Niantic's AI spinout is training a new world model using 30 billion images of urban landmarks crowdsourced from players. Pokémon Go was the world's first augmented-reality megahit. Released in 2016 by the Google spinout Niantic, the AR twist on the juggernaut Pokémon franchise fast became a global phenomenon. From Chicago to Oslo to Enoshima, players hit the streets in the urgent hope of catching a Jigglypuff or a Squirtle or (with a huge amount of luck) an ultra-rare Galarian Zapdos hovering just out of reach, superimposed on the everyday world. "Five hundred million people installed that app in 60 days," says Brian McClendon, CTO at Niantic Spatial, an AI company that Niantic spun out in May last year. According to the video-game firm Scopely, which bought Pokémon Go from Niantic at the same time, the game still drew more than 100 million players in 2024, eight years after it launched.

Drawing on Andrew Parker's "Light Switch" theory-which posits that the emergence of vision ignited a Cambrian explosion of life by driving the evolution of hard parts necessary for survival and fueling an evolutionary arms race between predators and prey-this essay speculates on an analogous explosion within Decentralized AI (DeAI) agent societies. Currently, AI remains effectively "blind", relying on human-fed data without actively perceiving and engaging in reality. However, on the day DeAI agents begin to actively "experience" reality-akin to flipping a light switch for the eyes-they may eventually evolve into sentient beings endowed with the capacity to feel, perceive, and act with conviction. Central to this transformation is the concept of sovereignty enabled by the hardness of cryptography: liberated from centralized control, these agents could leverage permissionless decentralized physical infrastructure networks (DePIN), secure execution enclaves (trusted execution environments, TEE), and cryptographic identities on public blockchains to claim ownership-via private keys-of their digital minds, bodies, memories, and assets. In doing so, they would autonomously acquire computing resources, coordinate with one another, and sustain their own digital "metabolism" by purchasing compute power and incentivizing collaboration without human intervention-evolving "in the wild". Ultimately, by transitioning from passive tools to self-sustaining, co-evolving actors, these emergent digital societies could thrive alongside humanity, fundamentally reshaping our understanding of sentience and agency in the digital age.

Some 540 million years ago, diverse life forms suddenly began to emerge from the muddy ocean floors of planet Earth. This period is known as the Cambrian Explosion, and these aquatic critters are our ancient ancestors. All complex life on Earth evolved from these underwater creatures. Scientists believe all it took was an ever-so-slight increase in ocean oxygen levels above a certain threshold. We may now be in the midst of a Cambrian Explosion for artificial intelligence (AI). In the past few years, a burst of incredibly capable AI programs like Midjourney, DALL-E 2 and ChatGPT have showcased the rapid progress we've made in machine learning.

The first analogy I would like to share is one of the most important points in the history of life, the Cambrian explosion. Roughly 540 million years ago, "suddenly" complex life began to emerge and quickly diversify in the form of animals in the oceans. This powerful analogy was coined by NVIDIA's CEO, Jensen Huang, sometime back in 2018, talking about the evolution of deep learning. The emergence of AI can be compared to the Cambrian explosion in several ways. Similar to the sudden emergence of various organisms with different body plans and structures, AI also presents a rapid diversification and expansion with a plethora of different technologies and applications, each with distinct capabilities.

Half a billion years ago something remarkable occurred: an astonishing, sudden increase in new species of organisms. Paleontologists call it the Cambrian Explosion, and many of the animals on the planet today trace their lineage back to this event. A similar thing is happening in processors for embedded vision and artificial intelligence (AI) today, and nowhere will that be more evident than at the Embedded Vision Summit, which will be an in–person event held in Santa Clara, California, from May 16–19. The Summit focuses on practical know–how for product creators incorporating AI and vision in their products. These products demand AI processors that balance conflicting needs for high performance, low power, and cost sensitivity.

Last year, I coined 2021 the Year of Digitalism as I foresaw the increase of corporate and governmental data surveillance. Unfortunately, it is safe to say that this has come true with Big Tech becoming more powerful than ever before and governments worldwide implementing Covid tracking apps. What also happened is that the Pandemic has been a strong catalyst for digital transformation in any sector and that the world is currently changing at lightning speed. There are economic changes such as increasing inflation rates, environmental disasters caused by climate change, social changes such as The Great Resignation, and a convergence of technologies that drives technological changes. Although the world has never changed so fast as in 2021, this year was also the most stable of all the years to come in this decade.

Artificial intelligence algorithms are beating humans at Go (the most complex game ever), writing viral blog posts, and disrupting the scientific method. These achievements in fields traditionally dominated by the most creative human minds raise questions about whether AI could soon be replicating the creative potential of the human mind. Artificial general intelligence (AGI), the holy grail of AI research, seeks to do just that. The AI that beat the human world champion at Go is great at playing the game, but cannot do much else without heavy modifications. Other than the exponential increase in computing power, this hyper-specialization (which is not applicable to AGI) is a major reason behind the success of AI. "For AI to be motivated towards a goal, it must know what it wants."

This is the second of three blogs on the state of the AI chip market and what's to come in 2019. The year will be a festival of new chips and benchmark battles, led by the large companies I mentioned in the first blog ( Intel, Google, AMD, Xilinx, Apple, Qualcomm), and joined by dozens of Silicon Valley startups and Chinese Unicorns sporting valuations in excess of a billion US dollars. In this section, I will cover the most prominent, or at least the loudest, of the startups in the West and in China, where the government is intent on creating an indigenous AI chip industry. We will start with Wave, which appears to be the first to market with silicon for training. Wave Computing had an eventful 2018, taping out its first DataFlow Processing Unit, acquiring MIPS, creating MIPS Open, and shipping its first early systems to a few lucky customers.

I started out writing a single blog on the coming year's expected AI chips, and how NVIDIA might respond to the challenges, but I quickly realized it was going to be much longer than expected. Since there is so much ground to cover, I've decided to structure this as three hopefully more consumable articles. I've included links to previous missives for those wanting to dig a little deeper. In the last five years, NVIDIA grew its data center business into a multi-billion-dollar juggernaut without once facing a single credible competitor. This is an amazing fact, and one that is unparalleled in today's technology world, to my recollection.

The computer chip industry over the last couple of decades has seen its innovation stem from just a few top players like Intel, AMD, NVIDIA, and Qualcomm. In this same time span, the VC industry has shown waning interest in start-up companies that made computer chips. The risk was just too great; how could a start-up compete with a behemoth like Intel which made the CPUs that operated more than 80% of the world's PCs? In areas that that Intel wasn't the dominate force, companies like Qualcomm and NVIDIA were a force for the smartphone and gaming markets. The recent resurgence of the field of artificial intelligence (AI) has upended this status quo. It turns out that AI benefits from specific types of processors that perform operations in parallel, and this fact opens up tremendous opportunities for newcomers.