Bill Tai
20.4K posts
Bill Tai
@KiteVC
🏄VC: 23 IPOs🤠 @Zoom 's 1st seed; Formative catalyst @BitfuryGroup @Canva; seed @Color @Tweetdeck;🌱 Founding Chair @Hut8corp @IPInfusion @TreasureData
United States Katılım Mart 2007
2.8K Takip Edilen37.8K Takipçiler
AND if marginal productivity comes from software and electrons generated from non oil sources.. then we live in a world where the NEXT currency is 'stored electrons.. ' I had this thought around 2009 , and expressed some of that thinking in this old medium piece from 2018 : @billtai/currency-in-the-4th-industrial-revolution-19a73d47b6c5" target="_blank" rel="nofollow noopener">medium.com/@billtai/curre…
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This piece on the 'petro dollar' is a GREAT primer on why the world is, what it is today (in terms of economies, currencies, geopolitical tensions, wars..) as it's about 'control of productivity'.
What is currency? What are savings? Stored productivity. But what comes next?
Felix Prehn 🐶@felixprehn
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Bill Tai retweetledi
Ali Ghodsi, the cofounder and CEO of Databricks, says Zoom has a massive chance to build an AI-first product, that could seriously disrupt the traditional enterprise SAAS.
Because it sits on the largest datasets of meeting videos and transcripts.
The big pain in enterprise software is data entry and coordination. Zoom already sits on the raw input: every customer call and internal meeting, plus the video, audio, and transcript.
If Zoom can reliably pull out decisions, context, and action items, then write them back into the right system of record automatically, as an AI-first workflow layer, it becomes the front door for work. That would replace lots of separate SAAS tools that exist mainly to collect notes and updates.
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Video from 'Bg2 Pod' YT channel (link in comment)
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Bill Tai retweetledi
September 2009. Jensen Huang walks onto a small stage at the Fairmont hotel in San Jose. About 1,500 people are in the room. He runs a company that makes chips for video games.
He spends the next 8 minutes doing math on a whiteboard, explaining why the future of computing won't come from making CPUs faster. He calls it "CEO math" and apologizes in advance to every computer science professor in the audience. Then he lays out an argument that almost nobody took seriously at the time: the way to make computers dramatically faster is to pair a regular CPU with hundreds of tiny parallel processors, the kind that already exist inside graphics cards. One CPU for the sequential stuff. Hundreds of GPU cores for everything else. He calls it "heterogeneous computing."
He shows the math. A workload that can be split into many pieces at once gets up to 200x faster on this combined system. A workload that has to run one step at a time loses nothing. "The most important thing in creating a new architecture," he says, "is to make sure it does no harm."
This was the first GPU Technology Conference. NVIDIA had launched a software platform called CUDA three years earlier, in 2006, to let developers write programs that run on graphics cards instead of just regular processors. Almost nobody cared. GPUs were for rendering Call of Duty, not for scientific computing. The academic world was polite but skeptical. The enterprise world ignored it entirely.
By this point, Huang had been making this argument for years. NVIDIA was a $7 billion company. It competed with AMD and Intel for market share in the graphics market. That was the whole business. Jensen kept saying the GPU wasn't just a gaming chip; it was a computing platform. He kept saying parallel processing would reshape every industry from medicine to finance to physics simulations. People kept nodding, then doing nothing.
Then deep learning happened. Around 2012, AI researchers discovered that training a neural network, which means teaching a computer to recognize patterns by running the same calculation millions of times across huge datasets, was exactly the kind of workload Jensen had been describing. GPUs can train AI models 10 to 50 times faster than CPUs. The architecture he outlined in this 2009 talk, with one CPU handling step-by-step tasks while hundreds of GPU cores crunch through massive amounts of parallel data, is now the literal blueprint for every AI data center on earth.
ChatGPT runs on NVIDIA GPUs. Claude runs on NVIDIA GPUs. Gemini, Llama, Midjourney, nearly every major AI model you've heard of was trained on NVIDIA hardware using CUDA, the software platform Jensen built for a market that didn't exist yet.
NVIDIA was worth about $7 billion when Jensen gave this talk. It is worth over $4.4 trillion today. That's a 600x increase. Jensen Huang, who founded the company at a Denny's in 1993 with two friends, now has a net worth of over $160 billion. He made Forbes' list of the 10 richest people for the first time this year.
GTC 2026 is currently ongoing. 17,000 people are packing a hockey arena to watch the same guy explain what comes next. In 2009, 1,500 people showed up at a hotel ballroom, most of them for gaming graphics.
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Bill Tai retweetledi
5 minutes ago, @karpathy just dropped karpathy/jobs!
he scraped every job in the US economy (342 occupations from BLS), scored each one's AI exposure 0-10 using an LLM, and visualized it as a treemap.
if your whole job happens on a screen you're cooked.
average score across all jobs is 5.3/10.
software devs: 8-9.
roofers: 0-1.
medical transcriptionists: 10/10 💀
karpathy.ai/jobs
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Bill Tai retweetledi
Every technology is a double edged sword ⚔️
Bin Xie@bxieus
It's now official：China started removing its 700M surveillance cameras nation-wide！The reason？Iranian govt has been using Chinese-made surveillance cameras for years and the Israeli intelligence hacked into all of them, which resulted in the death of Khamenei. What Iranians have been using are the same as what Chinese have been using：all made by Hikvision and Dahua of China. Xi Jinping doesn't want to end up like Khamenei.
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it's about time... just hope it's not too late to start the process of catching up. We need something like the Telecom Deregulation Act of 1996 to incentivize the private sector to take on the challenge of remaking our grid; that will speed things up considerably!
U.S. Department of Energy@ENERGY
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Decades ago, there were a dozen credible and well funded players that could compete at the then 'state of the art' megabit densities.. wild swings in part pricing was the result. Now it's down to three players, and the barrier to entry is enormous.
The architecture of memory subsystems has also changed quite a bit as has the margin structure of the end markets. Sure - I get why the P/E multiples have been low at past cyclical peaks as a result. I do think that the changes in industry structure and end use markets are significant enough though, for a 're-rating'.
Even if $mu's P/E does not rise to the same level as leaders like $nvda (although their net income margin structure may get pretty close in a quarter) , if it gets half way there, it'll drive a significant gain in $mu's market cap and share price.
Dean Marantis🇺🇸🇬🇷@Deenobrown123
I’m tired of the cyclical story for Micron. People act like that money is not real money or something. Micron will make $40 billion dollars in 2026. Micron will make more money in 2026 than Tesla, Palantir, Wal Mart, and Costco combined. Not just more than each of them, but more than each of them combined. Think about that. Those 4 companies have a total market cap of over $3.1 trillion dollars. Micron’s market cap is $450 billion. Plus micron may nearly make double of what they make in 2027. Micron is growing faster and making more money than them. Plus Micron is sold out for all of 2026 and 2027 will be sold out as well. Yet, Micron is worth 1/6th of what they are worth combined. Even if you only know 3rd grade math, you must realize that Micron is a great buy right now. Not a single stock in the American stock market comes close.
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Bill Tai retweetledi
She was told “no” more than 100 times. Now hundreds of millions of people use what she built.
Melanie Perkins @MelanieCanva, co-founder @canva
and Bill Tai @KiteVC
– born 1987, Perth
– mum a teacher
– dad a Malaysian engineer with Filipino and Sri Lankan roots
– no family money — just discipline
age 14
– selling handmade scarves at local markets
– training figure skating at 4:30 a.m.
– learning to show up when no one’s watching
at university
– tutors graphic design to pay the bills
– watches students struggle with Adobe Photoshop for an entire semester
thinks:
– design software should be stupidly simple
– online
– collaborative
first attempt
– 2007 — launches Fusion Books from her mum’s living room
– drag-and-drop yearbooks for schools
– grows into one of Australia’s biggest yearbook companies
the crazy idea
– if teenagers can design yearbooks
– anyone can design anything
so she decides to rebuild the entire design industry
the wall of “no”
– flies to Silicon Valley at 24
– no network
– no big-name backers
– no CS degree
investors don’t take her seriously
– hears “no” more than 100 times
rock bottom
– writes a letter to herself just to keep going
– promises she’ll find the team
– the money
– the product she sees in her head
hacking access
– learns kitesurfing to enter a VC’s invite-only circle
– meets @KiteVC
then
– gets introduced to Lars Rasmussen
– spends a year seeing every CTO candidate reject the idea
– finally lands ex-Google engineer Cameron Adams
2013 — Canva is born
– launches at 26
– early press includes negative reviews
– no big ad spend
just a product people quietly start telling friends about
then the curve
– millions of users within a few years
– 2018 — unicorn
– soon after — tens of billions in valuation
today
– hundreds of millions of users
– tens of millions paying
– used in 190+ countries
and she stays in Sydney
– no Silicon Valley move
– no flashy billionaire lifestyle
she marries co-founder Cliff Obrecht with a $30 street-market ring
the plot twist
– signs the Giving Pledge
– commits to giving away most of their wealth
– pledges the majority of their Canva shares to philanthropy
– already sending tens of millions directly to families in need
what she didn’t have
– no technical degree
– no rich parents
– no insider network
what she did have
– a classroom insight
– an obsession with simplicity
– the stomach for 100+ rejections
because the difference between a crazy idea and a global company
isn’t the pitch deck
it’s who keeps building after everyone else stops.
youtu.be/GUjt0iRJ3eo
YouTube
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