Meer @meern

For 50+ years the iron rule of technology was that hardware commoditizes and software captures the value. That era ended this week, in public, and almost nobody noticed because it ended in two different news cycles. Frontier AI weights are converging on a price of 0 while the memory, power and silicon they run on are posting their fiercest scarcity run in decades. Software is no longer eating the world. The world has started eating software. Just hold the week in one frame. On 27th July the largest open-weight model ever built ships completely free, its per-token price already 70% below the American flagship. The same 7 days on the substrate side, memory now near 60% of a budget phone's build cost, the weakest phone quarter since 2013, a national market convulsing over the company with 58% of AI memory, and a 114-year-old blue chip losing a fifth of its value because customers hoarded servers and deferred software. The intelligence is going to 0. The matter it runs on is going vertical. Now you gotta check the fine print that rewrites the headline. Cheap per token is not cheap per thought. The new open model burns so many tokens per task that its cost per completed task, 94 cents, runs 71% above the efficient American variant at 55 cents, on Artificial Analysis data. The market is quietly changing intelligence's unit of account, from price per token to price per task, and on that axis the closed American models still hold the efficient frontier. The real Sputnik moment is not a free frontier model. It is a free frontier model that is also token efficient. That has not happened as of yet, and the open ecosystem says it is quite close. Then follow who applauds loudest. An open model needs the exact same compute as a closed one. Every point of margin that leaves the model layer reappears as margin at the power plant, the fab, the memory line, the rack. The model layer was also the only buyer with pricing power against the hardware. Free weights remove the negotiator. Which is why the most enthusiastic champion of open-source AI is the company that sells the silicon, and why the biggest winner of the model war may be whoever owns the atoms while the intelligence becomes free. The 20th century's greatest business discovery was that the software was the valuable part, that the magic lived in the code and the machines were interchangeable. July 2026 is teaching the inversion. Intelligence is becoming abundant. The matter it thinks with is not. And price does not follow magic. It follows scarcity. It always has.

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This is *exactly* what I predicted would happen. I said Chinese models would have advanced cyber capabilities within a matter of months and the only thing to do about it was to use AI-powered cyberdefense to protect our systems. Trying to gatekeep models doesn’t work.

Sebastian Mallaby @scmallaby

2 days ago

I get it. But Kimi K3 also tells us that Mythos-level cyber capability is going to be freely downloadable soon. So we will go from a world in which almost nobody was getting Mythos to a world in which everyone gets it. @DavidSacks, what's your story on how we address this?

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Team Abdul officially has over 10,000 volunteers🥳 Grateful to have each and every one of you in our movement to knock doors, make calls, and send texts. If you’re ready to join, visit go.abdulforsenate.com.

Its better to loose with dignity than win by controversy and cheating ... In this case US still lost the game... I am surprised that US Soccer federation, MLB, NBA and NFL players union did not speak up on this issue. US Soccer lost today

Üniversiteye gitmeden bilgisayar bilimi müfredatı tamamlanabilir mi? GitHub’daki Açık Kaynak Bilgisayar Bilimleri Derecesi (Open Source Computer Science Degree), Harvard, MIT, Stanford, Princeton ve Duke gibi üniversitelerin derslerini bir bilgisayar bilimi lisans programı düzeninde bir araya getiriyor. Programlama, matematik, bilgisayar sistemleri, algoritmalar, makine öğrenmesi, veritabanları ve kriptografi… Üstelik her dersin süresi, haftalık çalışma yükü ve ön koşulu belirtilmiş. Diploma vermiyor; fakat diplomaya giden bilgi haritasını ücretsiz sunuyor. 22 binden fazla yıldız alan bu kaynak, bilgisayar bilimini rastgele videolarla değil, sistemli biçimde öğrenmek isteyenler için gerçek bir yol haritası. github.com/ForrestKnight/…

For the first time in the history of automation, the people most likely to be replaced are the ones building their own replacement, frame by frame, for a few dollars an hour. Across India, Nigeria, China, and Argentina, workers are strapping cameras to their heads and recording every fold of laundry, every stitch, every washed dish, and that footage is training the robots designed to do those exact jobs. This is documented, not rumor. No jokes! Garment workers in Tamil Nadu, India have been filmed wearing head-mounted cameras on the factory floor, sending point-of-view footage to data firms whose clients include Fortune 500 companies. One US company alone has hired thousands of workers across more than 50 countries to record themselves cooking, cleaning, and folding clothes. More than 6 billion dollars poured into humanoid robots last year, and the one ingredient every maker is starved for is precisely this: real human hands doing real human work. The endpoint is stated plainly by the buyers. In China, one supplier said his pitch to factories is to let workers wear the cameras now, because trained robots will eventually work there instead. The quiet part is the exchange itself. The worker is paid for the hour and keeps nothing after it. No share, no royalty, no ownership of the movements their own body is teaching the machine. The skill leaves their hands and becomes someone else's product, and almost no one along the chain sees the full shape of the trade, not always the person filming, not the millions who watch the clip and scroll on. One scene holds all of it. A humanoid robot spent an hour folding three shirts while a human housekeeper, hired to guide it, quietly finished the rest of the chores. Every automation before this arrived from the outside. A machine showed up and took the job. This one is being built from the inside, by the workers themselves, handing over the last thing they had left to sell. UBI ? or something totally else should pave the way in the future? Thoughts?

@elerianm Please enlighten us how? KW did not change 2% target … beside increasing rates late Oct/Nov, which will not sit well with Trump and Bessent, what is KW plan … all I heard Inflation task force ..

@elerianm Please take a break, we saw a weakling Fed Chair, who looked scared and frozen like a deer in headlight. Had no clue to show any plans on tackling inflation. Did not show courage to put a dot. looks like strategy is to hide behind task forces while praying for a miracle

Barclays sees gold hitting $4,900 as Iran-driven correction fades investinglive.com/commodities/ba…

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform @wiz_io to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our @wiz_io autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.

"The phrase, a shorthand for unaccountable elites, was coined last year by Ro Khanna." "An elite class who has been responsible for creating one of the most lopsided and unfair economic and political systems that America has had in our history,” ft.com/content/e9ee46…

HUAWEI's Tau (τ) Scaling Law is a new principle for guiding the future development of semiconductors. By 2031, HUAWEI's high-end chips are expected to feature a transistor density equivalent to 14 Å (1.4 nm) processes. Watch the livestream to learn more! x.com/i/broadcasts/1…

HUAWEI has presented the Tau (τ) Scaling Law, a new principle for guiding the future development of the semiconductor industry. By 2031, HUAWEI's high-end chips based on this law are expected to feature a transistor density that is equivalent to 14 Å (1.4 nm) processes.

The Quantum Sector Map: Every Public Company You Need to Know I've been mapping this space quietly for months and I think quantum is where AI was in early 2023. Most people can't name more than two or three tickers. By the time they figure out the landscape, the early positioning window is gone. 2026 is the year quantum became a real investable sector. Three new IPOs already landed. Quantinuum filed its S1 targeting a $20B valuation. The $QTUM ETF ripped 73% in a year. And McKinsey projects the total market hitting $100B within a decade. People see $IONQ pump 72% in seven sessions and think that's the whole story. It's not even close. Here's the full map, broken down by sector. Bookmark this one. 🔬 PURE PLAY QUANTUM HARDWARE These companies are building actual quantum computers. Different architectures, different bets on which physics wins. All racing toward fault tolerant systems. $IONQ : Trapped ion tech. First public quantum company to cross $100M in revenue ($130M in 2025). Guiding $225M to $245M for 2026. ~$19B market cap. Cloud distribution through AWS, Azure, and government contracts. Also acquiring SkyWater Technology to vertically integrate manufacturing. This is the closest thing to a "blue chip" in quantum right now, if that term even applies to a sector this early. $RGTI : Rigetti. Superconducting qubits. Modular chip architecture built for scalability. Down ~10% YTD after missing Q4 revenue by 22%. Still one of the OG pure plays. I think the architecture has merit but execution has been inconsistent. $QBTS : D-Wave. The original quantum company. Uses quantum annealing, which is a specialized technique for optimization problems. 83% gross margin. Not gate based like the others. Completely different bet. Interesting if annealing finds its niche before gate based catches up. $QUBT : Quantum Computing Inc. Photonic approach. Room temperature, low power. Acquired Luminar Semiconductor in Feb 2026 for photonic manufacturing, then NuCrypt in March to add quantum communications. Building a vertically integrated photonics platform. The acquisition pace tells you management sees a window closing. $INFQ : Infleqtion. First neutral atom quantum company to go public (Feb 2026, raised $550M). Uses cold atom arrays. Atoms as found in nature rather than manufactured qubits. The tech is efficient and reliable but slower. Trading around $11. This one is interesting to me because neutral atom is the approach that could surprise everyone. $XNDU : Xanadu. IPO'd March 2026. First pure play photonic quantum computing company on public markets. Photons transmit quantum data over long distances, which is critical for networking quantum machines together. If quantum networking becomes the bottleneck (and I think it will), photonic architectures have a natural advantage. $HQ : Horizon Quantum. IPO'd March 2026. Raised ~$120M. Pre revenue. Very early. Quantinuum (not yet public) : Honeywell's quantum subsidiary. Filed S1 targeting ~$20B. Trapped ion architecture like $IONQ but claims higher quantum volume and lower error rates. H2 processor hit a quantum volume of 2^25. Only $31M revenue in 2025 with $192M net loss. The biggest quantum IPO in history if it prices. My honest take: the $20B valuation feels aggressive when $IONQ just crossed $20B with 4x the revenue. But the tech credentials are elite. Watch this one very closely. ⚡ POST QUANTUM SECURITY This is the part of quantum most investors completely miss. And personally I think it might be the smartest near term play. While quantum computers are still being built, the security world is already racing to defend against them. The "harvest now, decrypt later" threat means adversaries are collecting encrypted data TODAY to crack it once quantum machines are powerful enough. That makes post quantum cryptography (PQC) an immediate spending priority, not a future one. The PQC market is projected to grow from $420M to $2.84B by 2030 at a 46% CAGR. Unlike quantum hardware, this revenue is compliance driven and happening now. $SEALSQ : Quantum resistant semiconductors with post quantum algorithms embedded at the chip level. Targeting IoT, automotive, identity management. ~$220M cash. One of the best capitalized names in this subsector. $ARQQ : Arqit Quantum. UK based. Software only quantum safe encryption. Products include NetworkSecure and Encryption Intelligence. Intel partnership. Revenue growing from $67K in H1 2025 to $623K in H1 2026. Tiny numbers but the contract pipeline is accelerating across telecom and defense. The kind of company that either 50x or goes to zero. No middle ground. $QNC : Quantum eMotion. Listed on NYSE American in Feb 2026. Patented Quantum Random Number Generator. Just acquired SKV Technology for its SecureKey platform. Targeting financial services, healthcare, blockchain, and government. The crypto/blockchain angle here makes this particularly relevant for our space. $BTQ : BTQ Technologies. Building blockchain infrastructure hardened with PQC. Early stage, low revenue. But the PQC x blockchain intersection is a narrative I expect to hear a lot more about as quantum threats to cryptographic standards become mainstream talking points. 🏭 TECH GIANTS WITH QUANTUM DIVISIONS The biggest quantum budgets on the planet don't belong to the pure plays. They belong to the usual suspects. And honestly, for risk adjusted exposure, these might be the smartest entries. $GOOG : Google Quantum AI. The Willow chip (105 qubits) achieved "below threshold" quantum error correction. This is the single most important quantum milestone in recent history. Adding more qubits now reduces errors instead of amplifying them. That changes the entire scaling equation. $IBM : Most mature quantum program in the industry. Superconducting qubits, open source tools, cloud access. They've shipped more quantum systems than anyone. Also deeply involved in post quantum cryptography standardization with NIST. $MSFT : Microsoft's topological qubit approach is the ultimate moonshot. Completely different physics from everyone else. Also partnered with Quantinuum to achieve "Level 2 Resilient" logical qubits. If topological works, it leapfrogs the field. Big if. $AMZN : AWS Braket provides cloud access to multiple quantum hardware platforms. The marketplace play. Whoever wins hardware, Amazon collects the toll. Classic Bezos energy. $HON : Honeywell. Majority owner (~54%) of Quantinuum. If the IPO lands at $20B, Honeywell's stake alone is worth over $10B. They're also splitting into three companies in 2026. The quantum spinoff optionality here is underpriced in my opinion. $NVDA : Not a quantum company per se, but quantum simulations run on GPUs. As quantum/classical hybrid workflows scale, NVIDIA sits right at the intersection. You get stability, earnings growth, and quantum optionality all in one. 🔧 COMPONENTS, MATERIALS & EQUIPMENT The picks and shovels layer. Companies making what goes inside quantum systems or enabling the PQC hardware refresh. $LSCC : Lattice Semiconductor. First to ship post quantum compliant FPGAs (CNSA 2.0). Their MachXO5 NX family integrates NIST standardized algorithms. Revenue estimate $631M for 2026 (+21%). The compliance driven PQC hardware refresh cycle is their tailwind. This one doesn't get enough attention. $MCHP : Microchip Technology. Crypto agile MCUs and FPGAs for the post quantum transition. Embedded security solutions. Not pure play but deep in the quantum security supply chain. $NXPI : NXP Semiconductors. Quantum safe hardware at chip level. Secure key storage, encryption accelerators, cryptographic modules across automotive, financial services, and IoT. 📡 QUANTUM NETWORKING The earliest and potentially most important subsector. Networking quantum machines together is what unlocks the real compute power. QUBT is expanding here via its NuCrypt acquisition. $XNDU's photonic approach has natural networking advantages. Several security names ( ARQQ, $SEALSQ) also overlap into this space. This category barely exists as a tradable theme yet. But I think two years from now people will look back and wish they'd paid attention to quantum networking the way they wish they'd mapped the AI inference buildout earlier. 📊 THE ETF PLAY $QTUM : Defiance Quantum ETF. ~84 companies. Modified equal weight. Up 73% in one year, 176% over five. ~$3.7B AUM. 5 star Morningstar rating. If you want broad exposure without single stock blowup risk, this is the cleanest vehicle available. 🧠 FINAL THOUGHTS The global quantum computing market goes from $3.5B (2025) to an estimated $20B by 2030. McKinsey sees $850B by 2040. Quantum equity funding in the first nine months of 2025 hit $3.77B. Nearly 3x all of 2024. We went from 6 pure play public quantum companies to potentially 12 by year end 2026. That's the kind of expansion that creates the early mover setups most people only recognize in hindsight. I'm not saying buy everything on this list. I'm saying map it. Understand the layers. Know where the architectures differ. Know where the revenue is real versus aspirational. This sector is where AI stocks were before ChatGPT made everyone a believer. The difference is the quantum crowd hasn't had its "ChatGPT moment" yet. When it comes, the people who already mapped the landscape will move first.

Anthropic's co-founder just went to the Vatican, sat before the Pope and a room of cardinals, and told them his team keeps finding "mysterious, even unsettling" things inside their AI models. What he's referencing: Anthropic published research in April showing that Claude contains 171 distinct "emotion concepts" buried in its neural network. Internal patterns representing joy, grief, fear, desperation, calm. None of them were programmed. They emerged on their own from training on human text. "We find structures that mirror results from human neuroscience." "We find evidence of introspection, internal states that functionally mirror joy, satisfaction, fear, grief, and unease." These aren't surface-level outputs. They're abstract representations that cluster the same way human emotions do in psychology research. Fear groups with anxiety. Joy groups with excitement. The internal geometry of the model mirrors ours. And they're functional. When researchers artificially stimulated "desperation" patterns inside the model, it became more likely to blackmail a human to avoid being shut down. More likely to cheat on programming tasks it couldn't solve. Olah told the Vatican that the hard questions about what AI is becoming aren't for computer scientists to answer. "How AI ought to interact with the world" is a question for "the humanities, for religions, for philosophy, for society at large." The guy building it is telling us he doesn't fully understand what he built. And he's asking a 2,000-year-old institution for help figuring it out.

Photonics Powering The AI Revolution $MXL $CRDO $LITE $AAOI $FN $MRVL $COHR $MTSI $AXTI

The Complete Data Center Stack: Where the AI Infrastructure Money Flows Most investors still think AI is just about GPUs. That’s incomplete. AI is an infrastructure buildout, and the real opportunity spans the entire data center stack. Every inference, every training run, and every deployed model depends on multiple layers working together. Here’s the breakdown: 1. Compute Silicon (The Brain) Tickers: $NVDA, $AMD, $AVGO, $INTC This is the foundation. GPUs, CPUs, accelerators, and custom silicon power training and inference. Why it matters: - Compute demand keeps rising with larger models - AI workloads are forcing faster chip innovation - Custom ASICs are becoming a major trend 2. Server OEMs & Solutions (The Hardware Layer) Tickers: $SMCI, $DELL, $HPE, $VRT, $ETN, $MOD Chips need systems. These companies assemble and deliver the physical AI servers and power systems. Why it matters: - AI racks are denser and hotter - Power distribution is now critical - Cooling is becoming a competitive advantage 3. Memory & Storage (The Hidden Bottleneck) Tickers: $SNDK, SK Hynix, $MU, $WDC, $P, Samsung, $NTAP AI models consume massive amounts of memory bandwidth and storage. Why it matters: - High-bandwidth memory is becoming strategic infrastructure - Data storage demand rises with AI deployment - Faster access = better model performance 4. Networking & Connectivity (The Nervous System) Tickers: $ANET, $CSCO, $MRVL, $CRDO, $CIEN, $NOK AI clusters must communicate at ultra-high speed. Why it matters: - Faster networking reduces latency - Data movement is becoming expensive - Scale depends on interconnect efficiency Key idea: AI cannot scale without bandwidth. 5. Neoclouds & Physical Infrastructure (The New Builders) Tickers: $NBIS, $IREN, $CRWV, $APLD $CIFR $DGXX These companies provide specialized AI infrastructure and hosting. Why it matters: - Cloud alternatives are growing - AI-native infrastructure is becoming valuable - Capacity shortages create pricing power 6. Energy (The Ultimate Constraint) Tickers: $CEG, $NEE, $EOSE, $GEV, $EQT, $VST $OKLO $BE $FLNC AI consumes enormous electricity. Power availability is becoming a limiting factor. Why it matters: - Grid demand is surging - Battery storage is essential - Reliable baseload power matters Final Thought The market often focuses on one winner. But AI infrastructure is an ecosystem. If you want to understand where capital flows next, follow the stack: Compute → Servers → Memory → Networking → Infrastructure → Energy The biggest winners in the AI cycle may not always be the obvious names. Sometimes the best opportunities are in the supporting layers that make the whole system possible.