Packy McCormick's latest dispatch from Not Boring doesn't just list tech launches; it argues that the convergence of augmented reality, autonomous driving, and generative AI has finally crossed the threshold from speculative hype to tangible utility. The piece is notable for its insistence that these technologies are no longer competing for attention but are instead forming a cohesive infrastructure for the next decade, with safety data and rapid construction timelines serving as the proof points. For a busy reader, the value lies in McCormick's ability to cut through the noise of product announcements to identify the underlying structural shifts in how we move, work, and understand our own health.
The Return of Wearables
McCormick frames the launch of the Meta Ray-Ban Display not as a gimmick, but as a correction to the trajectory of previous failed attempts at smart glasses. He writes, "Google Glass walked so that Meta Ray-Ban Display could run," highlighting the evolution from a novelty that made people uncomfortable to a device that "looks cool, delivers real world functionality, and is only $799!" The argument here is that form factor and price are finally aligning with utility. McCormick notes that the device "keeps you tuned in to the world around it, not distracted from it," a crucial distinction for a market weary of screens demanding total attention.
The integration of the Meta Neural Band, which reads muscle signals to allow for interaction without touching a screen, represents a significant leap in human-computer interaction. McCormick observes that the product "land[s] somewhere in between Airpods/previous versions of the Meta AR glasses and the full-on immersive Apple Vision Pro," effectively positioning it as the accessible entry point to a broader ecosystem. While the enthusiasm is palpable, critics might note that the reliance on live demos that were "spotted with live-demo mishaps" suggests that the software experience still has friction to overcome before mass adoption is guaranteed. Yet, the sheer accessibility of the price point and the familiar aesthetic make this a more plausible path to ubiquity than its predecessors.
"Google Glass walked so that Meta Ray-Ban Display could run."
The Inevitability of Automation
Moving from personal wearables to public infrastructure, McCormick pivots to the safety data released by Waymo, arguing that the technology has moved past the "valley of death" that plagues many startups. He presents a stark statistic: "Waymos experienced ~90% fewer crashes, 80% fewer injury-causing crashes ~78% fewer airbag deployments." This data is the cornerstone of his argument that scaled autonomous driving is not a distant dream but an immediate reality. McCormick writes, "From my perspective, humans cause a surprisingly low amount of crashes given the circumstances…we're all semi-distracted, hurling 5,000lb hunks of metal at 70mph+ speeds down winding and under-maintained roads."
The commentary suggests that the moral imperative for adoption is clear. If the technology can "avoid 9 out of those 10 crashes," the resistance from cities and regulators becomes harder to justify. McCormick points to the expansion into Nashville as evidence that "major cities" are beginning to accept the "win, win, win" scenario of safety, convenience, and cost reduction. However, a counterargument worth considering is that safety data from controlled, geofenced environments may not translate perfectly to the chaotic unpredictability of all weather and road conditions globally. Despite this, the trend line McCormick identifies is undeniable: the gap between human and machine safety is widening rapidly.
The Speed of Infrastructure
Perhaps the most striking section of the piece concerns the sheer velocity of physical infrastructure buildout, specifically regarding xAI's Colossus 2 datacenter. McCormick highlights the breakneck pace, noting that "xAI built in six months what took 15 months for Oracle, Crusoe and OpenAI!" This is not just a story about one company's efficiency; it is a story about the new rules of industrial capacity in the AI era. The author details how the project "circumvented Tennessee permitting restrictions by using a decommissioned power plant just across state lines in Mississippi," illustrating the lengths to which the executive branch of industry is willing to go to secure power.
McCormick describes the project as "the first ever Gigawatt-scale AI datacenter," a facility that will house enough computing power to train models that were previously unimaginable. The sheer scale—"roughly 200MW of cooling capacity"—suggests that the bottleneck for AI progress is no longer just algorithmic but physical. While the author celebrates this as "Elon magic," a critical reading must acknowledge that such rapid construction often comes with regulatory shortcuts and environmental trade-offs that may invite future scrutiny. Nevertheless, the speed at which these facilities are coming online fundamentally alters the timeline for when advanced AI capabilities will become available to the public.
"With FSD, we can avoid 9 out of those 10 crashes and reduce the severity of the crashes that do happen."
Predicting the Future of Health
The final major pillar of McCormick's optimism rests on the medical implications of generative AI. He introduces Delphi-2M, a model capable of predicting the rates of over 1,000 diseases decades in advance. McCormick draws a poignant parallel to the film Big Fish, writing, "Knowing how you'll die may be freeing, but predicting the diseases you might endure is more than freeing; it could be lifesaving." The model's ability to "map entire health trajectories" and generate synthetic data for training without privacy concerns represents a paradigm shift in preventative medicine.
The argument is that this technology allows healthcare systems to "flag population-level disease burdens years in advance," transforming the industry from reactive to proactive. McCormick notes that while clinical use is "5-10 years out," the commercialization is already underway. Critics might argue that predictive models are only as good as the data they are trained on, and historical health data often contains biases that could lead to inequitable outcomes for marginalized populations. However, the potential to model synthetic future health trajectories offers a way to stress-test interventions before they are ever applied to real humans, a capability that could save countless lives.
Bottom Line
McCormick's strongest argument is the synthesis of these disparate technologies into a single narrative of accelerating progress; the convergence of hardware, safety data, and predictive modeling suggests a near future where the quality of life improves measurably. The piece's biggest vulnerability is its tendency to treat regulatory and ethical hurdles as mere speed bumps rather than potential roadblocks, particularly regarding the rapid construction of energy-intensive infrastructure. Readers should watch for how these technologies scale in diverse, uncontrolled environments, as that will be the true test of whether this optimism is warranted or premature.