The silence is deafening. When a major tech initiative launches, the internet usually explodes with opinions, hot takes, and, crucially, data. But in some cases, you hear nothing. And that, in itself, speaks volumes.
Let's call this "metric amnesia"—the strange phenomenon where key performance indicators (KPIs) vanish from the conversation around a heavily hyped product or strategy. It's not just about a lack of transparency; it's about a deliberate obscuring of the truth. When the numbers are good, companies shout them from the rooftops. When they're not so good (or downright ugly), they tend to go quiet.
What’s happening when the data well runs dry? Are companies genuinely struggling to measure impact, or are they simply choosing not to share the results when they don't align with the desired narrative? I suspect it’s the latter. After years in the data trenches, I've learned that almost anything can be measured; the real question is whether companies want to measure it. And, more importantly, whether they want you to see the results.
Think about past tech hypes. The initial fanfare is always accompanied by projections, forecasts, and carefully selected data points designed to build excitement. But what happens six months, a year, or two years down the line? The initial rosy predictions often fade into vague pronouncements about "long-term vision" and "transformative potential." Where are the actual numbers? The user growth figures? The ROI calculations? Did the promised efficiencies actually materialize?
This isn't just about holding companies accountable (though that's certainly part of it). It's about understanding the underlying dynamics of the tech industry and the incentives that drive decision-making. Companies are under immense pressure to innovate, to disrupt, and to capture market share. But that pressure can also lead to a kind of collective delusion, where everyone is so focused on the potential upside that they ignore the warning signs.
And this is the part of the report that I find genuinely puzzling. How can so many smart people get caught up in these hype cycles? Is it simply a matter of groupthink, or is there something more insidious at play? Are analysts genuinely not asking the hard questions, or are they being subtly discouraged from doing so?
The online world doesn’t help. The lack of data is compounded by the echo chamber effect. Social media algorithms amplify existing biases and create filter bubbles where dissenting voices are often drowned out. A few positive reviews or endorsements can create the illusion of widespread support, even if the underlying reality is far more complex.
I've noticed a pattern: the more enthusiastic the initial coverage, the more likely it is that the follow-up reporting will be superficial and uncritical. This isn't necessarily the result of malice or incompetence; it's simply a reflection of the way the media ecosystem works. News outlets are under pressure to generate clicks and engagement, and positive stories tend to perform better than negative ones.
The challenge, then, is to cut through the noise and get to the underlying truth. It requires a willingness to question assumptions, to dig beneath the surface, and to demand hard evidence. It also requires a healthy dose of skepticism.
The absence of data is, itself, a data point. It suggests that something isn't adding up, that the numbers don't support the hype. It's a flashing red light, warning us to proceed with caution. And, more often than not, it's a sign that the emperor has no clothes.
beta technologies: what we know
2025-11-05
