Artificial intelligence is the boardroom catchword of the era, wielded by executives, investors, and governments alike as the next engine of digital capitalism. With mind-boggling amounts of capital riding on anything that can be branded “AI,” especially in the business technology sector, there’s an irresistible pressure for big vendors to not only innovate, but to be seen innovating—often at a frantic, herd-driven pace. Yet beneath the gloss of press releases and keynote demos, serious questions persist about the business logic—and long-term wisdom—behind the rush to integrate and acquire AI platforms. Recent examples, such as the spectacular collapse of Builder.ai, serve as stark cautionary tales, highlighting recurring pitfalls that remain common even at the most well-resourced firms.
For CEOs, sovereign fund managers, and VCs with vast sums at their disposal, AI offers the promise of not only transformative business returns, but also reputational gold. Investing in the “next breakthrough” can bolster stock price, attract talent, and position a traditional firm as future-proof. But the disconnect between marketing hype and technical reality is often yawning. Missteps in AI can rapidly shift from an embarrassing press cycle to existential threats, especially when those investments cut to the heart of a vendor’s mission—such as cloud service providers betting big on AI-powered code generation.
The business imperative to appear forward-thinking can, paradoxically, incentivize poorly vetted deals—moves driven more by optics than technical merit. According to a sharply satirical commentary in The Register, “you may not care very much if you see your investment as primarily marketing and the careful evaporation of money into the pockets of advisors, partners and consultants until years later it along with all the workers are all gone. In that case, you know your business better than we do.” This cynicism, however, masks a painful truth: investment cycles in the AI arena are often so hyped that they invite decisions that are rational only from the perspective of quarterly PR buzz, not sustained product or customer value.
Within this fog of ambiguity, vendors and customers alike frequently overlook foundational questions: What, precisely, does the AI product do? How does it do it? And what’s the actual, measured impact on workflow efficiency, cost, and outcome quality? Too often, answers are deferred to the results of “proof of concept” (POC) pilots—but even these are frequently undermined by their lack of realism or rigor.
There’s a parallel with the original Turing Test, a thought experiment that was continuously adapted and stretched far beyond its original intent—a benchmark much easier to game than truly pass. Today, some vendors market AI solutions that pass internal “tests” but fall apart under head-to-head, real-world scrutiny.
According to regulatory filings and post-mortem analyses (cross-referenced with technology commentary and industry reporting), Builder.ai’s demise was a result of overpromised capabilities, reliance on significant human intervention behind the scenes, and lack of transparency over key metrics. It’s a familiar arc: hype fuels adoption, implementation woes mount, and the financial structure is unable to absorb growing operational costs—especially in environments where executive leadership isn’t technical enough to spot early warning signs.
As articulated in the critical analysis, “code generating AI isn’t a standalone black box app generator, it is a team member that needs expert human collaborators. If it speeds their work and ups quality, then fine. If not, it’s a problem, not a solution.” Uncritical deployment of AI code assistants not only produces buggy or insecure code, but also raises organizational issues—most notably, cultural or managerial pressure that discourages reporting shortcomings.
In an environment where “it is politically impossible to report that your artificial helpmate is anything but [helpful], then both you and your upper management have got highly real problems.” Here, the authority of AI is conflated with infallibility, with dangerous consequences for software quality and business risk.
What’s needed is a transparent, benchmarked, and competitive standard—analogous to cybersecurity’s Capture the Flag contests or the autonomous vehicle sector’s foundational DARPA Grand Challenge. In these arenas, performance is measured head-to-head, with clear rules and success metrics. For AI to deliver on its promise, similar large-scale, open competitions could force vendors to demonstrate actual capabilities, not just theoretical ones.
This problem is not unique to AI, but is amplified by the hype cycles and lack of mature benchmarks animating the field today. For organizations serious about AI, transparency—internal and external—is not just a virtue, but an existential requirement.
This is a clarion call for a new modus operandi in business AI investment. The way forward isn’t to double down on empty hype or shelter behind carefully stage-managed pilot schemes. Instead, it’s to put the technology—and the vendors—through hard, transparent, public tests. Only then can the industry move past glossy decks and investment “fear of missing out” into real, durable innovation.
As the AI boom matures, the winners will be those who resist the pressure to look good at the cost of being good. For business software vendors, cloud platform giants, and their investors, the imperative is clear: verify before you buy, foster transparency at every level, and never mistake marketing momentum for technical reality. Only through this discipline can the promise of enterprise AI be realized—and the next billion-dollar blunder be averted.
Source: theregister.com How to help a big AI vendor making awful business decisions
The Allure—and Illogic—of Big AI Bets
For CEOs, sovereign fund managers, and VCs with vast sums at their disposal, AI offers the promise of not only transformative business returns, but also reputational gold. Investing in the “next breakthrough” can bolster stock price, attract talent, and position a traditional firm as future-proof. But the disconnect between marketing hype and technical reality is often yawning. Missteps in AI can rapidly shift from an embarrassing press cycle to existential threats, especially when those investments cut to the heart of a vendor’s mission—such as cloud service providers betting big on AI-powered code generation.The business imperative to appear forward-thinking can, paradoxically, incentivize poorly vetted deals—moves driven more by optics than technical merit. According to a sharply satirical commentary in The Register, “you may not care very much if you see your investment as primarily marketing and the careful evaporation of money into the pockets of advisors, partners and consultants until years later it along with all the workers are all gone. In that case, you know your business better than we do.” This cynicism, however, masks a painful truth: investment cycles in the AI arena are often so hyped that they invite decisions that are rational only from the perspective of quarterly PR buzz, not sustained product or customer value.
Hype Versus Reality: The Pitfalls of Evaluating AI Startups
Technical due diligence in AI is inherently challenging. The Register’s commentary brings forward a core dilemma: “Is it real, or is it a branding layer atop offshored humans? This would be a bad sign.” In the business application of AI—whether it’s machine learning for code, workflow automation, or predictive analytics—the actual working of the technology, as opposed to the promise, is what counts. Automated claims about code generation, for example, are notoriously slippery. “There’s no such thing and no-code AI app generation that always works. There’s no such thing as any approach to code generation that always works, so what are you buying - and when you’ve bought it, what are you selling?”Within this fog of ambiguity, vendors and customers alike frequently overlook foundational questions: What, precisely, does the AI product do? How does it do it? And what’s the actual, measured impact on workflow efficiency, cost, and outcome quality? Too often, answers are deferred to the results of “proof of concept” (POC) pilots—but even these are frequently undermined by their lack of realism or rigor.
The Broken POC Model and AI’s “Turing Test Problem”
A traditional POC, when applied to AI, is supposed to validate claims in a controlled, risk-reduced environment. Yet, as The Register points out, “POCs by themselves can be pointless, even misleading, if they don’t have scope and goals forensically designed and results rigorously verified. As the more astute observer will already have noted, forensic rigor isn’t a primary attribute of business AI, which explains a very great deal.” Too often, pilots are set up to succeed—using carefully selected tasks or datasets, and ignoring edge cases or real-world context.There’s a parallel with the original Turing Test, a thought experiment that was continuously adapted and stretched far beyond its original intent—a benchmark much easier to game than truly pass. Today, some vendors market AI solutions that pass internal “tests” but fall apart under head-to-head, real-world scrutiny.
The Builder.ai Collapse: Lessons in Caution
The saga of Builder.ai, which coded itself “into a corner” before declaring bankruptcy, underscores many of the systemic vulnerabilities in the AI business investment model. Despite attracting high-profile partnerships and customer case studies, the company struggled to deliver consistently functional software at scale, especially as technical challenges mounted and the limits of its “no-code” AI approach became glaringly apparent. When reality finally surfaced—delayed projects, subpar outputs, and opaque processes—it was too late for a recovery.According to regulatory filings and post-mortem analyses (cross-referenced with technology commentary and industry reporting), Builder.ai’s demise was a result of overpromised capabilities, reliance on significant human intervention behind the scenes, and lack of transparency over key metrics. It’s a familiar arc: hype fuels adoption, implementation woes mount, and the financial structure is unable to absorb growing operational costs—especially in environments where executive leadership isn’t technical enough to spot early warning signs.
Code Generation AI: Promise and Peril
Perhaps nowhere is the AI investment gamble more visible than in automated code generation. From Microsoft’s GitHub Copilot to a host of startup rivals, the promise is alluring: AI tools that can accelerate developer productivity, reduce human error, and bridge talent gaps. The reality? Results are highly variable and often depend on context, the complexity of the problem, and, above all, the skill and oversight provided by the human collaborator.As articulated in the critical analysis, “code generating AI isn’t a standalone black box app generator, it is a team member that needs expert human collaborators. If it speeds their work and ups quality, then fine. If not, it’s a problem, not a solution.” Uncritical deployment of AI code assistants not only produces buggy or insecure code, but also raises organizational issues—most notably, cultural or managerial pressure that discourages reporting shortcomings.
In an environment where “it is politically impossible to report that your artificial helpmate is anything but [helpful], then both you and your upper management have got highly real problems.” Here, the authority of AI is conflated with infallibility, with dangerous consequences for software quality and business risk.
Metrics That Matter: How to Actually Measure AI ROI
Given the opacity of many AI claims, how should business and technology leaders properly assess the impact of an AI adoption? The only useful POCs, the commentary argues, are those where “the time to produce an app and the quality therein, compared to the total cost of whatever mix of carbon and silicon, actually did the job. That means a standard app that can be built in a mix of ways, in a public arena where nothing can be hidden, and competition is all.”What’s needed is a transparent, benchmarked, and competitive standard—analogous to cybersecurity’s Capture the Flag contests or the autonomous vehicle sector’s foundational DARPA Grand Challenge. In these arenas, performance is measured head-to-head, with clear rules and success metrics. For AI to deliver on its promise, similar large-scale, open competitions could force vendors to demonstrate actual capabilities, not just theoretical ones.
Possible Benchmarks for Future POCs
- Open App Development Competitions: Teams, both AI-enhanced and conventional, tasked with building a specified business app under time and resource constraints.
- Real-World Datasets: Problems seeded with real customer requirements, un-curated and representative of business complexity.
- Transparent Scoring: Assessment based on code quality, speed, maintainability, and security, scored by impartial judges.
- Post-Contest Transparency: All code and process artifacts published for independent audit.
Organizational and Leadership Dynamics: Reporting and Reality
One of the most insidious risks linked with big AI spending is organizational blindness. In large enterprise settings, it is all too easy for reality to become subordinate to narrative—especially when internal dissent is discouraged and reporting lines shield management from inconvenient truths. If POCs and pilots are designed to succeed, and post-implementation issues are buried under layers of middle management, it becomes nearly impossible to course-correct before significant financial and reputational damage accrues.This problem is not unique to AI, but is amplified by the hype cycles and lack of mature benchmarks animating the field today. For organizations serious about AI, transparency—internal and external—is not just a virtue, but an existential requirement.
Critical Analysis: Strengths and Systemic Risks
Notable Strengths
- Potential for Real Productivity Gains: When appropriately scoped and transparently evaluated, AI tools can augment human teams and speed up processes that would otherwise require significant manual oversight.
- Differentiation and Competitive Advantage: Early, careful investment in AI that aligns with business mission can provide long-term competitive headroom—especially in fields where automation can yield incremental improvements at scale.
- Data-Driven Discovery: The rigor of AI, when properly implemented, can reveal patterns and insights previously invisible to human analysis.
Systemic Risks
- Overhyped and Under-Verified Claims: The tendency to believe vendor or startup hype without evidence breeds disappointment, or worse, crisis.
- Investment Driven by Optics: Pressure to announce AI “firsts” for marketing or investor-relations purposes often trumps sober technical evaluation.
- Human-in-the-Loop Gaps: AI is only as effective as its human collaborators, and assuming otherwise leads to broken implementations.
- Opaque Decision-Making and Reporting: The lack of transparency within organizations, as well as between vendor and customer, can mask problems until they have become unmanageable.
- Vendor Lock-In: Large AI contracts can create structural dependence on a particular technology or partner, limiting flexibility if the product underdelivers or becomes obsolete.
What Sensible AI Due Diligence Looks Like
Sensible due diligence, the piece suggests, is about more than technical code review or financial analysis. It involves multidisciplinary scrutiny—product managers, cybersecurity experts, engineers, and even customers—brought together to assess claims under stress. Core questions should include:- What is the AI claim, and what is its underlying mechanism?
- What metrics are being measured, and who is setting those metrics?
- Is there transparency regarding training data, model limitations, and post-launch support?
- Can the AI be tested in open, real-world competition against alternatives?
A Call for Public, Competitive Benchmarks
The final recommendation carries both satire and seriousness: “spend a fraction of that on a big public competition where app teams can face off with the best AI coding help they can muster. That’ll prove the POC everyone, especially you, needs to validate AI-assisted coding, and to make a framework for future development that won’t end up a costly, embarrassing mess. That is, of course, if the outcome of such an idea wouldn’t make you look even worse.”This is a clarion call for a new modus operandi in business AI investment. The way forward isn’t to double down on empty hype or shelter behind carefully stage-managed pilot schemes. Instead, it’s to put the technology—and the vendors—through hard, transparent, public tests. Only then can the industry move past glossy decks and investment “fear of missing out” into real, durable innovation.
Conclusion: Toward Real Value in AI Business Investment
The cautionary collapse of Builder.ai, and the broader critique offered by industry observers, should compel both business and technical leaders to fundamentally rethink the current model of AI investment. Opaque, hype-led spending patterns offer little protection against spectacular, costly misjudgments. In contrast, transparent, competition-based evaluation—alongside strong organizational reporting and realistic ROI benchmarks—can de-risk AI adoption and ensure that only real value, not vaporware, wins.As the AI boom matures, the winners will be those who resist the pressure to look good at the cost of being good. For business software vendors, cloud platform giants, and their investors, the imperative is clear: verify before you buy, foster transparency at every level, and never mistake marketing momentum for technical reality. Only through this discipline can the promise of enterprise AI be realized—and the next billion-dollar blunder be averted.
Source: theregister.com How to help a big AI vendor making awful business decisions
