European startups have done eight $1b+ raises in the first half of 2026, compared to none one year ago. @dig_ventures General Partner @MelissaLester4 on European VC checks getting bigger: "We have the talent here. Now, we need to catch up. Which we are on both the capital and the experience side of things." "If you compare the first half of this year to the first half of last year, we've seen eight $1 billion plus raises just in the first half of this year, contrary to none last year." "...European funds are starting to really step up in the size of the investing that they're doing, which typically founders would have to go over to the US to achieve." "I do think the pendulum is changing."

Stifel says DRAM supply would need to grow ~50% in 2027 to close deficit but expects bit growth to slow to just ~20% while HBM4 pricing per bit could roughly double. That keeps memory cycle tighter for longer with $MU and $SKHY heading into another major step-up in HBM pricing.

$SNDK Datacenter supplied 88% of Sandisk’s sequential revenue increase in fiscal Q1 2027. That makes the durability of AI storage spending central to the next stage of its growth. 1. Where the growth came from Revenue rose 23% sequentially to $11.031B. Datacenter contributed $4.802B, up 61%, lifting its revenue share from 33.2% to 43.5%. I calculate that datacenter supplied $1.825B of the company’s $2.066B net revenue increase. Revenue growth combines volume, pricing and mix; it should not be read as shipment growth. 2. The next-quarter test Fiscal Q2 guidance calls for $12.2B–$12.6B revenue, 84.5%–85.5% non-GAAP gross margin and $55–$57 non-GAAP diluted EPS, all EPS pre-split. The revenue midpoint implies 12.4% sequential growth. The margin midpoint is slightly below Q1’s 85.1%. Management is forecasting further expansion with broadly stable margins, rather than another step-up in profitability per sales dollar. 3. Keep the weaker business in view Consumer revenue fell 29% sequentially. Datacenter’s larger contribution makes its purchasing cycle more important. This quarter establishes growth; it cannot establish how long today’s pricing and margins will last. 4. What the split changes The 8-for-1 split is expected to begin adjusted trading November 20, following the November 9 record date and November 19 distribution. The same adjustment applies to EPS. Comparing a post-split share price with pre-split earnings would understate the valuation multiple by eight times. The split itself creates no additional earnings or ownership. 5. What I would track next Datacenter demand, realized pricing, manufacturing costs and cash generation will determine whether this earnings level is durable. Supply expansion and customer spending changes are the risks to test. My read: the business evidence supports datacenter-led growth. Turning that into an investment return still requires a purchase price that leaves room for a less favorable memory cycle.

The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field. I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so. First, I don’t see any step up in the risk of human extinction from AI compared to a few months ago. The theories about this remain the same fantastical, science fiction scenarios as a few months ago. The biggest change in AI risk is its cybersecurity capabilities — a topic which we should take seriously — but this, too, will not lead to the end of the world. The most notable recent event leading to increased fear was when an OpenAI team deployed an agent swarm that hacked into Hugging Face. Much of the popular press contained significant hype. For example, some publications reported that a swarm of 1,200 agents carried out the attack. While this was technically accurate, as I write this, I have about 1,300 processes running on my laptop. Yes, the ability to get large swarms of agents to work in parallel on a task is a significant technical advance, And, in computing, many processes run at the same time. So this shouldn’t be seen as some magical capability. Additionally, OpenAI’s buggy sandboxing and monitoring processes were key to enabling this incident. Fixing these bugs and putting in place improved monitoring would be appropriate fixes, not pausing AI. There are many well known ways to attack software systems. The main advantage of AI agents is that they are relentless. They will tirelessly try many tactics — and have the patience to chain vulnerabilities together — that previously would have taken an infeasible amount of human effort. But in the long term, I believe the advantage will lie with defenders (because they have more information with which to identify bugs, which they can fix), but the cyber-threat landscape has changed significantly. There are still bottlenecks to identifying and exploiting a vulnerability. AI agents still have to try a lot of things to see what works, and taking these actions takes time and might be detected by defenders. This is why, even though it is now easy to obtain versions of leading open weight models that have had their guardrails removed or weakened, so they will not refuse to try to execute cyber attacks, the world has not ended. I am also concerned about the anthropomorphization of AI in a lot of reporting, where LLMs and agents are unnecessarily treated as if they were people. If I wield a hammer, miss a nail, and accidentally dent the wall, it’s not the fault of the hammer. The problem lies in how I used the hammer. Similarly, if I prompt an agent and it hacks into someone else’s system, the responsibility lies with me, not the agent. Of course, we want to build systems that are as safe and predictable as possible. (For example, an unsafe hammer would be one whose head randomly flies off under normal use.) Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use. One new element in the forecasts of AI-enabled doom is AI companies disclaiming responsibility for their own products. “I didn’t do it; my out-of-control agent did!” There’s a balance to be struck between the responsibility of the tool maker and the tool user, but when something goes wrong, let’s hold the people building and/or using the hammer responsible, rather than the hammer. (By the way, if you’re worried about AI bioweapon risk, David Bellamy has a great post on why this, too, is overhyped. Briefly, the bottleneck in building a bioweapon is not intelligence, but lab work and manufacturing.) Pausing AI progress will create much more harm than benefit. First, our adversaries will certainly not slow down. Second, engineering requires discovering problems empirically so we can fix them. If we pause AI by a decade, we will also delay finding and implementing safety engineering fixes by about the same duration. Of course, the incentive to stoke fears — for regulatory capture, to garner attention, or to make one’s technology seem more powerful — remains the same as before. Disclaiming responsibility is a new one. Taking a hard technical look at the actual risks however, I see little factual basis for the degree of fear that’s been stoked up. We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building. [Original text (with links): ]

Rivian $RIVN CEO RJ Scaringe told CNBC that 2027 is an inflection year as R2 volume really starts to show. He expects positive auto gross margin by year end, independent of credits, after a significant step up in volume in Q4. Level 4 autonomy will first deploy with $UBER in 2028, and a year end milestone could bring in another $250M equity investment. Volkswagen has funded $5.3B of $5.8B in expected JV capital, and Amazon $AMZN demand has stepped up this year. Rivian is replacing its current Nvidia $NVDA chips with RAP1, which it designed in-house. TSMC $TSM makes it on 5nm, with two in each vehicle for 1,600 TOPS.

Today, on National First Responders Day, we recognize the LVMPD department members who answer the call, step up in moments of uncertainty, and work every day to keep our community safe. We honor and celebrate the dedicated men and women who put their lives on the line daily to keep our communities safe. We also recognize our fellow first responders across the valley who work alongside us to protect lives and help those in need. To all who serve on the front lines, thank you for your dedication, sacrifice, and commitment to our community!

$AAPL - MORGAN STANLEY SEES “APPLE AI” BULL CASE STRENGTHENING Morgan Stanley analyst Erik Woodring maintains Overweight with a $360 price target, saying Apple’s latest launches represent a major step-up in innovation. He sees the iPhone Duo as Apple’s most innovative device in years, while stronger iPhone 18 Pro upgrades and broader Apple Intelligence integration could accelerate replacements. With 57% of iPhone users on devices older than the iPhone 15 Pro, Woodring sees potential for multiple years of above-trend growth.

Nope, the latest Qwen3.8-Max-0902 slid back in my Cosmic Dodge game test. It took 3 attempts to get the prompt correct and a final one to smooth out the buggy gameplay. The original Qwen3.8-Max one-shotted this game perfectly. Not a step up in this sample of 1 test.

I used Grok 4.6 to build a 2D agent simulation of The Office. Here are notes and observations on 4.6: It is a huge step up from 4.5 - both in the quality of visual outputs and in the breadth of work: personality, features, and finding usable assets for this project. The editorial content and writing were good - I like the "feel" of this model. One of the biggest wins for 4.6 is speed. The throughput (~80 tok/s) and latency are so good that it feels Composer-adjacent - something @ericzakariasson called out in his writeup. That speed + cost + performance makes for a true "workhorse" model that's practical for both personal and work use. My goal was to simulate The Office through "agents" that behaved like the characters - including all of the neuroticism and idiosyncrasies that made the show so funny. I knew what I wanted artistically - I've built similar 2D apps with pixel art. I asked Grok 4.6 to research psychological modeling, pull character descriptions, find references for the cast, and assemble 2D sprites with the Universal LPC sprite sheet generator and some RPG tilesets I grabbed online. I rattled a brief into the @cursor_ai mobile app late one night at the Cursor office and woke up to an MVP. Then I spent a day or so spamming cloud agents until the simulation worked how I liked. The cool part is that Grok did the simulation, art, and research on its own: - Every couple of minutes, each person scores a few possible actions and picks one. They pathfind around desks, work, grab coffee, talk at the cooler, hide in the annex, or end up in a meeting. - Click someone and you can inspect traits, needs, mood, relationships, and whatever they're doing right now. You can also speed time up, force Michael to call a meeting, or roll a random office event. - Each character has Big Five scores that bias what they tend to do. Needs like work, social, caffeine, status, and comfort rise over time and fall when they do something about them. - Grok reconstructed the layout from Dunderpedia (Office Wiki) and online sources - Michael's office and the conference room on the north glass, Pam at reception, sales and accounting in the bullpen, the annex to the east, restrooms and the warehouse down the stairs. - Grok helped me find a few sprite sheet packs to implement. I was surprised at how accurate some of the character sprites were - from hair color to clothing. The combination of a performant model, cloud workspaces, and high throughput made for a really fun devex - the amazing folks at @cursor_ai and @SpaceXAI make my job easier everyday. A huge shout-out to @_Brian_Zhang, whose Notion hackathon project inspired this exploration.