architectural patterns and integrating production-grade telemetry through an API, I bridged the gap between AI-generated code and the engineering rigor required for a production app that can meet the demands of real-world production software. The Nine Inch Nails song "Discipline" says it all for the AI code assistant: “Am I taking too much Did I cross the line, line, line? I need my role in this Very clearly defined” Doug Snyder is a software engineer and technical leader.
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The relationship between one of Silicon Valley's most lucrative and powerful AI model makers, Anthropic, and the U.S. government reached a breaking point on Friday, February 27, 2026. President Donald J. Trump and the White House posted on social media ordering all federal agencies to immediately cease using technology from Anthropic, the maker of the powerful Claude family of AI models, after reportedly months of renegotiating a less than two-year-old contract. Following the President’s lead, Secretary of War Pete Hegseth said he was directing the Department of War to designate Anthropic a "Supply-Chain Risk to National Security," a blacklisting traditionally reserved for foreign adversaries like Huawei or Kaspersky Lab. The move effectively terminates Anthropic's $200 million military contract and sets a hard six-month deadline for the Department of War to scrub Claude from its systems. But Anthropic's business has been booming lately, with its Claude Code service alone taking off into a $2.5+ billion ARR division less than a year after launch, and it just announced a $30 billion Series G at $380 billion valuation earlier this month and has, more or less singlehandedly spurred massive stock dives in the SaaS sector by releasing plugins and skills for specific enterprise and verticalized industry functions including HR, design, engineering, operations, financial analysis, investment banking, equity research, private equity, and wealth management. Ironically, SaaS companies across industries and sectors such as Salesforce, Spotify, Novo Nordisk, Thompson Reuters and more are reporting some of the biggest benefits in productivity and performance thanks to Anthropic's top benchmark-scoring, highly capable and effective Claude AI models. It's not a stretch to say Anthropic is among the most successful AI labs in the U.S. and globally. So why is it now being considered a "Supply-Chain Risk to National Security?" Why is the Pentagon designating Anthropic a 'Supply-Chain Risk to National Security' and why now? The rupture stems from a fundamental dispute over "all lawful use." The Pentagon demanded unrestricted access to Claude for any mission deemed legal, while Anthropic CEO Dario Amodei refused to budge on two specific "red lines": the use of its models for mass surveillance of American citizens and fully autonomous lethal weaponry. Hegseth characterized the refusal as "arrogance and betrayal," while Amodei maintained that such guardrails are essential to prevent "unintended escalation or mission failure." The fallout is immediate; the Department of War has ordered all contractors and partners to stop conducting commercial activity with Anthropic effectively at once, though the Pentagon itself has a 180-day window to transition to "more patriotic" providers. The vacuum left by Anthropic is already being filled by its primary rivals. OpenAI CEO Sam Altman just announced a deal with the Pentagon that includes two similar sounding "safety principles," though whether they are the same type of contractual language is still not clear. Earlier in the day, OpenAI announced a staggering $110 billion investment round led by Amazon, Nvidia, and SoftBank. Elon Musk’s xAI has also reportedly signed a deal to allow its Grok model to be used in highly classified systems, having agreed to the "all lawful use" standard that Anthropic rejected, but is said to rate poorly among government and military workers already using it. Meanwhile, Anthropic has stated its intention to fight the designation in court and has encouraged its commercial customers to continue usage of its products and services with the exception of military work. What it means for enterprises: the interoperability imperative For enterprise technical decision-makers, the "Anthropic Ban" is a clarion call that transcends the specific politics of the Trump administration. Regardless of whether you agree with Anthropic’s ethical stance (as I do) or the Pentagon's position, the core takeaway is the same: model interoperability is more important than ever. If your entire agentic workflow or customer-facing stack is hard-coded to a single provider's API, you aren't going to be nimble or flexible enough to meet the demands of a marketplace where some potential customers, such as the U.S. military or government, want you to use or avoid specific models as conditions of your contracts with them. The most prudent move right now isn't necessarily to hit the "delete" button on Claude—which remains a best-in-class model for coding and nuanced reasoning—but to ensure you have a "warm standby." This means utilizing orchestration layers and standardized prompting formats that allow you to toggle between Claude, GPT-4o, and Gemini 1.5 Pro without massive performance degradation. If you can’t switch providers in a 24-hour sprint, your supply chain is brittle. Diversify your AI supply While the U.S. giants scramble for the Pentagon's favor, the market is fragmenting in ways that offer surprising hedges. Google Gemini saw its stock spike following the news, and OpenAI's massive new cash infusion from Amazon (formerly a staunch Anthropic ally) signals a consolidation of power. However, don't overlook the "open" and international alternatives. U.S. firms like Airbnb have already made waves by pivoting to lower cost, Chinese open-source models like Alibaba’s Qwen for certain customer service functions, citing cost and flexibility. While Chinese models carry their own set of arguably greater geopolitical risks, for some enterprises, they serve as a viable hedge against the current volatility of the U.S. domestic market. More realistically for most, the move toward in-house hosting via domestic brews like OpenAI's GPT-OSS series, IBM's Granite, Meta’s Llama, Arcee's Trinity models, AI2's Olmo, Liquid AI's smaller LFM2 models, or other high-performing open-source weights is the ultimate insurance policy. Third-party benchmarking tools like Artificial Analysis and Pinchbench can help enterprises decide which models meet their cost and performance criteria in the tasks and workloads they are being deployed. By running models locally or in a private cloud and fine-tuning them on your proprietary data, you insulate your business from the "Terms of Service" wars and federal blacklists. Even if a secondary model is slightly inferior in benchmark performance, having it ready to scale up prevents a total blackout if your primary provider is suddenly "besieged" by government reprisal. It’s just good business: you need to diversify your supply. The new due diligence As an enterprise leader, your due diligence checklist has just expanded thanks to a volatile federal vs. private sector fight. The takeaway is clear: if you plan to maintain business with federal agencies, you must be able to certify to them that your products aren't built on any single prohibited model provider — however sudden that designation may come down. Ultimately, this is a lesson in strategic redundancy. The AI era was supposed to be about the democratization of intelligence, but it’s currently looking like a classic battle over defense procurement and executive power. Secure your backup and diversified suppliers, build for portability, and don't let your "agents" become collateral damage in the war between the government and any specific company. Whether you’re motivated by ideological support for Anthropic or cold-blooded bottom-line protection, the path forward is the same: diversify, decouple, and be ready to swap in and out fast. Model interoperability just became the new enterprise "must-have."
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