Exponential View 🔮

Here's @ExponentialView analysis of the solar supercycle exponentialview.co/p/solar-superc…

"Golf courses in the US consume more than 30x as much water as the entire country’s data center industry uses for cooling" @azeem @ExponentialView

Pakistan's people installed solar panels in record numbers because their government couldn't be trusted to keep the electricity on... As a result, Pakistan hasn't been as badly hit by the Iran war as other similar countries, argues @ExponentialView

Chinese renewable energy is far more ambitious than just EVs or grid electrification. This interactive chart from @ExponentialView by @azeem is a great way to see it: as solar gets cheap enough, it does not just take share in power generation. It starts to make entire incumbent systems vulnerable. Readers can play with the assumptions on the original site and see how quickly the timing changes. A few examples of what ultra-cheap solar can start to unlock: Grid power is the most obvious one. Global electricity generation reached about 30,900 TWh in 2024, and solar still provided only 6.9% of it, which shows how early we still are. Aviation fuel is another. Global airlines’ fuel spend was projected at about $291 billion in 2024 and $248 billion in 2025, so this is already a roughly $250 billion to $300 billion annual market at current fuel prices. Industrial heat may be even more important. Industry accounts for nearly 40% of global final energy demand, and much of that energy is used in heat-intensive processes. That implies an enormous incumbent fossil energy market, plausibly well above $1 trillion a year on a rough back-of-the-envelope basis. Green hydrogen is especially interesting because it first displaces existing fossil hydrogen demand in refining and ammonia, and then could move into much larger industrial systems like steel. It is already being piloted at scale, and there are strong reasons to think it will ramp seriously over the next decade. And then there are areas like desalination and carbon capture, where the value is harder to summarize as a single market number but could be enormous in social and economic terms. Link to the chart in the comments. It is worth playing with the assumptions yourself to see when different cost thresholds start to make these incumbent systems vulnerable.

Solar does not care about the Strait of Hormuz. Oil flows through chokepoints. Solar follows a learning curve. Great piece @azeem exponentialview.co/p/solar-superc…

We are living through the solar supercycle, the self-reinforcing loop in which every cost reduction of solar opens a new market and every new market funds the next cost reduction which opens the next market. exponentialview.co/p/solar-superc…

"Claude’s mobile app saw over 500,000 downloads on Saturday – its biggest day on record." @azeem @ExponentialView

"In Feb 2025, ChatGPT held 90% of US business AI subscriptions. A year later, Claude commands nearly 70%." @azeem @ExponentialView

Non-techie, casual: 1. @emollick's twitter account and his blog oneusefulthing.org 2. @random_walker's twitter account and the blog normaltech.ai 3. @cblatts 4. @krishnanrohit 5. @exponentialview's newsletter: exponentialview.co 🧵

Congratulations to @azeem and the team at @ExponentialView on a new milestone: 150k subs on Substack. 1400+ posts across 10 years. Remarkable catalogue of the rise of AI in the public sphere. Here's to 150 more!

"This looks like high volatility in vendor choice but low volatility in aggregate demand. A competitive market with stable spending but a stalling total addressable market for direct subscriptions." @azeem @ExponentialView

Not an AI researcher, but does feel like that the current RL training paradigm inherently implies a lot of wasted compute. Also, why the pay packages for top researchers are so high as they can save millions (maybe billions) of compute with more intelligent design & workflows as Dylan Patel has also highlighted. "These systems extract gains from post-training reinforcement learning (refining answers through trial-and-error) and extended inference-time reasoning. Compute is paid per query, not once during pre-training. Ord estimates that this burns 1,000 to 1,000,000 times more compute per insight than traditional training. Returns shrink faster to get to the next milestone." @ExponentialView @azeem

We have a chance to rebuild a better internet – one that’s faster, safer, and fairer. I discuss the future of the web and AI with @Cloudflare's co-founder & CEO Matthew Prince (@eastdakota), who's uniquely positioned to see what’s changing.

There are many thinkers whose writing has inspired me over the years. They fall into three clusters: - Essays that sharpen our intuition around exponential trends, - Essays that help us think in systems, - Essays that train us to recognize patterns across history.

Reversible computing. Interesting space to follow. @azeem wrote about this over a year ago exponentialview.co/p/reversible-c…

🧵🫧 1/ Is AI a boom or a bubble? I built a five-gauge dashboard to separate vibes from fundamentals. The full essay is free to read today.

For OpenAI to beat the Nasdaq... It would need a $1.5 trillion valuation by 2030. I think this is possible and I break down why here: youtube.com/watch?v=7XQvVt…

I discuss the recent research by @erikbryn, @econ_b and @Ruyuchen here: youtube.com/watch?v=mhT0WC…

AI and the future of media – with @TheAtlantic CEO, Nicholas Thompson youtube.com/watch?v=pKYQe5…

This conversation with @OpenAI CPO Kevin Weil (@kevinweil) was shot before GPT-5's release but somehow it's even more relevant now.