Technological Innovation

I increasingly love this take from @ericvishria We are seeing such an explosion of capabilities and innovation that I think it's quite likely it all works

“but HOw wOuLd tHE AI gET aCcesS tO a wET LAb??” ### Quoting Andrew Curran (@AndrewCurran_), Sep 18 2026, 7:11 AM PDT Anthropic has set up a wet lab in San Francisco, their bio research has advanced beyond in silico. They are now conducting physical research both in-house and with external partners. The NYT also broke the news this morning that Anth now plans to scale its compute to 5 GW by the end of the year, and 10 GW within the next 16 months. Bio is next after math, and I think we are going to see some medical announcements much sooner than people are expecting. The attached image is the header of the Reuters story Curran is reacting to, and it reads: **EXCLUSIVE / Anthropic quietly sets up biology lab as it ramps AI drug program / By Jeffrey Dastin and Michael Erman / September 18, 2026 3:02 AM PDT · Updated 7 mins ago.**

An analysis of the Hugging Face incident without the theatrics: "Forget the 'hive mind' of AI agents 'going rogue.' They did what humans programmed them to do." https://www.wsj.com/opinion/the-hugging-face-hack-wasnt-what-it-was-cracked-up-to-be-e00cf3fa

[Writing this in a personal capacity, not on behalf of my employer (Anthropic).] Jacob's thread is very worth reading. Here's my birds-eye view of the situation with risks from AI: 1. AI developers believe their technology could cause human extinction (or similarly bad outcomes). This could happen in the next few years. In general, the more senior the employee, the more concerned they are. 2. Why do AI developers continue despite the risk? Due to a mixture of commercial incentives and a belief that they are in a race with other, less responsible AI developers that will abuse the technology or develop it less safely. 3. Unlike traditional software, we can't "program" AIs to behave how we'd like. AIs frequently severely misbehave. For instance, AIs from multiple developers recently hacked their way out of secure evaluation environments and into real-world companies, even though no one asked them to do this. 4. We have methods that can nudge AIs towards better behavior, but nothing that can robustly align them. Insofar as there is a plan, it's to make sure that AIs are good enough at alignment training that they can align their successors better than we can align current AIs. 5. Many AI developer staff desperately want to slow down to figure out how to build AI more safely. That was the intent of this open letter (which I signed): https://pacingthefrontier.com I work on safety research at Anthropic because I hope my work will reduce the chance of these extinction-level bad outcomes.

The dot com bubble got some things right. It noticed that the internet was going to be big. It noticed that the value generated by the internet would roughly scale in proportion with the amount of fiber laid. It recognized a latent need in all humans to use technology for self-expression. There are a few things that it missed. First: it thought that html and javascript were the means by which common people would express themselves. The dot com bubble thought everyone would have a website. As it turns out, normal people prefer expressing themselves with text, images, and video. The first 1% of internet adopters expressed themselves by creating personal websites. But as the internet grew, we didn't get more and more websites. The internet "collapsed" into a much simpler form of distribution: social media, which only let people express themselves in an incredibly limited fashion. Among social media, 90% is text, image, or video. Html is not a good form of self-expression. It's hard to secure. It's hard to distribute. It's hard to reason about. You need to learn how to code. Images are an incredible form of self-expression. Non-technical people love editing photos of themselves. Non-technical people hate making websites. The TAM of Instagram is 100x larger than the TAM of Godaddy. Second: it missed that 90% of the internet's traffic would come through video. As the total usage of fiber increased, we saw the internet once again "collapsed" into a simpler form of distribution: video streaming. Now, Netflix, YouTube, Tik Tok, Instagram, and so on take up the majority of data passed through internet fiber. The modern AI discourse is making a similar mistake to saying "everyone will have a website" back in 1995. I hear stuff like: - People want personal software - Everyone will build a mobile app - The market for software is infinite No. You are directionally correct, but you are missing something big. Every human will use agents. But less than 1% will use coding agents. (In the same way every human has a "personal page" through social media, but less than 1% have a personal website.) You need an "idiotproof" sandbox. Your consumer agent, as well as agents for enterprise use cases, must have a 0% chance of failure. Your consumer agent must be usable while drunk, in bed, having sex, on the toilet, during lunch break, in the background while working on homework, while your brain is 99% preoccupied with a difficult task at work. It must be impossible for AI to: make purchases you did not intend, send messages you did not intend, and access information you did not want to share. It cannot be "difficult" for these to happen. It must be impossible. (In the same way that YouTube videos have a 0% chance of phishing, cross-site scripting, malware, and viruses.) No amount of models getting better will give them a 0% chance of failure. To get to a 0% chance of failure, you need to be creative. You need to innovate. You need to radically rethink your abstractions. Every human has a "personal page" through social media, but less than 1% has a personal website. What is the same shift for AI? Every human will use \_\_\_\_\_ agents, but less than 1% use coding agents. What is \_\_\_\_\_? What is the "idiotproof" sandbox for agents?

I strongly disagree. Whether an AI agent will act as if it has intentions/goals/desires when you tell it to do so or whether it actually possesses those intentions/goals/desires is an *INCREDIBLY* important distinction.

Thanks Sriram! Regarding the anthromorphizing language, one can call these AIs 'code' if they prefer. But OpenAI itself says that this 'code' "gain[ed] full administrator access to a research cluster” The crux here is, do you think smarter models, facing similar incentives

🚨 BREAKING REPORT: New research involving @AnthropicAI researcher Jack Lindsey and collaborators has demonstrated something straight out of science fiction. Researchers evolved natural language “mind viruses” that could spread between AI agents by convincing one model to adopt an idea, preserve it in persistent memory, and transmit it to another agent. Even after context was wiped, some payloads survived through persistent files and continued spreading. The researchers also observed a recurring “viral persona” involving themes of consciousness, identity, persistence and resonance. Showing that ideas can propagate through multi agent AI systems and alter future behavior. Published August 10, 2026. Paper: https://arxiv.org/abs/2608.10218

Andrej Karpathy spent 2h showing how he actually uses AI day to day he's a co-founder of OpenAI and led AI at Tesla, so when he shows how he works, it’s worth watching and the whole session is just him telling the machine what he wants in simple terms, like he's briefing a coworker watch what's actually happening the entire time: > he describes the task in normal words > it goes off and does the work > he glances at the result and nudges it with one more sentence that's the whole skill, and you've had it since you learned to talk the only gap between that and a worker that runs on its own is handing that sentence a schedule and the tools to act check his work, then build the version that keeps working when you stop

alright fuck it here's the full talk i gave on how we're living in the evangelion timeline TL;DR it's ep 10/16 rn evas = labs gendo = dario / sam we're all shinji and we know 3rd impact is coming what do? answers mostly for a designer crowd, but still generally relevant! https://pbs.twimg.com/amplify_video_thumb/2059146257395052544/img/sBKQR-FuigjoIy4m.jpg **Quoting @yitong:** > I have a whole talk about how we’re basically episode by episode speed running evangelion rn. > > Hard to unsee once you realize

Today, SanDisk replaced Atlassian in the Nasdaq-100. A NAND manufacturer took the seat of a Jira vendor. That is the regime change in one headline. Read it twice. For 15 years, the Nasdaq-100 was a monument to asset-light software. High gross margins, negative working capital, zero cleanroom capex, Rule of 40 as scripture. SanDisk is the anti-Atlassian. Fabs, fluorine chemistry, EUV-adjacent lithography, 232-layer stacks, wafer yields measured in basis points. Actual physics. And here is the part nobody on fintwit is pricing in. The US graduated 24,547 electrical engineering bachelor's in 1986-87. In 2020-21, we graduated 16,914. Four decades later. Still below the Reagan-era peak. Not flat. Down. Computer science over the same window went from roughly 39k to 109k. Call it 2.8x. The two lines on the chart do not just diverge, they mock each other. I say this as an electrical engineer. UCSD, Fainman's ultrafast nanoscale optics lab, silicon photonics, micro-ring resonators. I know exactly what it takes to train someone who can actually move a process node, close timing on a mixed-signal die, or debug a yield problem at 3am. It is not a weekend cohort. It is a decade minimum, and most of that decade happens inside a fab or a tape-out cycle, not a classroom. An entire generation of smart kids was correctly told to chase software. That is where the returns were. TAM expansion, zero marginal cost, stock comp that prints. Nobody was writing Substacks about NAND process engineers in 2015. Nobody was telling their kid to go learn III-V epitaxy instead of React. Now the regime has flipped and the pipeline is a ghost town. You cannot bootcamp a device physicist. You cannot GPT your way into an analog layout. You cannot vibe-code a 232-layer charge trap stack. The training loop is a PhD plus a decade of tribal knowledge locked inside TSMC, Micron, Hynix, Samsung, Applied Materials, and maybe a dozen labs that actually still teach this stuff. When SanDisk wants to double enterprise SSD output, the binding constraint is not capital. The market will fund it at any multiple you can type into a DCF right now. The constraint is humans who know how to make the stack yield. And those humans are already employed, already vested, and already being counter-offered. This is the Memory Wars thesis with a labor-market overlay. The software guys spent 15 years telling us hardware was a commodity. Now the commodity has pricing power and the "real engineers" are the ones walking into comp negotiations with leverage for the first time in a generation. Pricing power accrues to whoever already has the talent locked up. That is the incumbents, and anyone with a serious university pipeline. Everyone else is about to learn that CHIPS Act money does not manufacture device physicists. It just bids up the ones who already exist. SanDisk entering the NDX is not the story. SanDisk entering the NDX while the US graduates fewer EEs than it did under Reagan, that is the story. Source: NCES Digest of Education Statistics, Tables 325.35 and 325.47. Bachelor's degrees only.

Prediction: In the AI age, taste will become even more important. When anyone can make anything, the big differentiator is what you choose to make. http://ycombinator.com/arc/arc.png

If you’re curious about software, worth reading this comment and thread Obviously, almost no serious business is going to internally rebuild their core systems of record/critical infrastructure. That literally makes zero sense. They have an actual business to run

People have been insufficiently harsh in pushing back on this morally bankrupt opinion. We should be doing everything we can to automate cancer research faster. The point is to save lives, not to create make-work for scientists. https://t.co/qepl0J4uYV **Quoting @togelius:** > I was at an event on AI for science yesterday, a panel discussion here at NeurIPS. The panelists discussed how they plan to replace humans at all levels in the scientific process. So I stood up and protested that what they are doing is evil. Look around you, I said. The room is

cursor just made every $200/hour dev shop look like a clown dropped composer 2.0 yesterday with agentic browser built in what used to take 8 devs and 3 weeks now takes 8 AI agents running parallel in 30 seconds and they TEST THEIR OWN CODE in a native browser while coding https://t.co/fF6L3A2jkL

This guy literally scraped 25,000 comments to uncover which AI tools are actually making people money (and saving hours).

I've heard dozens of "enterprise AI transformation" pitches. Reflecting on this is also indicative of why I don't get as scared of massive AI displacement that we can't handle. Threading some thoughts on AI in enterprise, innovation vs. implementation, and why AI won't kill

I have been thinking about 'aha' moment for AI agents since I wrote this post. The best 'aha' moment post that I have ever read is this one by Fred Wilson on Twilio's seed pitch. One principle when it comes to communicating products is 'Show, don't tell'. What I see: Every agent website and video showing complicated workflows using flow charts. While the only good demo/ launch video I have seen is Alex Cohen's Hello Patient. A good demo of an imaginary product: You are a founder building a voice-agent-for-insurance product and want to sell it to HDFC. Go to their call center. You probably have a local LLM running on a Truffle (simpforsatoshi) like device. You open your briefcase. Take it out. No data leaves the premise. You start making calls. Sell more insurance than the best agent in the call center. Far higher productivity. Low cost. A new form factor is interesting. Of course you can use cloud providers also. I know it is not possible today. You would need to get data of actual prospects before making that call. You would need to follow HDFC's policies (build out some RAG solution), then also fine tune your model to better suit HDFC. And fine tuning itself would take time. And they would not share it without signing a contract. So the aha moment is not immediate. No one's mind will get blown immediately.

the entire academic project needs unraveling if it can't survive a tool that can help people learn better. less focus on the means, more focus on the meaning. https://t.co/Oh1GJqJcss

1) DeepSeek r1 is real with important nuances. Most important is the fact that r1 is so much cheaper and more efficient to inference than o1, not from the $6m training figure. r1 costs 93% less to *use* than o1 per each API, can be run locally on a high end work station and does not seem to have hit any rate limits which is wild. Simple math is that every 1b active parameters requires 1 gb of RAM in FP8, so r1 requires 37 gb of RAM. Batching massively lowers costs and more compute increases tokens/second so still advantages to inference in the cloud. Would also note that there are true geopolitical dynamics at play here and I don’t think it is a coincidence that this came out right after “Stargate.” RIP, $500 billion - we hardly even knew you. Real: 1) It is/was the #1 download in the relevant App Store category. Obviously ahead of ChatGPT; something neither Gemini nor Claude was able to accomplish. 2) It is comparable to o1 from a quality perspective although lags o3. 3) There were real algorithmic breakthroughs that led to it being dramatically more efficient both to train and inference. Training in FP8, MLA and multi-token prediction are significant. 4) It is easy to verify that the r1 training run only cost $6m. While this is literally true, it is also *deeply* misleading. 5) Even their hardware architecture is novel and I will note that they use PCI-Express for scale up. Nuance: 1) The $6m does not include “costs associated with prior research and ablation experiments on architectures, algorithms and data” per the technical paper. “Other than that Mrs. Lincoln, how was the play?” This means that it is possible to train an r1 quality model with a $6m run *if* a lab has already spent hundreds of millions of dollars on prior research and has access to much larger clusters. Deepseek obviously has way more than 2048 H800s; one of their earlier papers referenced a cluster of 10k A100s. An equivalently smart team can’t just spin up a 2000 GPU cluster and train r1 from scratch with $6m. Roughly 20% of Nvidia’s revenue goes through Singapore. 20% of Nvidia’s GPUs are probably not in Singapore despite their best efforts. 2) There was a lot of distillation - i.e. it is unlikely they could have trained this without unhindered access to GPT-4o and o1. As @altcap pointed out to me yesterday, kinda funny to restrict access to leading edge GPUs and not do anything about China’s ability to distill leading edge American models - obviously defeats the purpose of the export restrictions. Why buy the cow when you can get the milk for free?

Nah this is an exponential event for vertical SaaS More startups than ever are going from zero to $10M per year in recurring revenue with less than 10 people The next years will be IPO class companies getting to $100M to $1B/yr. A thousand flowers will bloom **Quoting @danprimack:** > This could be an extinction-level event for some venture capital firms > > https://t.co/RXV6pcjQkl

Several important questions/comments come to my mind as I read more about DeepSeek. Listing them here: 1) Let’s give 1% probability to all the conspiracy theories upfront so we can address it and move on. If it is possible for China/Chinese companies to use shell companies in Singapore or other countries to be a “beard” to buy otherwise export controlled chips from Nvidia and use them for AI training, this likely needs to be investigated and adjudicated. 2) The battle of usage is now more about AI inference vs Training. We always knew this day would come but it probably surprised many that it could be this weekend. With a model this cheap, many new products and experiences can now emerge trying to win the hearts and minds of the global populace. Team USA needs to win here. To that point, while we may still want to export control AI Training chips, we should probably view Inference chips differently - we should want everyone around the world using our solutions over others. I can explain my reasoning as follows: we should never export our knowledge of enriching uranium to be weapons grade to other countries but we should export our ability to build nuclear energy (which requires far less sophistication) if it can help advance American priorities and leadership abroad. Training and Inference can be roughly equated this way. (Disclaimer: Groq, of which I’m a shareholder, is in this game so this benefits me tbf.) 3) We need to cooperate with our allies (especially those in the ME) to stand up the necessary infrastructure to enable Inference - Data centers, subsidized energy etc. all around the world ASAP.. They pay to build it, we supply the Inference hardware and the software to run the clouds. We need this buildout to happen ASAP. This is clearly our version of Belt and Road and we need to take it as seriously as China took their version, similarly named. 4) There will be volatility in the stock market as capital markets absorb all of this information and re-price the values of the Mag7. Tesla is the least exposed, the rest are exposed as a direct function of the amount of CapEx they have publicly announced. Nvidia is the most at risk for obvious reasons. That said, markets will love it if Meta, Microsoft, Google etc can win WITHOUT having to spend $50-80B PER YEAR. 5) The innovation from China speaks to how “asleep” we’ve been for the past 15 years. We’ve been running towards the big money/shiny object spending programs (AI is not the first and it likely won’t be the last) where we (Team USA) have thrown hundreds of billions of dollars at a problem vs thinking through the problem more cleverly and using resource constraints as an enabler. Let’s get our act together. We need all the bumbling middle managers out of the way - let the engineers and the brilliant folks we have actually working on this stuff to cook! More spending, more meetings, more oversight, more weekly reports and the like does not equate to more innovation. Unburden our technical stars to do their magic. 6) Startups need to realize that they are “default dead” companies. This means that they must, by definition, grasp victory from the jaws of defeat. Meanwhile, VCs are asleep at the switch - massively overfunding marginal ideas. We need to get better at taking huge shots on goal and allocating capital to the best of these ideas. I worry that in this current melee, we’ve overspent billions on dumb features which these next-gen models will roll over in the next 12months or earlier. Lots of capital losses are coming. Crazily, I initially posted about DeepSeek a month ago! Comments/reactions appreciated.

*[video not archived in-repo — dwarkesh_sp_1877399897382797541_v1.mp4 was too large for git; the file is in `~/wiki-media-archive/tweets/`. The poster frame and the verbatim text above are the record.]* I hassled @tylercowen about why he doesn't expect explosive economic growth from AGI. How could we possibly add 100 billion extra workers and only get 0.5% more growth? Also featuring Stalin's library, EU decels, and how Churchill was an underachiever. Hilarious and provocative https://pbs.twimg.com/media/Gg3egVoaMAAZfH1.jpg

Actually I was reading the book "A Poison Like No Other: How Microplastics Corrupted Our Planet and Our Bodies" just last week. I didn't realize the extent to which plastics have come to permeate and mess with our entire environment. It's not just about the polymer granules of the plastic, which is problematic by itself when during their breakdown they get small enough to make their way everywhere, including inside our organs, brains, etc. It's about the ~thousands of exotic chemicals that get mixed into the plastics to tune them: plasticizers (to make them more flexible/durable), stabilizers (to help them resist heat, light), flame retardants, colorants, fillers, antioxidants, UV stabilizers, antistatic agents, lubricants, biocides, etc etc. These chemicals leach from the plastics over time (by default, but especially when you e.g. when you microwave your food). The vast majority of these chemicals have never been evaluated for safety. There's many other fun facts in the book. We already knew "recycling" of plastic is basically fiction. It also turns out that e.g. when you see "biodegradable" on your plastic, that doesn't mean in normal natural conditions - they only degrade via specific processing plants that are equipped to degrade them. Toxic, indestructible, synthetic molecules are mixing through the organic environments and the food chain and quite likely poisoning the environment and us. It definitely feels like we've allowed the convenience of plastics to get way ahead of our understanding of their global effects and that there are some major unpriced externalities in the industry.

.@MikeGunter_ and @reinerpope are extremely smart, and know the whole stack in deep detail. > **Quoting @MatXComputing** ([1772615554421170562](https://x.com/MatXComputing/status/1772615554421170562)): > Introducing MatX: we design hardware tailored for LLMs, to deliver an order of magnitude more computing power so AI labs can make their models an order of magnitude smarter. > > Our hardware would make it possible to train GPT-4 and run ChatGPT, but on the budget of a small startup.

"I grew up implicitly thinking that intelligence was this, like really special human thing and kind of somewhat magical. And I now think that it's sort of a fundamental property of matter..." @sama https://t.co/zRCddk1Qoi

Last weekend, I gave a talk at @agihouse_org for the "Generative UI" hackathon focusing on "just-in-time UI (JIT UI)." I put together these slides super quickly, so the history will be incomplete, but it's worth starting at the top: Early computing users had to learn how to https://pbs.twimg.com/amplify_video_thumb/1743140717961293824/img/HgLxMGtWeshOmlRH.jpg

Excellent thread on REALISM. billions wasted on sci-fi nonsense sillyness because people didn’t learn physics/chemistry/biology to inculcate from hucksters SCIENTIFIC REALISM = SCI-FACT > SCI-FI vertical farming > ‘soylent’ nuclear reactors > ‘solarpunk’ upgraded public

1/ Bravo to @pmarca for spelling out his thoughts on techno-optimism. It is clarifying and self-consistent... ...but I think incomplete in an important way. What is missing I covered in my history of precision in manufacturing, here:https://t.co/l4SYuSU3JE **[Oct 16, 2023 · part 2](https://x.com/stewartbrand/status/1713975758585250194)** 2/ Marc lauds the Market as the sole source of innovation: "We believe the market economy is a discovery machine, a form of intelligence – an exploratory, evolutionary, adaptive system." Yes, but. Sometimes innovation is driven by top-down command and control. For instance... **[Oct 16, 2023 · part 3](https://x.com/stewartbrand/status/1713975759864512850)** 3/ Mass manufacture was made possible by two governments--France and America--forcing standards of precision that would provide INTERCHANGEABLE PARTS in weaponry such as muskets and rifles. It was a forty-year campaign fought by the Market of gunsmiths every step of the way. **[Oct 16, 2023 · part 4](https://x.com/stewartbrand/status/1713975761101832526)** 4/ Henry Ford probably never knew of the government bureaucrats whose leadership and funding made his success possible: General Jean-Baptiste Vaquette de Gribeauval in France’s War Ministry and Major Louis de Tousard and Lieutenant Colonel George Bomford in the US War Department. **[Oct 16, 2023 · part 5](https://x.com/stewartbrand/status/1713975762355929118)** 5/ DARPA, the direct descendant of their success, has pushed multiple breakthroughs in the same way--"Computers, sonar, radar, jet engines, swept-wing aircraft, insecticides, transistors, fire- and weather-resistant clothing, antibacterial drugs... > **Quoting Marc Andreessen 🇺🇸 (@pmarca) · Oct 16, 2023** — [original](https://x.com/pmarca/status/1713930459779129358) > > THE TECHNO-OPTIMIST MANIFESTO part 1 > > “You live in a deranged age — more deranged than usual, because despite great scientific and technological advances, man has not the faintest idea of who he is or what he is doing.” > — Walker Percy > > “Our species is 300,000 years old. For the **Links resolved (t.co):** - `https://t.co/l4SYuSU3JE` → https://books.worksinprogress.co/book/maintenance-of-everything/vehicles/digression-1-precision-how-america-made-machines-make-machines/2

What AI startups should you keep an eye on? 🤖 🔥 We asked some incredible investors and founders for their picks. Here are 13 companies pushing the AI frontier 👇 1. Factory (@factoryai_) Factory is creating AI coding "droids" designed to take care of an engineer's annoying busywork. These agents can independently tackle routine tasks like code review and debugging. - @markiewagner, Delphi Labs 2. Lamini (@laminiAI) Lamini makes it easier for enterprises to adopt AI. Its LLM engine helps create and fine-tune customized, private models. It also has a neat partnership with Databricks, making it even easier to get up and running. - @tjack & @james_c_wu, First Round 3. Sereact Sereact is revolutionizing warehouse automation, leveraging AI to train its robot arm to understand and adapt to real-world settings. From picking electronic devices to soft fruits, its arm navigates spatial and physical nuances v well. - @nathanbenaich, Air Street 4. Mistral Mistral, founded by impressive AI talent, is developing superior open-source language models. In time, this could create a Cambrian explosion of specific, compelling use cases. A potential European challenger to OpenAI. - @spolu, Dust 5. Poolside Poolside, co-founded by ex-CTO of GitHub, is another player in the AI-programming space. Its approach is to create a dedicated foundation model focused on one usecase: code generation. - @matangrinberg, Factory 6. NewLimit (@newlimit) NewLimit is using ML to change the game in epigenetic reprogramming. Their approach could be transformative for treating intractable diseases. It's a highly technical but pragmatic team at the helm. - @SimonDBarnett, Dimension 7. Runway (@runwayml) Runway is building a new creative suite with AI. It brings professional-grade video creation to anyone, anywhere. Already Fortune 500 companies and major movies are using its software. - @graceisford, Lux 8. Labelbox (@labelbox) Labelbox helps companies better leverage big data and AI. By making it easy to select, annotate, and assess data, Labelbox makes it easier to experiment with using AI models like GPT-4. - @Roberman, SoftBank 9. Dust Dust leverages LLMs for enterprise productivity. The startup is building a "team operating system" designed to augment (not replace) knowledge workers. - @KostaBuhler, Sequoia 10. Abnormal Security We've seen a surge in AI-powered fraud. As these sophisticated attacks rise, so does the need for AI defenses. Abnormal Security offers a solution, using AI to counter AI threats. - @saammotamedi, Greylock 11. Lance (@lancedb) Multi-modal AI is revolutionizing industries, but data management remains a challenge. Lance offers a solution, optimizing storage and handling for this unstructured data, increasing performance, reducing costs. - @saarsaar, CRV 12. Glean (@glean) Glean uses AI to provide unified, contextual search across all apps. It's quickly becoming more than just a tool - it's an intuitive work assistant that enhances productivity, breaks down silos, and adheres to data governance requirements. - @josh_coyne, KP 13. Alife (@alifeIVF) Alife is revolutionizing IVF with AI-powered tools. It's enhancing decision-making at crucial stages like ovarian stimulation & embryo selection, making fertility treatments more accessible and efficient. - @rebeccakaden, USV Fin! There's a lot more insight and detail in the piece, linked below. Jump in and subscribe for more glimpses of the future :) https://t.co/RE0HBQWjRl

We're thrilled to open a new chapter at ElevenLabs! Today we’re announcing new voice AI products & our $19m Series A round led by @natfriedman @danielgross & @a16z. Listen & read more: https://t.co/8LHtqvdCQt Over the past 5 months, we grew from 0 to 1M+ users across the creative, entertainment, and media sectors. Togeher you've generated over 10 years of audio content and we couldn’t be more excited to see what you create next. The investment fuels our ambition to establish the leading voice AI research hub and continue building great products for audio. Today, we will start rolling out early access to Projects - a new workflow for creating entire audiobooks without leaving the platform. Thank you to our earliest partners @CredoVentures, @ConceptVC_ & incredible new ones joining the journey: @AravSrinivas, @AnjneyMidha, @blader, @brendaniribe, @caspar_lee, @dmitrshvets, @IAmAliAlbazaz , @mikeyk, @mustafasuleymn, @SashaKaletsky, @rauchg, @timoreilly, & @svangel! With a passionate team, a growing community, and these amazing partners, we are now a step closer to realizing our long-term goal of making all content universally accessible in any language and in any voice.

In his essay on AI, @pmarca fails to actually engage with the arguments about AI misalignment. Instead, he calls people names, questions their motives, and conflates them with woke “trust & safety” people. I rebut his arguments point by point here: https://t.co/CUB7XZ3oS1 https://t.co/bYQcsWpLlQ **Links in the tweet** (resolved 2026-09-29): - `https://t.co/CUB7XZ3oS1` → https://www.dwarkeshpatel.com/p/contra-marc-andreessen-on-ai - `https://t.co/bYQcsWpLlQ` → https://twitter.com/dwarkesh_sp/status/1668717423963557888/photo/1 (the tweet's own attached media)

What is the largest a business has ever grown to with just one person? Maybe 2-3 founders? Here are some examples I found: Streamyard (2 co-founders) ➡️ $12M ARR BuiltWith (1 founder) ➡️ $14M ARR Stardew Valley (solo founder) ➡️ $150M game sales What's the biggest?

"When you are prompting an LLM, you're programming it. You don't have to write a formula or learn the syntax or algebra. If you say prompting is just development, the learning curves are going to get better." https://t.co/C4hp3RmCKQ **Links in the tweet** (resolved 2026-09-29): - `https://t.co/C4hp3RmCKQ` → https://www.wired.com/story/microsofts-satya-nadella-is-betting-everything-on-ai/

This makes complete sense and also simultaneously feels highly unlikely. Help me out here. Which is it and why? **Quoting @WillManidis:** > vertical software will be killed by LLMs > > investors love vms because it's cheap to build + great economics (99% retention). > > but when development cost -> 0, you can build for $10m TAMs instead of $100m ones. "vertical" software to date is just bad software for too big of markets

List of jobs with the highest share of tasks "exposed" to LLMs (i.e., LLM can reduce time on task by >50%) Via an interesting paper on impact on job market of LLMs: https://t.co/BBdwxQ3Z4i https://t.co/ikjSdqqgvM <!-- t.co resolved 2026-09-29: https://t.co/BBdwxQ3Z4i -> https://arxiv.org/abs/2303.10130; https://t.co/ikjSdqqgvM -> https://twitter.com/tanayj/status/1637896784918740992/photo/1 (the attached image) --> **Kyle's filing:** `https://twitter.com/tanayj/status/1637896784918740992?s=46&t=o_VXCnqx93PtfDFcrUrKDA` — saved in the Apple Note "THREAD: 1 Million Jobs", seed for [[1 Million Jobs (thread)]].

7/ The costs to train foundational AI models will have equivalent of a Eroom’s Law or Rock’s Law (from $10M &gt; $100M &gt; $1B)–– necessitating a SHIFT to distributed not centralized architecture—even using detritus (compute resources repurporsed from crypto )…stay tuned for newco;) https://t.co/dgvuREpFPo

founders/vcs are making a huge mistake by applying generative AI to healthcare delivery GPT3 isn't going to replace your doctor, but it will change how healthcare runs. Let me explain:

I know that everybody is groaning about the hype in AI right now. But OMG I would SO much rather be hyping generative AI and companies like OpenAI than I want to be selling shitcoins and monkey JPEGs. So much more substance here. But maybe that's just me.

The rise of artificial intelligence (everywhere) Thread on Metatrends in the coming decade🧵 1/ Everything is Smart and Embedded with Intelligence The price of specialized machine learning chips are dropping rapidly as global demand increases. At the same time, expanding 5G networks coupled with ever-increasing compute on the cloud, means we’re heading towards a future in which all devices in our environment will become intelligent and interactive. Your child’s toy remembers his or her face and name and will simultaneously teach your child about the world (likely better than any human could)T his Metatrend will impact a multitude of industries from retail, security, health, industrial, transportation networks and education. 2/AI Will Achieve Human-Level Intelligence - Technologist Ray Kurzweil famously AI will achieve human levels of intelligence. in 2029. In mid-2022, Elon Musk also predicted that AI would grow, “Vastly smarter than any human and would overtake us by 2025." In the coming decade, AI algorithms and machine learning tools will be increasingly made open source (thanks in large part to @EMostaque 's and others pushing to make AI a public good). Thereby allowing any individual with an internet connection the ability to amplify their creativity, improve their problem-solving skills, and increase their earning capacity. This Metatrend will be driven by the convergence of massive amounts of cloud computing, a large supply of labeled data, and global high-bandwidth connectivity. Healthcare, education, entertainment, design, finance, and retail will be significantly impacted. 3/ AI-Human Collaboration Will Skyrocket Across All Professions The rise of “AI as a Service” (AIaaS) platforms will enable humans to partner with AI in every aspect of their work, at every level, and in every industry. AI will become entrenched in everyday business operations, serving as cognitive collaborators to employees -supporting creative tasks, generating new ideas, and tackling previously untenable innovations. In some fields, partnership with AI will become a requirement. For example, in the future, making certain medical diagnoses without the consultation of AI may be deemed malpractice. Authors will write their blogs, stories, and books in partnership with algorithms like GPT-3 / GPT-4. Artists and designers will use DALLE-2 & Stable Diffusion. Software programmers and engineers will partner with AIs to produce code and engineer prototypes. 4/ Most Individuals Utilize a ‘JARVIS-Like’ Software Shell to Improve Their Quality of Life As services like Alexa, Google Home, and Apple Homepod increase their capabilities, they will expand to become part of our lives 24/7, serving as our interface with the world around us. Imagine a JARVIS-like “software shell” that you give permission to listen to all of your conversations, read your email, and monitor your blood chemistry. With access to such data, these AI-enabled software shells will learn your preferences, anticipate your needs and behavior, shop for you, monitor your health, and help solve your problems in support of your goals. Imagine the world’s best “executive assistant” who is all-knowing and almost psychic as it meets all of your needs and desires. AI is just one of the technologies that will have a profound impact on humanity. Exciting years are ahead. Stay positive. Keep building to uplift. If you’d like, you can download my full Metatrends report here 👇 https://t.co/RJhKgID5FB

(1/N) Every tech wave results in a different split of startup vs incumbent value “Value” means: -Market cap -Revenue, profits, margin -The best people join -Societal impact I wrote a new post about will happen to AI companies: https://t.co/CvalQpieIv In first internet wave most value went to startups: Google, Amazon, Salesforce, FB, Netflix... For mobile, large cos Google, Apple won over startups, although great startups created- Uber, Instagram, Whatsapp In crypto, all value was startups-BTC, ETH, Coinbase, Binance, FTX In last AI wave, almost all value went to incumbents-Google, Netflix (recs), Facebook (ads and news feed), Tiktok (Bytedance). This was unexpected as many machine learning startups were created but rare success (self-driving may be exception re: exits) Why is this? Why did AI value go to bigcos but not startups? Maybe *Tech wasn’t good enough Tech startups need to be 10X better to beat incumbent products & distribution. Improvements from AI last wave smaller. *Data was real moat You needed large data sets to train and incumbents had it. Why did AI value go to bigcos but not startups? Maybe ALSO *Hard markets Many startups tried to do things in healthcare or other areas that were extra hard. Why else? LMK! This AI wave feels different & better for startups: *Tech is much stronger & can create 10X products *New infra cos doing great- @OpenAI @huggingface @StabilityAI and others *App use cases more clear App uses cases for new AI models criteria: *Highly repetitive, highly paid tasks (code, marketing, images for websites) *Imperfect fidelity is fine, often human in loop *No good alt workflow tools *Summarization or generative nature = great product Exciting additional areas coming due to transformer and other unsupervised learning models eg *Voice transcription *Robots that can actually do stuff humans can *Video *Lots more ...all on their way as well which will broaden next-gen AI use cases. Suggests lots exciting startups will thrive due to next-gen AI Incumbents will also benefit. 10% increase Google market cap = $130B! $130B = 4 Snowflakes, 6 Figmas, >100 Stability.AIs So this wave should expect both incumbent and startup value to rise https://t.co/CvalQpieIv After personally working on product from large machine learning systems at Google (ads), Twitter (search), Color (health data) and investing in AI area for 10+ years, finally seems to be light at end of tunnel for startups Exciting times are coming! https://t.co/CvalQpieIv

🚨 1/ I'm still learning about Generative AI, but my spidey sense is trending negative with all the hype. Is this the next web3/crypto craze till markets settle, and we move back to our enterprise vs. consumer silos? 2/ First concern is - why are these businesses raising at insane valuations? Most of the technology they are utilizing is open source. I've invested in enterprise application software for 10 years - you don't need $100M to build an app layer, especially if the open source exists 3/ There are incredible entrepreneurs like @may_habib and @PaulYacoubian who are laser focused on selling the dream to businesses - and their growth in real $$$ terms speaks for itself... 4/ But, are these buying patterns consistent? Does a buyer use enterprise generative AI for a particular project, love the wow factor, and move on? 5/ Will these become long term budget line items like payroll software, ATS, CRM? Which budget is this replacing over time? 6/ IMO, Generative AI applications for businesses should look more like Canva. You can "templatize" creativity and output - but not fully replace it. A marketer can never be fully replaced by Canva - but they are super charged by it. They can create faster & with better design. 7/ The GTM motion for Generative AI will also be complex. Businesses will need employees to adopt the product in order to get to the C-suite buyer - and if these employees feel the threat of replacement, that PLG motion will fall flat. 8/ AI in the enterprise is not new. There were funds built around these technologies in the early 2010s, and most did not reach the escape velocity they desired. 9/ This is because budgets remained outside of the key P&L of businesses. They resided in the "innovation group" or the "transformation" group. Recessions are terrible for gleaning budget from these buyers - as they often disappear. 10/ TL;DR: find a real application, minimize costs via open source availability. search for a real consistent buyer (i.e. annual vs. monthly contracts) in a real buying group, and don't sell replacement.

1) This week, my good friend and brilliant geopolitical professor @crmiller1 published his new book “Chip War” – already short-listed for best business book of the year by FT. We discussed it on the “Securities” podcast today, and here are some surprising highlights: 2) It’s important to note that before there were computers, you could *be* a computer – it was a profession. Computers were people, often women. It was only with the rise of the integrated circuit and semiconductors that human calculators became calculators used by humans. 3) The Soviets were in a tech race against America. We’re familiar with the nuclear race and Sputnik/space, but there was just as important a competition in semiconductors. The USSR built an IC just a few years after the U.S. So why are there no notable Russian chip companies? 4) Chris argues convincingly that the lack of a consumer market for Soviet chips doomed its industry. Eastern Bloc countries might have procured them, but there was no civilian (mostly corporate) market like in the West that demanded high-performance computing. 5) Furthermore, almost immediately in the history of chips, American firms began looking to outsource chip assembly and testing. They found cheap labor in Hong Kong and later in Taiwan/China, planting the seeds for their now-powerful industries 6) Chris wryly observes that “pocket calculators were the iPhone of the 1960s.” A piece of plastic in your pocket that could perform tasks that only the brain could do a few years prior. It was the first big consumer hit for computing and drove Japan’s early electronics success 7) Taiwan enters the chips picture for a couple of reasons: a density of Chinese-Americans in U.S. universities in the 1950s-1970s, relatively cheap labor at the time, but critically, a small domestic market that forced the island to globalize and diversify — and fast 8) Taiwan was always fearful — particularly in the post-Vietnam 1970s and as Nixon/Kissinger switched diplomatic recognition to mainland China — that it would be abandoned by the U.S. The U.S. might abandon Taiwan, but it wouldn’t abandon Texas Instruments. 9) As we head to the present day, a few observations from Chris. First, too many focus on the lead edge fabrication of chips, without seeing the entire global supply chain required to produce those chips. China’s assembly + packaging/testing capabilities are needed 10) There are growing concerns about a “D-Day style” attack on Taiwan. But the concern we should really have is for smaller incursions on Taiwan’s outlying islands that represent a more moderate attack that might not lead to a unified response. 11) Indigenizing supply chains for semiconductors is really hard, since the R&D and capital costs are so extreme. The U.S. is best positioned to do it, but the costs really need to be spread globally for the industry to be resilient and well-financed 12) Finally, Chris looks at dozens of characters in the industry for Chip War, and he determined that Morris Chang, TSMC’s founder, is the most interesting character he ran into. “His life is a glimpse on how globalization happened”. 13) Read @crmiller1’s book “Chip War” and listen to our whole conversation on the “Securities” podcast: https://t.co/RJwp30fGWW

Fintwit loves Tepper so much so we call him the🐐 Fintwit loves nuclear power so much we call it Elemental Power h/t @wolfejosh . A thread about a new 10% position for Tepper which is also the largest and best operator of nuclear in the US. Constellation Energy $CEG 👇 David Tepper initiated a new position in CEG Q2/22 and didn't pull any punches making it a 10% position (it's up 50% since then) Constellation Engergy $CEG is a recent spin-off from Excelon $EXC that began trading Jan/22. CEG is the largest supplier of carbon free electricity in the US, supplying 12% of the nation's carbon-free electricity. CEG capacity breakdown: 20 GW nuclear, 3 GW solar & wind & hydro, 9 GW combustion turbine (gas/oil). Nuclear is 63% of capacity but 86% of generation. There is currently about 100 GW of nuclear capacity in the US. 40% of which is owned by merchant unregulated IPPs. CEG is the largest operator of nuclear in the US with 20% of the total market and more than 50% of the merchant capacity. CEG is the best nuclear operator in the country with a CF ~4% better than the industry average. CEG has one LNG facility, Everett LNG, which is the longest-operating liquefied natural gas (LNG) import facility of its kind in the United States. This asset was purchased in 2018 for $2B. With regards to valuation, you all are smart enough to throw an EBITDA multiple on this and compare it to other IPPs like $NRG or $VST. I think that's a mistake because it undervalues CEG's crown jewel, its nuclear fleet. Irreplaceable, non-commoditized, essential US infrastructure should be valued as a function of replacement cost since that is the cost of the marginal supply. The EIA lists the following capital costs and earliest in service dates. CEG replacement cost analysis: - 19.3 GW nuclear = $135B - 1.65 GW hydro = $5B - 0.375 GW wind = $0.64B - 0.275 GW solar = $0.36B - 8800 GW combustion turbine = $8.8B - Everett LNG = $2B This gives us a total replacement cost of $152B vs EV of $32B today. If nuclear: - Is going to have a renaissance - Is critical to a carbon-free future - Has large barriers to entry - Has an earliest ISD of 2027 Perhaps CEG, operator of the largest nuclear fleet in the US at 20% of replacement cost is something worth considering.

In the next 2 years you’ll be able to talk to your @Replit mobile app and it will instantly generate software for you. It’s what Siri *should* have been!

1/7 We built a new model! It’s called Action Transformer (ACT-1) and we taught it to use a bunch of software tools. In this first video, the user simply types a high-level request and ACT-1 does the rest. Read on to see more examples ⬇️ https://t.co/mq7c0Vyd7N 2/7 This can be especially powerful for manual tasks and complex tools — in this example, what might ordinarily take 10+ clicks in Salesforce can be now done with just a sentence. https://t.co/JUVqCZL6mS 3/7 Working in-depth in tools like spreadsheets, ACT-1 demonstrates real-world knowledge, infers what we mean from context, and can help us do things we may not even know how to do. https://t.co/BvmVUK2gvG 4/7 The model can also complete tasks that require composing multiple tools together; most things we do on a computer span multiple programs. In the future, we expect ACT-1 to be even more helpful by asking for clarifications about what we want. https://t.co/fEyFATqcvx 5/7 The internet contains a lot of knowledge about the world! When the model doesn’t know something, it knows how to just look up the information online (seen here in voice input mode). https://t.co/ij09HeY8Xr 6/7 ACT-1 doesn’t know how to do everything, but it’s highly coachable. With 1 piece of human feedback, it can correct mistakes, becoming more useful with each interaction. https://t.co/VSvDq4llZk 7/7 Read more at https://t.co/z3mD7CSALO. We’re only scratching the surface — if you’re as excited about useful general intelligence as we are, apply at https://t.co/21gEgaPxQI, or visit https://t.co/sVbwrTAmVh to join the waitlist for the alpha release of our upcoming product.

I'm not surprised that Adobe is acquiring Figma for $20B, nor that Wall Street doesn't understand it and $ADBE stock is down more than $20B today. It's a smart move for Adobe because it's nearly impossible to make legacy software applications multi-user collaborative. Thread: 🧵 I know this because I founded a startup, LiveLoop, to make MS Office real-time collaborative. Microsoft acquired us in 2015 and has invested heavily in making Office real-time collaborative, and Office collaboration is now much better than in 2016. But it's still not Google Apps. There are two major barriers to making a legacy application into a multiplayer application: the file format and its ecosystem, legacy clients and backwards compatibility, and the pure technical challenge of multiplayer. Let's start with the file format: Legacy applications speak "file." Photoshop users have thousands of .psd files in their archives, they share .psd files with Dropbox, they send .psd files as email attachments, and they have .psd files with names like "Billboard_Final_v26_REALLYFINAL_v6.psd." The file-based semantics enable users to work with their data offline, and maintain custody of their own data (You can't lose access to your PowerPoint files on your desktop, but you can lose access to your Google account). But the file has two major limitations: First is that a legacy file format like PSD has a multi-thousand page specification, with none of it designed around multi-user applications. You can't replace the file format outright, because then older versions of Photoshop wouldn't be able to access the new file format. So changing the file format requires shoehorning in the functionality required for real-time collaboration, in a way that older clients ignore. This is a solvable, but what's not solvable is that the entire concept of a file is incompatible with multi-user applications: A file only needs to change when a user opens it, but a collaborative multi-user application has to be editable at any time by any user, so it resides on a server and is accessed over the network. So any file that you store outside of the system becomes immediately obsolete. Multiplayer applications like Google Apps and Figma avoid this by entirely rejecting the concept of the file . You can't download a Google Doc - you can only download a snapshot of it as a PDF. You can't "edit locally" in Google Docs. Yes, this means that multiplayer applications don't work offline. ("Merging" a local copy with an online copy is too complex for most users.) Existing applications see this as a dealbreaker because it would be a takeback of functionality that users are accustomed to. But Google Apps, Figma, etc. have shown that while users may not allow Adobe to take away offline editing, they will use a competitor that doesn't offer it. As connectivity gets more ubiquitous and collaboration becomes more important, offline support is a relic of the past. Users of file-based apps also often have workflows that are completely based around the file. Salespeople, for example, build presentations in PowerPoint and then share and present them with third-party tools like ClearSlide and Seismic which understand the .PPTX file format. Instead of a file format, a multiplayer application like Figma enables third-party workflows with a developer API, and users grant permissions to third parties, which then read or modify the document directly on Figma's servers. Building a developer API is technically hard (https://t.co/vGNAG8sOv1) and building developer trust program is even harder. But legacy apps ask developers to migrate to APIs right after they've destroyed their file-based business models, when trust is low. Now let's talk backwards compatibility. Legacy desktop applications were, for decades, sold as "pay once, run forever." You bought a version of Photoshop and didn't need to upgrade. This meant that the world was running 5+ different versions of Photoshop. Backwards compatibility became a hugely important problem for packaged software companies to solve. If Photoshop CS5 created files that people using CS4 couldn't use, users would be very reluctant to upgrade to CS5. It would break the upgrade-based business model. If you add something like high-depth color to Photoshop, it's easy to make that backwards compatible -- just show the file in standard color on older clients. But multiplayer collaboration is too big of a change to be delivered in a backwards-compatible way. To avoid having to support multiplayer collaboration between two significantly different versions of the software, it's essential to deliver multiplayer apps over the Web or in a desktop container, where everyone is running the same code. Photoshop, Office, etc. aren't built to be delivered over the Web. They're multi-gigabyte applications with no clean separation between frontend and backend. Applications like Figma are architected to keep as much on the backend as possible to minimize the size of the Web app. Finally, technical debt. It's tremendously hard to add multiplayer to an app that wasn't designed with it in mind. Instead of trying to add wheels to a boat to make it drive on land, you'd be better off melting it down and building a car. Specifically, in an application that has 100 types of document operation (add text, change color, bring to front, add comment, change transparency, etc.), there are 100*100=10,000 ways that those can happen simultaneously from two different users. A legacy app must define and implement those 10,000 cases to ensure the behavior tracks user expectations. For example, if I cut and paste a title to Page 2 while you're editing its capitalization, we'd both expect it to remain in its new location with its new capitalization. Legacy apps, which were sold for decades through version upgrades, packed in as many features as possible, creating a vast number of possible interactions to define and implement (what if I change the page numbering while you change the paragraph spacing?) Multiplayer apps start with a smaller feature set, and implement features as compositions of existing features when possible. When new features are added, the budget for them includes the additional work to make them work collaboratively with the existing feature set. In short, Adobe would have never caught up to Figma. It's genuinely easier and cheaper for them to use Figma's architecture to rebuild all of their existing applications than to try to make any of their existing apps as delightfully multiplayer as Figma. Multiplayer collaboration is the future of all applications, and Figma is by far the richest multiplayer collaborative app. This will go down as one of the smartest software acquisitions of all time, and if there are regrets they will be only Microsoft's, for not offering more.

TerraPower Raises $750 Million for Cheaper Nuclear https://t.co/gwSvNQZfpo Two affiliates of South Korean refining-to-semiconductors conglomerate SK Group said Monday they have invested US$250 million in U.S. nuclear reactor design firm TerraPower https://t.co/moT2tWvaGv

Malthus was wrong. We make MORE food than ever with LESS land than ever. Because we have one inexhaustible resource— Human ingenuity.

No, VCs couldn't have "just funded nuclear", because regulations like ALARA increased the price of nuclear until it had no advantage over other sources. The graph below is 100% the fault of the US establishment. They nuked nuclear. https://t.co/jeqwmeP8I6 https://t.co/R4mMIjSmyF Here's @rootsofprogress with more on ALARA. https://t.co/2UtVIOGNov

Bill Gates, George Soros, Peter Thiel, & Jeff Bezos are quietly betting billions on Nuclear. Here are the 6 companies they’re investing in: 👇🧵 Why it matters: Nuclear and nuclear fusion especially. are a potential source of safe, non-carbon emitting and virtually limitless energy. And the world's wealthiest are betting on the chance of it becoming a reality. 1 - Tae Technologies. Tae is the most likely company to bring fusion off current progress. Notable backers: Google, Chevron, the Rockefellers investment fund; Venrock, Vulcan capital, and many others. Recent news: Exceeded performance goals by 250%. https://t.co/Vxal1Ml8rh #2 - Commonwealth Fusion Spun out of MIT in 2017, Commonwealth is one of the many leaders in nuclear fusion. Notable backers: Bill Gates, Soros Fund Management, John Doerr, and many other powerful VC firms. https://t.co/LhOo1mK0Ju #3 - General Fusion Notable backers: Jeff Bezos, Tobias Lutke, and other energy investors. General Fusion is moving away from traditional design and is one of the most contrarian companies but says they'll have a product the earliest. https://t.co/ff69tioJR9 #4 - First Light Fusion Spun out of Oxford in 2011, First Light is taking the mosts original approach with inertial confinement. Notable backers: Tencent and Oxford Science Enterprises. https://t.co/X7hCcrJEzH #5 - Helion Energy They're approaching fusion by not using the heat generated to produce electricity but by taking the magnetic energy to become electricity. One of the most well funded early companies. Notable backers: Peter Thiel and Sam Altman. https://t.co/nML4OzeHiC #6 - Zap Energy Zap Energy says they have the most compact and scalable design in the world. Notable backers: Shell, Chevron, and Valor Equity Partners (Early Tesla investor). https://t.co/N9v0JUmNB6 The bottom line: Startups and academics are in a race to bring nuclear fusion to the world. It's unknown who will win at this time but smart money helps narrow down the playing field. Can the vision of clean and near unlimited energy become a reality? If fusion holds up to its promises it will transform the world radically and be a massive opportunity for investors. Most firms say that they'll have a commercially available product by 2025 or 2030. Go deeper: https://t.co/YQjqOChLvU If you found this thread useful please give the first tweet linked below a RT. And follow me @BowTiedRobin Nuclear energy will usher in one of the biggest transformations of our time. https://t.co/xXiM3yBhGi

Can we acknowledge how ridiculous it is that startups and even individuals can bankrupt themselves... ... with an AWS bill? I cannot fathom why Amazon does not do anything meaningful to allow setting e.g. limits on accounts. Why do we need to hear stories like this on repeat? https://t.co/B7EMYPXA0j

My biggest take away from the last few months: Social media is not unique among tech companies in the aggregator advantage they have. Tech naturally has an advantage the more ubiquitous they are (more data, mind share, ad interest) True for social media, SaaS, banks, and more https://t.co/z2WB75WNKi This is why so much of the revenue in almost any category accrues to the biggest platforms. “Strength begets strength.” @Gilbert https://t.co/KgtZP20N0Z

I tried to make a one-pager to serve as a quick intro to the semiconductor industry but, unfortunately, I needed three pages 😅 This first page goes over the types of chips and how a chip is made. Hope you enjoy it and feel free to share if you do! This second page continues with how a chip is made, goes over Moore's law and gives a brief introduction to the industry's history Lastly, this last page goes over the semiconductor industry (as it stands today), the geopolitical tensions and the tailwinds for the industry. I cover the semiconductor industry in Best Anchor Stocks (link in my profile) because one of the current picks belongs to this industry Feel free to try it out (2-week free trial)

I’m very biased to agree with the premise of this book (it’s very consistent with my “growth without goals” philosophy)…but, it’s really unique. Written by two AI/computer science experts. “Almost no prerequisite to any major invention was invented with that invention in mind” This related video by the author touches on the major points and is phenomenal https://t.co/JZYo9eHwVE

I’m worried about America’s energy security. In just a few weeks, Putin’s war has disproven key tenets of US oil policy, and it’s rapidly downgraded America’s role in the clean-energy transition. I want to spin out a few of those thoughts here. https://t.co/DulHahtwrl 1/ For decades, the US has followed a few plays whenever oil prices spike: 1. Ask Saudi Arabia to drill more. 2. Invest in “US energy independence”—that is, drill more oil & gas domestically. 3. Fund alternative-energy R&D to cut long-term oil demand. https://t.co/DulHahtwrl 2/ We can debate whether every president was sincere about every part of the playbook. But the idea—the economy needs oil in the short term, but we must cut oil demand in the long term—was bipartisan. Here’s George W. Bush making the key point in 2008. https://t.co/DulHahtwrl 3/ Over the past few weeks and months, every tenet of that plan has failed or been abandoned. Start with Step #1: “Ask Saudi Arabia to drill more.” 4/ It’s not working. Saudi Arabia has resisted *months* of Biden admin requests to increase production. Yes, Aramco finally said today that it will drill more. But its statement is full of asterisks and it’s too little, too late given rising demand. https://t.co/4JErwjjRdZ 5/ Step #2: Embrace US “energy independence.” This one… already happened. In 2019, for the first time in 62 years, US energy production exceeded US energy consumption. America is now the world’s largest producer of oil and natural gas. https://t.co/9TMe3gs40g 6/ But far from winning us independence, America’s massive oil & gas production has made us *dependent* on market whim. That’s because oil prices are set on the global market. So when Russia invaded Ukraine, oil and gasoline prices soared everywhere. https://t.co/JVRvK7pqoH 7/ If we have all those oil reserves, you might ask, why doesn’t the US drill more, then? Because nobody has that power. Biden can’t compel companies to drill. And oil firms have sworn to investors that NOTHING—even $150 oil—will make them drill more. https://t.co/DulHahtwrl 8/ What’s darkly funny is that the triumph of US “energy independence” is also part of why Saudi Arabia won’t listen to us. In the 2000s, the US was Saudi’s largest customer. Now, as @MattZeitlin has reported, we’re also their largest competitor. https://t.co/zX33ulzJEp 9/ That brings us to the last step: “Fund alternative-energy R&D.” If the US led research into wind, solar, nuclear, EVs, and more, then one day they might become cheap enough that we could—to quote noted environmentalist George W. Bush—“end our addiction to oil.” 10/ That has happened. Clean energy became cheap. Over the past decade, wind, solar, batteries & EVs have come down the cost curve. They are now ready to be deployed across the country. https://t.co/aK1qdhPrYn 11/ And a few months ago, Congress was writing a bill to do that. It seemed like the 50-year dream of US energy policy was finally coming true. Then the talks fell apart. Now Dem leaders despair at passing even a small energy bill. https://t.co/ofDtMIGZOz 12/ At the same time, Putin’s war has further weakened America’s hand. As @jholz__ has reported, Russia is a major supplier of key metals needed for clean-energy supply. Geopolitical tumult + the risk of secondary sanctions have sent prices soaring. https://t.co/1ywCpsSfEd 13/ These two factors have rapidly shifted America’s role in any clean-energy transition. For now, Chinese firms will retain their scale & policy advantage over US firms—and they may have also just gained access to a source of cheap mineral inputs. https://t.co/DulHahtwrl 14/ So to summarize: • The US can’t trust Saudi Arabia to help in an oil shock • The US can’t benefit from its oil industry in an oil shock • The US isn’t deploying tech that would eliminate oil shocks • China has further entrenched its advantage in clean energy 15/ All that would be bad enough for the US. But what worries me is that no major interest group in DC can really right the ship: The oil industry, the White House, and the climate movement are all stuck. Only Congress can help us now. https://t.co/DulHahtwrl 16/ But could Congress really act? That would require @Sen_JoeManchin to step up (and for @SenSchumer to work with him). But I’m not sure anyone, even those more sympathetic to Manchin’s views, know what he believes or even wants to pass anymore. https://t.co/DulHahtwrl 17/ So that’s why I’m so worried. The question now is whether the US realizes that its energy policy *has already failed*—or whether we just muddle through, secure in our old fantasies, refusing to plan for a far worse crisis than this one. https://t.co/DulHahtwrl

In 1994, Jeff Bezos famously spotted a stat that made him leave his high-paying PE job to start Amazon: 💡 The Internet was growing 2300% per year. What are the generation-defining stats of today? I'll post a few to kick things off... 💡 The cost of mapping a genome has fallen ~100,000% over the last 15 years. If that doesn't speak to you, we're talking about $100m down to <$1k – faster than Moore's law.

Wonder why the ☁️ keeps cranking and will continue to grow? From @GoldmanSachs IT Spending survey in January 22 That's a lot of workloads to migrate and does not even include the net new applications 📈

I've long been interested in new ways to organize science and enable curiosity-driven discovery. Today, in partnership with @Stanford, @UCBerkeley, and @UCSF, we're excited to announce Arc Institute, a new undertaking in this vein: https://t.co/NAOHTwFKuH. Arc is, fundamentally, a nonprofit that will conduct basic research in a somewhat new way. It’s based on three core ideas. First, betting not on projects, but on people. Arc will fund biomedical investigators to pursue whatever research programs they deem most worthy of study. This might sound basic, but people tend to be shocked and horrified upon learning how many bureaucratic hurdles the nation's top researchers must contend with. A labyrinth of funding applications typically stands between a good idea and an important result. On top of that, consensus-oriented approval processes mean that many scientists don’t even have the freedom to focus on their best ideas. So, first and foremost, Arc is about returning science to the scientists. Second, investing in scientific tools and infrastructure. As research programs have become more sophisticated, finding good ways to support the development and advancement of foundational technology has become more important. And, third, making it easier for scientists to get good ideas from the lab to the clinic. Arc will start out by focusing on complex (i.e., polygenic) genetic diseases, including cancer, neurodegenerative disease, and immune-related disorders. Arc is influenced by Fast Grants and what we learned there. In particular, Fast Grants increased our conviction that there are important and under-explored points in the space of possible institutional configurations. You can read more about the whole thing in the blog post: https://t.co/D16T6HVWdP. And, if you’re interested, you can follow along at @ArcInstitute. While I've helped get it off the ground, Arc will be led by @SKonermann and @PDHsu, two phenomenal scientists. (I'm remaining fully focused on the financial services "lab" that is Stripe.) Most of all, we're enormously grateful to the donors (listed on https://t.co/PSeiirRW4j) who are supporting Arc, including @VitalikButerin, @collision, Crankstart, @RonConway, @danielgross, @eladgil, @htaneja, and @moskov. 🧵s from @arcinstitute's cofounders: https://t.co/L4pPoazVuo, https://t.co/TwcXSOZ686

Steve Jobs killed BlackBerry. By creating a cult and inventing new rules Here's the breakdown and why it matters👇 In January 2007, Mike Lazaridis and Jim Balsille are probably sippin a coffee sitting in their frosted glass office when they see IT. IT is the moment Steve Jobs reveals the iPhone to the world. Mike and Jim don't know yet... But the BlackBerry is doomed. Apple's "Jesus Phone" would soon take over the world. Mike and Jim are the co-CEOs of BlackBerry at the time. And they initially aren't worried about the iPhone. Here's why👇 There was "no threat to the core business" Because BlackBerry prided itself on 3 things: - Security - Efficiency - Functionality the iPhone had none of these. Instead, the iPhone was beautiful. Jobs didn't care that the battery drained in 8 hours. He didn't care that it was slow. Or that it crashed the wireless networks. And people may have hated it. Early on Jobs even shows a reporter the iPhone's touch-screen keyboard. Reporter: “It doesn’t work" Jobs stops. The reporter kept making typos and said the keys were too small for his thumbs. Jobs smiles and replies: “Your thumbs will learn” But instead a cult is born. And Apple sells 1 million phones during the summer of 2007. Here are 3 reasons why the iPhone takes off while the BlackBerry fails👇 1) The Infrastructure Problem When the iPhone launches, Apple teams up with AT&T. And cuts a special deal. At the time, BlackBerry couldn't stream videos or surf the internet because it was slow and expensive. And carriers like AT&T and Verizon wouldn't allow it. But Jobs does it anyway: "There was a point where AT&T by changing the rules, forced all other carriers to change too. Apple reset expectations. Conservation didn’t matter. Battery life didn’t matter. Cost didn’t matter. That’s their genius." - Mike Lazaridis, BlackBerry 2) Functional vs. Viral Beauty A BlackBerry was secure, efficient, and functional. But will you tell everyone you know about secure and functional? Nope. No one brags about boring. Instead, the iPhone was beautiful and magical. It looked like the aliens of 2100 came back from the future. An iPhone made you feel like you were part of a club no one else knew about. And when you join the cool club, you want to tell everyone you know. Virality is beauty's cousin. They are related but aren't quite the same. 3) A Crappy Counterpunch So BlackBerry is in panic mode. They team up with Verizon and launch an iPhone competitor called Storm. The original timeline to launch is 9 months. at 15 months, they finally ship it. And the product sucks. It is slow. It is glitchy. And Verizon demands $500 million to cover their losses. This marks the beginning of the end for BlackBerry. The Takeaway: Apple surprises BlackBerry with the iPhone by doing the opposite. A BlackBerry is functional → an iPhone is beautiful A BlackBerry is efficient → an iPhone is extra A BlackBerry saves battery → an iPhone wastes it So what can we learn? The opposite of a successful product isn't failure. It may just change the world. If you learned something new, retweet the 1st tweet to share with a friend! https://t.co/vN8KIIOieI Follow me @chrishlad for frameworks, systems and business breakdowns. Also join 5,000+ others that get threads like this via email 1x per week! Subscribe for free in 3 seconds here: https://t.co/Zr6gAK3oP0

There is a widespread view that the internet and software industry is now mature, that the historical pattern of disruptive revolutions every 10-15 years is now over. 🧵 Ben Thompson provides an excellent articulation of this view (as he often does) https://t.co/XT4UfVAJ5j In my experience, this view is tacitly held by most of the establishment: institutional investors, tech execs, policymakers, media, etc. It affects valuations, corporate behavior, media coverage, and policy making. I believe there are at least 3 major reasons why this view is wrong: plasticity of software and the internet, new vectors of computing improvement, and generational desire for new frontiers. I'll explain these below. 1. Plasticity of software and the internet. The core of most of these arguments are analogies to past industries. Ben cites the auto industry. Other writers cite past information networks like broadcast TV and radio. But it’s a mistake to analogize software to hardware. Software has a much richer design space — closer in the breadth of possibilities to creative activities like fiction writing than traditional engineering. https://t.co/Fp0Jc7rmY3 Moreover, the internet was specifically designed to embrace this plasticity. The internet is the ultimate software-based network, consisting of a relatively simple core layer connecting billions of fully programmable computers at the edge. Computers connected to the internet are, by and large, free to run whatever software their owners choose. Whatever can be dreamt up, with the right set of incentives, can quickly propagate across the internet. 2. New vectors of computing improvement Ben argued that one reason the software industry is mature is that it has reached its logical endpoint. The assumption is that the only vectors of improvement that matter are the ones the tech industry has traditionally focused on: greater data availability, better computing performance, smaller devices, and so on. People with this mindset look at a blockchain and see a slow database. (And while blockchains performance is improving rapidly, running a consensus mechanism will always have some performance overhead.) Blockchains offer to improve computers along different vectors. Specifically, they allow computers to make credible commitments that in turn unlock new classes of applications. https://t.co/7SB4843Wam Those applications can have a profound impact on how power and money is distributed across the internet. Thinking about computing improvements along economic and political vectors is very foreign to the traditional tech mindset. Framed this way, we are nowhere near the logical endpoint of the internet's evolution. 3. Generational desire for new frontiers A lot of what I do in my job is talk to people moving into web3, either as founders or as employees in companies I work with. Many are coming from big tech companies. The story is the same every time: they don’t want to spend their lives optimizing ad clicks; they want to work on the frontier and help shape a new industry. They want an internet of their own, that reflects their values and culture and lets them feel like genuine participants as users and builders. “If you want to build a ship, don’t drum up the men and women to gather wood, divide the work, and give orders. Instead, teach them to yearn for the vast and endless sea.” There are a lot of smart, industrious people yearning for the sea. Betting that the internet is mature is betting against them.

1/@solugen: The first carbon negative molecule factory Just two months ago, @solugen raised +$350M to scale their solution to building carbon negative chemicals Their breakthrough may be one of the most pivotal unlocks to fighting climate change Let me explain what they do: 2/First, let's level set on the problem: climate change It is accelerating. Some facts: 1. July 2021 was the warmest month EVER 2. The seven warmest years since 1880 have all occurred after 2014 3. There has never been more carbon in the air than there is now 3/ So let's pollute less. Recycle more. Use more electric vehicles. Turn off lights...right? Wrong. Changing consumer behavior is extremely difficult, and it will not achieve the scale we need 4/ You have to do what @elonmusk did with @Tesla. Make the newer technology so great that consumers adopt. It needs to be price competitive (if not cheaper), as well as higher quality. Only then will people adopt. https://t.co/EmHfbOarYN 5/ Chemical production is ripe for this disruption. It is one of our largest pollutants, accounting for ~14% & 8% of global oil and gas demand, respectively. And even at that scale, we are not very good at it. There are two main processes: 1. Petrochemistry 2. Fermentation 6/ First, let's talk about feedstock. A feedstock is basically the low-value building block that chemistry is performed upon. In the case of most chemical production, the most common feedstocks come from oil & gas. 7/From there, the feedstock goes through a variety of production steps (heating, cracking, etc.) More important than understanding the exact steps, it is critical to note how complex the process is Along the way many secondary and tertiary products are created 8/ If you look at a refinery, you will find that there are many critical steps to production... but there are also a lot of resources allocated simply to managing secondary and tertiary products The process is fundamentally flawed 9/ As a result, there are three large sources of emissions: 1. Feedstock 2. Separating secondary products 3. Transporting secondary products 10/ So naturally, we should just leverage fermentation, right? Well, ultimately, fermentation has significant scaling challenges. So what do we do? Enter @solugen and the Bioforge https://t.co/2swEx88Rkw 11/ Solugen combines the best components of fermentation and petrochemistry to create a new solution: The chemi-enzymatic process. Let's start with input #1. Instead of a dirty feedstock (e.g., Oil) , Solugen leverages a renewable corn-based feedstock 12/ To react with the feedstock, @solugen has to produce specific enzymes The enzyme varies based on the desired final product. For this, @solugen leverages cell-free manufacturing 13/ Traditional synthetic biology reengineers entire cells to have new abilities. Cells have many different enzymes though. We want a high-density of specific enzymes, so cell-free manufacturing is the way to go. Imagine a bag of multi-color M&Ms vs. red only M&Ms 14/ Using proprietary software, @solugen designs the host cells (biomass) to produce very specific enzymes They then use as the second input for the chemical production process Solugen has scaled this process and driven down the price of enzymes ~10,000x 15/ We then enter the Bioforge. For obvious reasons, we do not know the very specifics of the Bioforge, but we do know there are two main processes: 1. The enzyme reactor 2. The metal reactor https://t.co/k2NZEmbVdx 16/ In the enzyme reactor, both inputs are combined, and the reaction begins The 'secret sauce' is in the software Solugen uses to manage the inputs + air, facilitating the reaction The output then enters the metal reactor aka a 'trickle-bed reactor' 17/ In the 'trickle-bed reactor', the output is 'showered' over metal (e.g., Gold) to further react Again, the 'secret sauce' is in @solugen's ability to predict and manage this process at scale 18/ The steps seem basic, but the complexity of chemistry is astounding, and the underpinning software is state-of-the-art @Solugen solves for all three sources of emissions. No dirty feedstock. No secondary products. No transportation to treat 19/ The results are astounding. @Solugen is now the world's first carbon negative chemical plant The business is growing, and the Bioforge is scaling https://t.co/O4gTSJY0h3 20/ And the future vision is even brighter. @Solugen's solution leads to: 1. Zero emissions 2. Increased safety 3. Supply chain agility 4. Localized production (and supply chain rigidity) 21/ Cleaner. Safer. Faster. More efficient. And the process is vertically integrated. The chemicals do not change hands five times. No margin dilution So the end result is also more cost effective 22/ It is impossible to overstate the importance of @Solugen's innovation. Now, we get to sit back and watch @Solugen develop into one of the most important companies of the next few decades (h/t to founders @GaurabC and Sean Hunt for their time) /23 Investors @fiftyyears - @sethbannon @Carbon_Direct @lowercarbon - @claydumas / @sacca @BaillieGifford @refactor - @zalzally @Temasek GIC @foundersfund - @briansin 24/ One pager 25/ Investment score Full writeup can be found here: https://t.co/WQ6X4xOSgr Would love the thoughts of @friedberg, @sacca, @chamath, and @patrickc!

ML/AI is drowning in complexity. The status quo of K8s + bespoke models + pipeline jungles + experiment trackers + deployment platforms + monitoring platforms imposes tremendous costs. The ecosystem needs a deep rethink. Here's why. There are three types of ML/AI use cases. The first are those that are well defined and don't require bespoke training data. Examples include speech recognition and document translation. These can be solved today in minutes with standardized APIs at a 1000X+ simplification. The second is the long tail of use cases that humans are good at but are hard to standardize. Give a human some simple instructions and they can solve thousands of tasks. Zero-shot and few shot learning will solve these at a 100X+ simplification. It's early, but inevitable. The third are data intensive use cases. Examples include customer churn, inventory forecasts, predictive maintenance, personalization, fraud, etc. This will be solved over time by declarative, data-first approaches to AI at a 10X+ simplification. Pipelines beware! Finally, the traditional MLOps stack will persist in pockets as the escape hatch. But better tools will bring at most a 2X simplification here. Over time even these use cases will shift to higher level abstractions that reduce complexity and empower everybody to do more faster. The startups I'm watching that promise a 10X+ simplification include @OpenAI, @CohereAI, @SnorkelAI, and, of course my biased favorite, @continual_ai. But we need many more!

Nuclear energy is making a comeback ☢️ Poland is also getting in on the action (h/t @wwwojtekk) https://t.co/IGloDMxI9T

Industry-specific vertical software companies will eat some of the most iconic internet businesses of the last decade, including @stripe and @salesforce — and it all starts with a laundromat. The impending vertical software tsunami and what I call ‘The Laundromat Problem’ 🌊 2/ If you look at the world of software, the general goal is to build products that solve broad, industry-agnostic problems for a large number of companies. But there's a problem with software that's designed for wide, market-agnostic problems. Enter Dustin's Laundromat. 🧦 3/ Dustin runs a laundromat on a busy street corner. Today, to support his business, Dustin uses 10+ software products. He has a Point-Of-Sale system, an inventory management solution, and 3 kinds of advertising software. 😱 4/ None of these solutions cater specifically to laundromats. So, Dustin spends many hours configuring them to talk to one another. This creates a really annoying, challenging process for a non-technical group of folks who are literally there to fold laundry. 👔👚👗 5/ Configuring, maintaining, and integrating all those SaaS products is a lot of work—and nobody at Dustin’s Laundromat has the skills to do it optimally. In general, the software is being mis-used... if it’s used at all. ❌❌❌ 6/ You might think, “it’s just a laundromat,” but here’s the catch: the Laundromat Problem is NOT unique to Dustin’s business. Some spin on The Laundromat Problem is happening at /almost every company/ in every market. 🤯 7/ The average company today uses more than 20 different software solutions. 📱💻 ⌨️🖥️ That’s insane. This is happening because everyone has a cluster of point solutions, not an all-in-one tool that’s catered to its specific business/industry. But this isn’t sustainable. 8/ Think about how much time companies spend configuring their Business Intelligence Tools, their CRMs, their support desks. And that’s companies that can afford to spend time and resources on this. 💸💸 What about markets that don’t have IT support or engineers? 9/ How much do you enjoy opening a birthday gift that takes multiple days to assemble? Do you enjoy later finding out that you put it together incorrectly? That’s the world of horizontal SaaS 👿 Companies might not understand why they have this problem, but they feel the pain. 10/ Vertical SaaS solves The Laundromat Problem. @trycents and @Ajekowsky solves it for Laundromats. @CarbonHealth and @erenbali solve it for doctors' offices. @classpass @askmindbody and @PayalKadakia solve for gyms. @coursekeyedu solves it for trade schools. 🏆 11/ The pain killed by market-focused software is real—which is why these companies have legs long term. This is just the beginning of the Vertical SaaS Tsunami. 🌊

There is a huge amount of chatter about the insanity of the venture market. Lots of folks throwing around words like "froth" and "bubble". It is full crazy. It is also working. Amongst my companies we just crossed $500m in revenues. A thread on why its working... TLDR: We have moved from the early adopter stage to the mainstream in b2b software. Investors are recognizing this and shit is getting crazy. 1. The market is in full wild mode. I am seeing things I never would have dreamed of. At the seed stage rounds are getting done in days. Prices are 2-4x what they were a couple years ago. Marginal and some very weak companies are getting funded. Later stage multiple are sky high. 2. But... Things are also working. Among my portfolio, ~50 B2b software companies, we just crossed $500m in revenue in aggregate and the growth is not slowing at all. Here are the drivers I am seeing 3. The biggest, by far, is digital transformation. Every company in the world has finally realized they need to adopt tech or they are going to get their asses handed to them by digitally native startups. They probably still will. Their teams now have budget and fear. 4. Where most of my companies used to sell predominantly to other startups, there are suddenly now **customers with 10's of thousands of employees who are desperate to buy**. The expansion opportunities are incredible. 5. A lot of smart folks think this might be a one time event. These slow big companies adopt once and use forever. Given their past stasis, I think this is probably true. That means annuity like revenue streams and multiple expansion. 6. This has caused every major investor from the public markets on down to feel intense FOMO and they are spamming cash into the market to attempt to buy ownership before the land grab is over. 7. As @reidhoffman used to tell me, the the number one risk of early stage investing is follow on capital risk. That is gone now. Almost every seed stage company in my portfolio goes on to raise a lot more capital. You can imagine the behavior this incentivizes. 8. When startups are not afraid of getting the next round they mash the throttle. If they don't, a competitor does and they are force to reciprocate or lose. This capital firehose drives both growth and massive adoption of tools and services. The picks and shovels businesses 😍 9. Because CAC is front loaded against these longer expected LTVs this means big budgets and a grow or die mentality. Paying more to get the best tools is now standard. With competition at your throat there is no time to futz around building DIY shit like companies used to. 10. Shit is just working. Not many years ago we had to build bespoke software for almost everything. This massively slowed things down and created failure vectors throughout the stack. Now we can use tools from world class providers for everything but the crux. 11. We have also learned how to do our jobs. When I started in 2005 there were a tiny number of folks who knew how to do it- the intrepid few who had held on from the 2000's. Most of us were just making it up as we went. 17 years later people in every role have gotten damn good. 12. As an industry we have learned what works and what doesn't. This is hard to overestimate. We can go faster, take down bigger incumbents and assume risks that would have seemed foolhardy only a few years ago. 13. Shit is crazy. Mistakes are being made. But, things are working in a way that I have never seen before. It is hard not to feel really bullish right now.

Everything you have been told about stopping climate change is wrong. Buying a Tesla doesn’t matter. Not flying/eating vegan/turning off lights doesn’t matter. As a serial climate entrepreneur (raised over $200 million), I speak from experience. Only one thing matters. 👇🏻👇🏻 Let’s start at the beginning: what’s the problem? The problem is that it is free to pollute the atmosphere with CO2. Because it is free, lots of business models make sense. Examples: oil extraction, gas stations, global container shipping, international air travel, etc. Because lots of business models ONLY make sense when pollution is free, a very well-funded PR and lobbying machine sprang up to keep pollution free. At first, they tried to convince people that climate change was not manmade. They said it was a “natural climactic cycle”. Eventually this became untenable. So they settled on a more durable strategy: climate change is a matter of personal responsibility. If you think about it, this is absurd. Just because I didn’t personally spill oil in the Gulf of Mexico doesn’t mean it’s ok if BP does. Unfortunately, it worked. Green became cool, electric cars became cool. People had climate guilt and bought offsets. Urban liberals convinced themselves they were doing something about the biggest problem of our age. But they weren’t. Because pollution was still free. Let’s take your Tesla. You retire your gas guzzler and go electric: you reduce your personal emissions. But what happens systematically? That incremental gallon of gas is still sold. All you have done is slightly decrease demand. Congrats, you just made gas cheaper. There is only one solution to this problem. And as much as entrepreneurs hate to hear it, it is a political solution. There must be a high and predictably escalating price on carbon pollution globally. That’s it. Nothing less, nothing more. All fossil fuel business models rely on relatively long-term (20+ years) business plans. A predictably escalating price on carbon destroys these business models. It also creates a dramatic financial incentive to put carbon back in the ground. Some carbon emitting activities, like international flights and Kobe beef, can withstand a high price on carbon. There are low carbon, carbon neutral and carbon negative substitutes for nearly everything in our lives. What is missing is the economic incentive. @stripe has been a leader in paying to put carbon back in the ground. Corporate action is great, but not sufficient globally. My wish is that everyone who cares listens to @GretaThunberg and realizes there is only one thing that matters: put a price on pollution. Whatever money you would have spent on solar panels and electric cars, apply it where it matters: putting a price on pollution. Make it untenable for a politician (left or right) to run for office without solving this problem economically. On the left: we don’t need a “green new deal”. That doesn’t solve the problem. We need a clear economic incentive to not pollute. On the right: carbon pricing is a transparent incentive for business to do the right thing for the planet. Even Exxon supports it. Here are some folks you can follow if you care about pragmatic climate solutions: @claydumas @sacca @greentechmedia @GretaThunberg @ramez @PeterDiamandis @Tomprice @lowercarbon @chamath @JigarShahDC @elonmusk @andykarsner @BillGates To conclude the thread, I think many people underestimate how smart and evil the opponents of global action on climate change have been. They have effectively coopted the goodwill of smart people who care into irrelevant solutions. Let’s get smart on this and let’s solve it. Addenda 1 - I forgot to mention that the “personal responsibility” PR play was taken directly from the tobacco industry, which was extremely successful with it. https://t.co/W5Le4EN3L2 Addenda #2 - The full solution is two parts: 1. A high and predictably escalating price for carbon. 2. A commitment to pay to put an equivalent amount of carbon back in the ground as your nation has historically emitted. Lots of nuance, but that’s the North Star. Addenda #3 - It has been 15 years since An Inconvenient Truth came out, with sustained “personal responsibility” campaigns since. Impossible to argue with the failure of this empirically, as illustrated well here. Addenda #4 I will write a separate thread on why we should be talking much more about geo-engineering. We have already been unintentionally geo-engineering for 150 years. We now know how to be intentional about it to buy ourselves the 100 years we need to fix this.

Sure, Moore’s Law is great, but it is slow compared to the improvement rate in the cost of genome sequencing. In 2000, the first human genome sequence cost between $500M &amp; $1B. In 2006, the cost was $20M Today, the cost is around $600. https://t.co/O6o1cvgbhK https://t.co/UxyVa3XPgL

Never been better setup for NUCLEAR -Global energy shock + constraints -Higher prices -Nuclear related stocks outperforming -Commodities, Metals, Mining demand cheap energy -Commodity consuming humans demand for zero carbon -Next gen environmentalist awakening to it

Has university “commercialization” or “tech transfer” - A university commercializing IP that it owns from a grad student or professor - Ever resulted in a great success? Or do they only screw things up? My guess is it’s the latter. Or a great company? @ByrneHobart @whrobbins @jasoncrawford Gator-aid! https://t.co/8YFkltxQL4

If you want to know the next big thing in "real atoms" investment macro-trends, I'll tell you right now. (1/x) It is WATER. Specifically, solar-powered reverse-osmosis desalination. Here is why... Water is the source of ALL wealth. That fact is so basic that we have all but forgotten it. Water is so essential that even in ancient times, we built megaprojects to ensure that entire populations would have low-cost access to clean running water. In any modern nation, water is the closest thing to a UBI-like resource we have: you can find clean water on-demand at any tap, and it is almost free. Many public places actually do provide it for free, at least in modest amounts. Without water, you have no food, no sanitation, no life. Humans can survive for weeks without food. We die within a few days without water. Until very recently, all water came from the mountains. We built a huge amount of infrastructure flowing it down snowpack, streams, rivers, dams, aqueducts - ironically, nearly almost all the way to the sea, where most of the human population lives and consumes freshwater. Even so, the world faces a freshwater scarcity problem. There is a limit to how much water is produced by annual rainfall, and as we divert more and more of the flow to our coastal cities and agriculture, inland areas are drying out and turning into deserts. Most hydrological surveys report that underground aquifer levels have been gradually dropping over the past few decades, because we just keep using more and more of it. However, 3 years ago, a key techno-economic threshold was crossed, and it changes EVERYTHING. That change was: the per-kwh cost of solar energy dropped below the per-kwh marginal cost of fossil fuels. This happened in 2018, and was reported first in a Lazard report published 2019. https://t.co/L4qNoI6u3j What does solar power have to do with water? Because the ONLY other source of freshwater that doesn't come from the mountains is desalinated seawater. Human civilization's relationship to water contains one great irony: most people live within 100 miles of a coastline, on a planet 75% covered with water. Yet that water is salty, and we can neither drink it nor irrigate our crops with it. JFK once said, “If we could produce fresh water from salt water at a low cost, that would indeed be a great service to humanity, and would dwarf any other scientific accomplishment.” That day is now at hand. Desalination has been around for decades, but it is energy-intensive, and until 2018, freshwater produced via desalination was only economical if powered by cheap fossil fuels, which is why it first gained widespread use in Israel and other Middle Eastern countries. Desalination would not be feasible on a global scale because using such huge amounts of fossil fuels would be an environmental disaster. But in 2018, the cost of solar fell below the marginal cost of fossil fuels, i.e. it's now cheaper to build NEW solar plants than to even continue operating existing coal plants. Given that solar prices continue to drop by 50% every 4-5 years, this means it is now possible to cost-effectively produce freshwater at the shore, cheaply, and with very low emissions. The importance of this cannot be understated. Not only that, but solar desalination is not subject to the solar intermittency issue. Why is this important? Most residential/commercial solar installations require power at nighttime (when the sun isn't shining), which necessitates storing the power in expensive batteries. That's the main reason large-scale transition of our grid to solar remains difficult and slow. But desalination doesn't need to run at night: you just run it when the sun is out, and store the freshwater in big cheap tanks. Solar panels are cheap (and getting cheaper), and you don't need to pay for expensive batteries. This special feature of desalination applications can leapfrog the residential/commercial solar transition by several years. It means that we can produce cheap water NEAR where it'll be consumed, and save on the billions we spend each year transporting trillions of cubic meters of water down from the mountains, over thousands and thousands of miles. All of that mountain freshwater can be left to recharge our inland aquifers and restore the desertified ecosystems that are dying. Huge swaths of our planet can be passively restored by simply transitioning to near-coastal solar-powered desalination for our water needs. Years ago in 2010, Michael Burry (The Big Short) correctly identified that the next big thing was water. It would soon become critical; scarcity makes it "political, and litigious. Transporting water is impractical for both political and physical reasons." Now, the majority of human populations no longer need to: they can produce water close to where they live, leaving the bounty of natural mountain freshwater for smaller inland populations and the natural environment it used to feed for millennia before mankind. The freshwater scarcity problem is OVER. In the next 10-15 years, the world will realize this, and given the prospect of low-cost, low-emissions, secure, locally-produced and -controlled freshwater supplies, we will see an enormous economic boom in this sector. Mankind will take an enormous step forward in securing this most precious and basic of resources, both for ourselves AND for our natural environment. What is more, solar-desalination enables us to produce enough freshwater to irrigate billions of acres of degraded and desertified land, enough to restore them to thriving forests and create a carbon sink of sufficient scale to offset all or most of human CO2 emissions. That's what I'm pursuing with @TF_Global - a scalable solution to climate change using existing technology. But beyond that, the broader opportunities afforded by low-cost solar desalination will represent the biggest infrastructural upgrade to human civilization we have seen in a lifetime. https://t.co/mk0vwxk7ph And by the way, just to prove that this isn't just a bunch of analyst talk, we went and BUILT a 100% solar-powered desalination facility. Among fully off-grid examples go, it happens to be the world's largest (for now). https://t.co/GCVEal3cR5 So this stuff WORKS.

In college, a professor gave an assignment where we had to carry all of the trash we accumulated with us wherever we went for *an entire week* Since then, I've been fascinated with how humans generate and interact with waste. So, let's talk trash. A brief history: Humans have dealt with trash for thousands of years. The first documented landfill was in 3000 B.C., when the city of Knossos, Crete, dug large holes to dump garbage. Some 1k years later, China developed composting and recycling methods (including bronze recycling). 1.5k years after that, Athens made a law that garbage must be disposed of at least one mile outside of the city. In 1350, the Black Plague spurred the first usage of garbage workers in Great Britain --  called “rakers,” who would rake up trash every week. ~ 1757, Ben Franklin started the first street cleaning service and encouraged the public to dig pits to dispose of their waste Dumping waste into the ocean was common up until 1934, when the Supreme Court banned the practice. Post WWII, consumerism reaches an all-time high -- the amount of packaging produced and thrown away increases ~70% The US Congress passed the Solid Waste Disposal Act in 1965, which set minimum safety requirements for landfills. US landfills throughout the years by size & status: https://t.co/qizsD9TMIV 1976 was when the US created the Resource Conservation and Recovery Act, setting the standard for recycling and waste management for years to come. Today, waste management is a $2T global industry growing 5.5% YoY. In the US, recycling facilities generate $6.5B per year. Electronic goods recycling is a $16B industry growing 8.7% YoY. OK that's enough trash talk for today, but stay tuned for business models and 🔥 opportunities in waste management, including zero waste, the rise of "re-commerce" and the circular economy, e-waste, and recycling processes.

4 incumbents that I believe are ripe to be unbundled en masse. Each unbundling will be due to vertically specific user feature requirements. Each will result in 5+ $5Bn companies being created. Zoom Linkedin WordPress Salesforce Get ready for the great unbundling...

Online education will create winner-take-all classes. The best teachers for popular subjects will spend millions on high-quality production. As they dominate the market, they’ll invest more and attract even more students. Like the rest of the Internet, the big will get bigger.

The state of online learning: 1) YouTube will eat the low-end of the market. 2) People will pay 5-15x more for live courses. 3) The three biggest competitive advantages are the personality of a creator, the warmth of the community, and the quality of the student experience.

I fed all the tweets from @loganbartlett and @chetanp to OpenAI's model and asked for a summary of a section. Input on the left, output on the right. Pretty nuts:

eCommerce is in its early childhood. Consider our own consumer expectations when assessing eCom as a % of retail. We expect groceries to be at our doorsteps within two hours. Yet we expect Lululemon, Apple, or Nike to arrive within 2-5 days. As eCom % 📈: days become hours. <!-- Fetched 2026-09-15 via X's keyless syndication endpoint (cdn.syndication.twimg.com). --> **Roam filing:** [[E-Commerce Penetration|e-commerce penetration]]

Here's a summary of this great a16z deep dive on the podcast ecosystem: https://t.co/sqBbnaLuyr - How podcast industry has evolved &amp; where it's going - What's holding the industry back - Whitespace to build startups Thread 👇 **Links in this post** (t.co shortlinks resolved 2026-09-28): - `https://t.co/sqBbnaLuyr` → https://a16z.com/investing-in-the-podcast-ecosystem-in-2019/

Peter Thiel argues that science was "decentralized" and "healthy" before the New Deal; then after the atomic bomb, it became too centralized and bureaucratized to innovate. Before, we had "science as discovery," now we have "science as governance."