Its Hard to Start Over. But It is Time… From Forbes to Substack.
I’ve been publishing on Forbes for over a decade, focusing primarily on AI Hardware as an Industry Analyst. My company is Cambrian-AI Research, which I founded after leaving Moor Insights and Strategy in 2019. I have over 1000 posts, and well over 1M reads since I left corporate America Tech companies (AMD, IBM, HP, Calxeda) and began helping Semiconductor clients like Nvidia, IBM, Intel, Qualcomm, Synopsys, Cadence, and many startups like Cerebras, D-Matrix, Baya Systems, Groq, and others. But sometimes my articles get too technical for a Forbes audience, and so I am adding Substack as a way to reach you with the Semi-News you need to know to lead businesses, develop AI apps, or invest in AI companies.
Why Substack, and why now
I have enjoyed engaging with my readers on Forbes, but as I said above, I need a new spot to share what I REALLY think, positive and negative, about the AI hardware industry. And I need to do it without an Editor looking over my shoulder every time I publish about my clients and their competitors. It’s really that simple: I need the freedom to tell you what I think, and I need feedback to tell me where I go wrong!
What kind of community am I looking to build here
As I said, I need feedback. I’m not always right, and I am not a data scientist. I am a marketing professional with an out-of-date MS-CS degree and over 40 years of industry experience. I need YOUR perspective. Thanks in advance!
I typically publish whenever there is real news from my clients or from other companies I follow in the AI HW space.
For example, I am working HotChips this week, and there will be lots of news. And there are new companies, like Analog Physics who is building… get this…. a quantum architecture to use AI to control swarms of devices like drones. Yep. Cool stuff coming your way!
Yeah, It’s called Cambrian-AI for a reason
More than a few years ago, NVIDIA CEO Jensen Huang used the term “Cambrian Explosion” to refer to the rapid growth in AI models (which, of course, his chips run exceedingly well! ;-). I think the term is appropriate to both AI software, models, and the specialized hardware that accelerates AI. I began publishing an annual blog in January 2019, summarizing the year’s innovations and forecasting what we expect for the coming year. But I am getting ahead of the story…
Some 540 million years ago, life on earth expanded exponentially, all in a brief period (only 13-15 million years). In that time, life on earth exploded from simple, primarily single-cell organisms into a myriad of fascinating forms. In fact, nearly all major animal phyla on the planet today came into being during this time, beginning the journey that has culminated in the diverse array of life we see today.
Similarly, over the last few decades, scientists and engineers have created an explosion of AI hardware and models that can solve previously impossible problems. Yann LeCun, Geoff Hinton, Yoshua Bengio, Andrew Ng, and others began experimenting with Deep Neural Networks, eventually applying parallel programming on GPUs in early 2009. Yann LeCun’s work in convolutional neural nets and Andrew Ng’s use of large-scale GPUs to accelerate deep neural networks (DNNs) ignited the Cambrian Explosion in AI. Now, well over 100 hardware startups and scores of others are developing hardware supposedly faster than NVIDIA vaunted GPU’s. Of course, the big boys are anxious to compete, including Amazon AWS, Alibaba, AMD, Google, Huawei, Intel, Qualcomm, and AMD. Consequently, we are forecasting a renaissance of domain-specific semiconductors, all chasing after a $500B market that will change the world. And our lives, for better or for worse.
In 2019 when I started Cambrian-AI, there was little new silicon to show for the tens of billions in invested capital. And NVIDIA continued to create better and better GPU solutions. It seemed like everybody else had an investor pitch that claimed orders of magnitude of performance and efficiency advantages. But only Google actually had a GPU alternative in production, the Google Tensor Processing Unit, or TPU.
But now that is beginning to change, with scores of companies shipping silicon to developers for porting and testing. Hence, I created Cambrian AI Research, where investors, media, and AI practitioners can keep up with the latest AI innovations, and communicate their plans and innovations, with 100’s of blogs and dozens of research papers.
Hope to see you back soon!!