Moore's Law -- putting more and more transistors on a chip -- accelerated the computing industry by so many orders of magnitude, it has (and continues to) achieve seemingly impossible feats. However, we're now resorting to brute-force hacks to keep pushing it beyond its limits and are getting closer to the point of diminishing returns (especially given costly manufacturing infrastructure). Yet this very dynamic is leading to "a Cambrian explosion" in computing capabilities… just look at what's happening today with GPUs, FPGAs, and neuromorphic chips. Through such continuing performance improvements and parallelization, classic computing continues to reshape the modern world. But we're so focused on making our computers do more that we're not talking enough about what classic computers can't do -- and that's to compute things the way nature does, which operates in quantum mechanics. So our smart machines are really quite dumb, argues Rigetti Computing founder and CEO Chad Rigetti; they're limited to human-made binary code vs. the natural reality of continuous variables. This in turn limits our ability to work on problems that classic computers can't solve, such as key applications in computational chemistry or large-scale optimization for machine learning and artificial intelligence. Which is where quantum computing comes in. But what is quantum computing, really -- beyond the history and the hype? And where are we in reaching the promise of practical quantum computers? (Hint: it will take a hybrid approach to get there.) Who are the players -- companies, countries, types of people/skills -- working on it, and how can a startup compete in this space? Finally, what will it take to get "the flywheel" of application development and discovery going? Part of the answer comes full circle to the same economic engine that drove previous computing advances, argues Chris Dixon; Moore's Law, after all, is more of an economic principle that combined the forces of capitalism, a critical mass of ideas, and people moving things forward by sheer will. Quantum computing is finally getting pulled into the same economic forces as well.

It’s the end of the beginning — not the beginning of the end — for wearables, argue the guests in this episode of the a16z Podcast. Especially as we move from the first, to the next, generation of wearable devices: not just activity trackers and watches but VR/AR gear, “hearables”, continuous glucose monitors, and more. The quantified self movement then takes these empirical tracking- and data-gathering tools to better reason about what works and doesn’t work in our bodies to help us solve problems and live better lives. Yet the act of gathering data isn’t the hard part… it’s linking them to insights and outcomes. Because we really do have very little data about what works at a collective let alone an individual level. With a new age of biohacking upon us — where people can apply engineering principles to manipulate what we take into our bodies (inputs) to tune how we perform (outputs) — can we finally embrace these tools? What will it take to make something that’s mainly a niche activity/community (quantified self was formally started a decade ago!) into something more mainstream for all? (Hint: it involves cookie recipes.) And finally, what are the societal implications of all this, from avoiding data dystopias to embracing the consumerization of government projects too? Joining us to explore these questions and more (in conversation with Sonal Chokshi), we have: neuroscientist and data scientist Rachel Kalmar, currently a fellow at The Berkman Klein Center for Internet and Society at Harvard University; co-founder of The Quantified Self blog and community Gary Wolf; and Geoffrey Woo, co-founder and CEO at Nootrobox (an a16z company).