The AI lie
How tech companies use secrecy and hype to shape perceptions
This article contains speculative thinking and should be read critically.
Imagine John Stith Pemberton1, sitting behind his desk, experimenting with a new drink. He mixes some ingredients, tastes it, and thinks, "This is good". He decides to sell it and calls it "Coca-Cola".
What would his first instinct be? To tell everyone about his new drink and its ingredients?
Of course not.
He would keep it a secret, knowing that if he revealed the recipe, others would copy it. He might even claim to use a certain ingredient just to mislead competitors.
This scenario is not unique to Pemberton; it is common in many industries, especially technology.
There are numerous examples where tech companies make claims about their internal technologies or methodologies, but in reality, these statements are strategies to mislead and confuse competitors.
In this article, we will explore some of these examples and, ultimately, examine the so-called "AI lie".
Examples
PageRank
In the 2000s and early 2010s, Google's public messaging strongly emphasized PageRank2 as the centerpiece of its ranking algorithm.3
Over time, insiders, ex-Googlers, and leaks such as the 2024 Google Search API leak4, revealed that actual ranking had become vastly more complex, driven by machine learning models and hundreds of other signals beyond PageRank. By focusing public discussion on PageRank, Google potentially distracted competitors and SEO practitioners from reverse-engineering the real system.
Inside the Apple
Apple is legendary for its culture of secrecy. Former employees have reported that Apple sometimes exaggerates the sophistication of its internal tools or deliberately uses code-names and "decoy" projects to keep competitors uncertain about its real activities.
In the book Inside Apple by Adam Lashinsky5, it's described how Apple sometimes assigns employees to projects that have misleading code-names or fake project names so that even those working on them can't fully know what the final product will be. For instance, Lashinsky reports that some engineers hired for secret hardware teams didn't know what product they were building until very late in the process.
Another example is where former Apple engineer David Shayer wrote in TidBITS6 that the company sometimes deliberately overstates the capabilities of its internal tools or keeps separate teams siloed under misleading project names. This keeps details away from both internal leaks and external competitors. Shayer describes how Apple isolated the iPod Linux project team and would occasionally float information about features or tools that were either exaggerated or never meant for release.
The Browser Wars
In the 1990s, Netscape Navigator and Microsoft Internet Explorer were locked in an intense battle to dominate the web browser market7. This was a high-stakes, fast-moving competition where being seen as the technical leader was often as important as actually delivering features.
In their book Competing On Internet Time by Michael A. Cusumano and David B. Yoffie8 talk about how both companies sometimes announced upcoming features or technologies that were still experimental or might never ship. These announcements were often used strategically to influence competitors' product roadmaps and to create the impression of rapid innovation and technical superiority.
One concrete example was Netscape’s "server push". Introduced in 1995, it allowed a web server to send multiple pieces of content over a single HTTP connection without waiting for a new request. It was presented as revolutionary, but in reality, it was a hack around HTTP/1.0 limitations and fragile in practice. It worked in demos and simple cases but wasn’t robust or widely useful at scale.
This feature was heavily marketed and talked about in conferences, articles, and interviews to show Netscape’s engineering superiority even though it was of limited practical use for most developers.
Cryptocurrency / blockchain startups
Similar stories could be found again and again in the world of cryptocurrency and blockchain startups. Many of these companies have made grandiose claims about their technologies, often using buzzwords like "decentralization", "smart contracts", and "consensus algorithms" to create an aura of innovation and technical sophistication. However, many of these claims are often exaggerated or misleading.
One of the well known and ongoing examples is the claim that blockchain technology is the decentralized nature of Ripple's XRP Ledger. Ripple has long marketed XRP and its network as fully decentralized, and suggested its consensus algorithm was novel and highly robust. In practice, Ripple Labs controlled a significant proportion of validator nodes and owned over half the XRP supply. Critics and researchers noted that the network’s functioning depended heavily on Ripple’s infrastructure, making it far less decentralized than advertised.9
Another example is IOTA's "Unhackable Tangle". IOTA promoted its "Tangle" as a quantum-proof, feeless, and unhackable protocol, and boasted about its custom hash function. However, external security researchers (including at MIT's DCI) found serious flaws that could allow for practical attacks. IOTA developers dismissed criticism and even justified intentionally adding "copy protection" to confuse attackers though critics argued this hurt security transparency more than it protected.10
We use AI everywhere
In recent years, the AI industry has become the modern equivalent of Coca-Cola's secret formula: something mysterious, powerful, and carefully guarded. But unlike a drink recipe, AI companies often publicly hint at or exaggerate what's happening under the hood. Not just to impress customers and investors, but also to confuse or slow down competitors.
One clear example is OpenAI. After releasing GPT-4, OpenAI leaders made deliberately vague references to even more advanced systems (often rumored as "GPT-5") supposedly in training.11 They gave no concrete timelines or details, leaving competitors and the press to speculate wildly.12 This ambiguity serves a purpose: it helps keep rivals like Anthropic and Google DeepMind guessing about OpenAI's real progress, making them hesitate or shift roadmaps.
Then there's Tesla with its "Full Self-Driving" (FSD) claims. For years, Elon Musk has promised that Teslas will soon achieve Level 5 autonomy, meaning they could drive themselves without any human oversight. In practice, Tesla's system is still Level 2: it requires drivers to pay attention at all times. Yet these big promises about AI-powered autonomy serve to keep Tesla positioned as an innovation leader, shape public perception, and perhaps most strategically, force competitors to spend resources catching up to something that doesn't fully exist.13
At Meta's LlamaCon event, Microsoft CEO Satya Nadella stated that roughly 20–30% of code across Microsoft's repositories is now "written by software". What Nadella didn’t clarify was whether this includes simple autocomplete suggestions, full function generation, or light templating, leaving the metric open to interpretation. Critics argue that including autocomplete vastly inflates the figures.14
In all these cases, secrecy and hype become part of the competitive strategy. They protect genuine breakthroughs, but they also inflate expectations and obscure real limitations. And just as Coca-Cola never fully revealed its formula, AI companies rarely show the raw code, datasets, or failure cases behind their grand claims.
Conclusion
This is not to say that we are being deliberately lied to, nor is it to deny the genuine power of AI as a technology. However, many claims about AI are often exaggerated or misleading. Much like Pemberton guarded his Coca-Cola recipe, companies tend to keep their AI technologies and methodologies secret.
It is important to remain skeptical and critical of such claims, recognizing both the limitations of these technologies and the influence of marketing hype. Making drastic decisions, such as reducing the workforce by 20% in the belief that AI will seamlessly take over, or filling half the codebase with AI-generated code, can lead to significant regret and technical debt.
Ironically, these missteps may eventually create opportunities for a new wave of engineers to clean up the resulting mess.