Some of the topics I touch on: Why I believe Vision Pro may be an over-engineered “devkit” The genius & audacity behind some of Apple’s hardware decisions Gaze & pinch is an incredible UI superpower and major industry ah-ha moment Why the Vision Pro software/content story is so dull and unimaginative Why most people won’t use Vision Pro for watching TV/movies Apple’s bet in immersive video is a total game-changer for live sports Why I returned my Vision Pro… and my Top 10 wishlist to reconsider Apple’s VR debut is the best thing that ever happened to Oculus/Meta My unsolicited product advice to Meta for Quest Pro 2 and beyond

Apple really played it safe in the design of this first VR product by over-engineering it. For starters, Vision Pro ships with more sensors than what’s likely necessary to deliver Apple’s intended experience. This is typical in a first-generation product that’s been under development for so many years. It makes Vision Pro start to feel like a devkit.

A sensor party: 6 tracking cameras, 2 passthrough cameras, 2 depth sensors(plus 4 eye-tracking cameras not shown)

it’s easy to understand two particularly important decisions Apple made for the Vision Pro launch: Designing an incredible in-store Vision Pro demo experience, with the primary goal of getting as many people as possible to experience the magic of VR through Apple’s lenses — most of whom have no intention to even consider a $4,000 purchase. The demo is only secondarily focused on actually selling Vision Pro headsets. Launching an iconic woven strap that photographs beautifully even though this strap simply isn’t comfortable enough for the vast majority of head shapes. It’s easy to conclude that this decision paid off because nearly every bit of media coverage (including and especially third-party reviews on YouTube) uses the woven strap despite the fact that it’s less comfortable than the dual loop strap that’s “hidden in the box”.

Apple’s relentless and uncompromising hardware insanity is largely what made it possible for such a high-res display to exist in a VR headset, and it’s clear that this product couldn’t possibly have launched much sooner than 2024 for one simple limiting factor — the maturity of micro-OLED displays plus the existence of power-efficient chipsets that can deliver the heavy compute required to drive this kind of display (i.e. the M2).

Software is misunderstood. It can feel like a discrete thing, something with which we interact. But, really, it is the intrusion into our world of something very alien. It is the strange interaction of electricity, semiconductors, and instructions, all of which somehow magically control objects that range from screens to robots to phones, to medical devices, laptops, and a bewildering multitude of other things. It is almost infinitely malleable, able to slide and twist and contort itself such that, in its pliability, it pries open doorways as yet unseen.

the clearing price for software production will change. But not just because it becomes cheaper to produce software. In the limit, we think about this moment as being analogous to how previous waves of technological change took the price of underlying technologies—from CPUs, to storage and bandwidth—to a reasonable approximation of zero, unleashing a flood of speciation and innovation. In software evolutionary terms, we just went from human cycle times to that of the drosophila: everything evolves and mutates faster.

A software industry where anyone can write software, can do it for pennies, and can do it as easily as speaking or writing text, is a transformative moment. It is an exaggeration, but only a modest one, to say that it is a kind of Gutenberg moment, one where previous barriers to creation—scholarly, creative, economic, etc—are going to fall away, as people are freed to do things only limited by their imagination, or, more practically, by the old costs of producing software.

We have almost certainly been producing far less software than we need. The size of this technical debt is not knowable, but it cannot be small, so subsequent growth may be geometric. This would mean that as the cost of software drops to an approximate zero, the creation of software predictably explodes in ways that have barely been previously imagined.

Entrepreneur and publisher Tim O’Reilly has a nice phrase that is applicable at this point. He argues investors and entrepreneurs should “create more value than you capture.” The technology industry started out that way, but in recent years it has too often gone for the quick win, usually by running gambits from the financial services playbook. We think that for the first time in decades, the technology industry could return to its roots, and, by unleashing a wave of software production, truly create more value than its captures.

Software production has been too complex and expensive for too long, which has caused us to underproduce software for decades, resulting in immense, society-wide technical debt.

technology has a habit of confounding economics. When it comes to technology, how do we know those supply and demand lines are right? The answer is that we don’t. And that’s where interesting things start happening. Sometimes, for example, an increased supply of something leads to more demand, shifting the curves around. This has happened many times in technology, as various core components of technology tumbled down curves of decreasing cost for increasing power (or storage, or bandwidth, etc.).

Suddenly AI has become cheap, to the point where people are “wasting” it via “do my essay” prompts to chatbots, getting help with microservice code, and so on. You could argue that the price/performance of intelligence itself is now tumbling down a curve, much like as has happened with prior generations of technology.

it’s worth reminding oneself that waves of AI enthusiasm have hit the beach of awareness once every decade or two, only to recede again as the hyperbole outpaces what can actually be done.

Every software framework you've ever used is in the abstraction game: it takes a general-purpose tool, picks a specific set of common use-cases, and puts up scaffolding and guard rails that make it easier to build those specific use cases by giving you less to do and fewer choices to think about. The lines between these three are blurry. Popular abstractions become standardizations.

We fully acknowledge that it’s hard to be confident in any predictions at the pace the field is moving. Right now, though, it seems we’re much more likely to see applications full of creative images created strictly by programmers than applications with human-designed art built strictly by creators.