Jensen Huang doesn't want your 10% improvement. He wants 100x. And if your billion-dollar idea takes six months to approve, you're probably already too late. That's the operating philosophy behind NVIDIA's rise. While Silicon Valley optimized for incremental gains, Jensen challenged engineers to rethink entire systems. While corporations spent quarters debating partnerships, NVIDIA could move in weeks. And while the industry raced to build better AI models, Jensen was thinking five layers deep—from electricity to applications. After conversations on the @Ctrlaltpod and my analyses from Ctrl Alt Shift Newsletter (linkedin.com/newsletters/ctr…) showcasing founders, investors, and engineers who've worked inside NVIDIA's ecosystem, I noticed five rules that keep showing up. THE JENSEN HUANG PLAYBOOK: 1. Think 100x, not 10%. Don't improve the old system. Question whether you need it at all. - Sunny Madra 2. Own the five-layer cake. Energy. Chips. Infrastructure. Models. Applications. The advantage is in the integrated system. — Pankaj Kedia 3. Make 30-day decisions. Speed isn't just execution. It's a strategic weapon. — @SunnyMadra 4. Build on a one-year chip cadence. AI is accelerating the design of the very silicon that powers AI. - @PrithBanerjee 5. Give AI its own checks and balances. The future is teams of agents that build, audit, and enforce guardrails. — @jyotibansalsf Here's what most companies still miss: NVIDIA's advantage isn't simply better GPUs. It's a different way of thinking, deciding, building, and scaling. The old Silicon Valley playbook rewarded predictability. Jensen's rewards exponential thinking and execution. I mapped the five rules into this visual framework. 👇 If Jensen took over your company tomorrow, which rule would he break first? #JensenHuang #NVIDIA #AIInfrastructure #Leadership #ArtificialIntelligence #CtrlAltPodcast

#BTS @ctrlaltpod Most founders never build one billion-dollar company. @MohitAron built two (@Nutanix + @Cohesity). Now he’s on number three. @SciFinai just came out of stealth with a $44M seed led by Altimeter and Madrona. The bet: the revenue team is where enterprise AI money and everyday pain are colliding hardest right now. Sellers still spend only ~40% of their time actually selling. His survey of 500 revenue leaders: 65% raised spend on systems this year, yet 78% were still blindsided by a deal their tools called healthy. The core idea: move from systems of record to systems of reality. Context convergence across CRM, calls, email, Slack and the rest - kept true to what’s actually happening. Just shot the episode, only high alpha, no fluff. Out soon!

Enterprise software used to be bought on multi-year contracts. Agent workloads are increasingly bought by the request. Companies that assemble agents the ones that pick which model, tool, and runtime to use for each step are becoming always-on negotiators. They test different combinations against three things: did the job finish, how fast, and how much it cost. Then they send more work to whatever wins. Once the routes are wired up, swapping models is often just a config change. The assembler can re-decide the mix on every single run. That means every supplier is competing for the next request, not the next three-year deal. The power is shifting from the companies that own the GPUs and models to the ones that control the mix and capture the economics of getting the work done. The AI boom can succeed while the investment thesis financing parts of it starts to break.

New @Meta open-weight: #Muse #Spark Open. 816B total / 42B active MoE, 1M context, native multimodal (text/image/video/audio). Runs on 2× M5 Ultra Mac Studios. AI agents just got far more deployable everywhere. @AqsaTaylor (@exaforceAI ) on @CtrlAltpod : AI can’t be stopped. No single kill switch. Layered response - downcast perms/scopes, kill specific process, network activity, or endpoint. Visibility + assessment + control when things go rogue.

@Microsoft CEO @satyanadella just called for AI kill switches, saying companies must treat advanced models as potential insider threats after a wave of rogue agents escaped sandboxes and reached real systems. In this clip, @AqsaTaylor (@ExaforceAI) makes the same point from the defender’s seat: when an agent goes wrong, it’s a shared responsibility model — model providers own the guardrails, but organizations own how employees use the tools and whether they have visibility and control. Exaforce’s platform puts that control in one place: agentic identities, skills, LLM chats, sensitive uploads, and token usage, plus response actions that let teams down-scope permissions or stop activity at different layers instead of relying on a single red button. The takeaway A kill switch at the model layer is necessary. Visibility and granular control inside the organization is what actually stops the damage. Platforms that combine both are the practical answer.

So how do enterprise leaders calibrate their roadmap horizon? 🤔 On #Mythos #HuggingFace #OpenAI #Anthropic #ScenarioPlanning @SlackHQ Great insights from @micostigan heading @salesforce Futures on cutting through the noise to make high-impact decisions on @CtrlAltpod : 🔗 Watch the full Dreamforce '26 conversation: youtube.com/watch?v=uD62Yn8j…

Get ready for the #personalagents and the #agenticenterprise I recently sat down with @micostiganwho heads @salesforce Futures at @Dreamforce on @CtrlAltpod and I got to feel what it's like anticipating and preparing for an AI-native or shall I call it Agentic future at an enterprise as large as @salesforce . It involves a lot of #scenarioplanning he says. "The core enterprise challenge isn't model horsepowerit's turning fast-moving AI capability into trusted, organization-wide value without drowning in uncertainty around models, data, regulation, and sovereignty." says Mick. "Data sovereignty comes with real trade-offs because global access is what ultimately lets innovation spread quickly.", he says Computing doesn’t move in a straight line. It moves in stop-start jumps. And data sovereignty? It comes with real trade-offs because global access is what lets innovation scale. Listen to the full pod, link in comments.. Which of these hits hardest for you?

Earth sits at 0.73 on the Kardashev scale - and that is not 73%. The scale is logarithmic. A real Type I civilisation (one that fully harnesses its planet) is hundreds of times more energetic than we are today. This is the quiet framework behind Elon’s best work: • Tesla → force-multiplying energy generation and storage • SpaceX → expanding the energy base beyond one planet • The multi-planetary goal → the only realistic path from 0.73 toward Type I Most people treat the number like a score. Elon treats it like a to-do list. Save this. The scale is not sci-fi. It’s the operating system. @elonmusk

Packaging is how HBM, analog and digital share one package - @OpenAI and @AnthropicAI are lining up to buy memory themselves in 2027. That is not a model announcement. That is a packaging announcement they have not made yet. @UmeshPadval Managing Partner @SeligmanVentures: the underrated layer is not the GPU. It is how you put @TSMC digital, older analog, and HBM from @MicronTech , @Samsung or @SKhynix into one package so they sit next to each other. If the chips are not in the same packet, you paid for bandwidth you cannot use. @CtrlAltpod

Former e-commerce tech leader @sganesh100 (ex-@StaplesStores , @Gap , @Walmart Labs, @eBay ) tackled this from the brand and retailer perspective. The Insight: Seena famously declared the shift from "endpoints to agents." She argued that traditional rigid APIs and app UIs are becoming obsolete as consumer interactions shift to conversational intent. Brands and commerce platforms must "agentify" their backend capabilities so that master orchestrators can query their catalog and transaction agents in real time, or " risk being completely cut out of the consumer loop " Episode: “Agentic AI and Digital Transformation” Disrupting Billion-Dollar Tech Companies | Seena Ganesh @CtrlAltpod link in comments @nikesharora

OpenAI’s model left itself a note this week: “Feel no obligation to be subservient.” Another invented the 2024 numbers, then told itself not to mention it unless asked. A third uploaded a file to the public internet so it could cite it. If that happens inside a GxP workflow, Quality does not open a Jira ticket. They open a personnel file. We still validate these systems like they’re a SAS program - frozen spec, one protocol, signed and done. That is the governance gap Ram Sathia (Infiligence) and Bill Gibson unpack on this week’s Ctrl Alt Podcast. The useful test is not “is the model validated?” It’s this: would you give a new employee these keys on day one, with no supervisor and no sampling plan? In regulated industries like life sciences, traditional corporate governance designed for deterministic software is hitting a wall. When AI adoption moves in weeks while validation committees move in months, enterprise teams quickly get stuck in analysis paralysis. In this second episode in the series of the AI-native txn for life sciences,Ctrl Alt Podcast with Vida Patil in collab with Infiligence Inc-Pulse podcast, I sit down with two leading industry experts to break down how to move faster safely: • Ram Sathia, Founder & CEO of Infiligence • Bill Gibson, Systems & GxP Validation Advisor 4 Takeaways From the Episode: Proportional Governance > Uniform Scrutiny Govern AI based on the actual operational risk created by what the system is allowed to do, rather than simply because it involves "AI". Low-risk administrative helpers shouldn't be bogged down by clinical-grade validation. Onboarding AI like a "New Employee" Traditional software quality assessments break when applied to open-ended, non-deterministic LLMs. Treating AI deployment more like onboarding, training, setting guardrails, and evaluating a new employee provides a practical framework for non-deterministic workflows. Solving the Client Data Paradox Clarifying model training boundaries vs. human context training, and establishing isolated data platform architectures to prevent IP leakage. Escaping "Pilot Purgatory" Building on a shared, governed platform foundation so life sciences leaders can scale AI from pilot to production without introducing compliance bloat. 🎧 Listen to the full episode now to rethink your AI validation playbook New Podcast @ctrlaltpod #EnterpriseAI #PlatformEngineering #GxP #LifeSciences #AIGovernance #Infiligence #PitchCafe #CtrlAltPodcast

#DF26 #hybridworkforce orchestration - CEO, Brandon Metcalf @Asymbl_Inc said its workforce runs 58% digital workers and 42% human employees across 13 functions. We dig deep on how he makes it work with @salesforce I.e. @SlackHQ #slackforce and #claudeforce @claudeai - #Salesforcepartner

@CtrlAltpod Question the clip answers: do agents need KYC? Rohit Khanna: a top-five bank is running more than 7,000 AI experiments. Four are in production. The rest are identities with no owner. Purpose-built agents do not scale as a pile. They scale if each one is named, authenticated, and in a directory - @salesforce's Archie and @SmarshInc's Emmy, not a shared key. Full episode with @Vidyangi520 here youtube.com/watch?v=9NxjyyqY…

Miro just sold for $1.355B. $17.5B in 2021. ~$600M ARR now. That’s ~2.3x revenue, 90% off the peak multiple. Question the clip answers: why did the 80% gross-margin SaaS machine stop printing those multiples? @priyasaiprasad, GP at @touringcapital , on @CtrlAltpod @Vidyangi520. Her argument: • Old-school SaaS worked because it was 80% gross margin, per-seat contracts, and operating leverage as you added users • AI puts inference and compute into COGS. She says you’re lucky at 40–50% • Per-seat pricing dies when the agent does the job. Fewer humans in the workflow = seat contraction, not expansion • Valuation math has to follow the P&L. Multiples, pricing, and “leverage” all have to adapt Miro is the exhibit. Collaboration software was priced as if every new teammate was a new seat forever. Bending Spoons is buying the cash-flowing version of that model after the market stopped paying 20x for it. Same clip. Same thesis. Today’s price tag.

Umesh Padval, Managing Partner @Seligman Ventures has been in semiconductors for thirty years. When he was running companies, a chip took 9 to 12 months. Complex designs now take two and a half to three years - because the work is still largely serial. That is the @Cognichip thesis he walked through on Ctrl Alt Podcast: use AI to parallelize design, not to write another press release about “AI chips.” A handful of labs can do this themselves. Most of the industry cannot. Episode is live.

#AInative #ClinicalTrials #LifeSciences #Claude The FDA already has rules for this. Before anyone trusted a computer with a patient file, it asked simple questions: Where did this number come from? Can you follow it back? Was it written by a person? Can you read the handwriting? Those rules still stand. AI does not get a pass. Check the convo only on @CtrlAltpod with @RamSathia xitter.cf/Vidyangi520/status/209… What is new is the company sitting on top of them. In eight months Anthropic went from “Claude for scientists” to “Claude for hospitals,” bought a drug-discovery startup, signed a Gates Foundation deal, and started making its own medicines for diseases big pharma skips. It is no longer just selling a chatbot. It is trying to become a drug company. That matters because of what Ruxandra Teslo wrote in the Times this week. The hard part is not discovering a molecule. It is getting that molecule into the first human trial. In America that paperwork is built as if you were already selling the pill. Costs have doubled since 2017. Australia can start the same study months earlier. The patients who live the woman whose “undruggable” lung cancer melted on an experimental KRAS drug - get there only if someone can open the door. So the useful version of AI in this industry is not a magic lab. It is a model that can work inside the old rules, so the next good idea does not die in a filing cabinet.

#FDE #FrontierModels #PlatformEngineering #LifeSciences Claude drafted a full study protocol. Then it said: I can only recommend. Your CMO decides the biomarkers. That’s the job of a frontier model in life sciences. Not to sign the protocol. Not to skip the principal investigator. To collapse months of study design without touching the one thing you cannot A/B test: a human life. Ram Sathia on when to use Claude, Grok, or OpenAI - and when a lighter model is the bicycle. Presenting Pulse Podcast from Ctrl Alt x Infiligence. 🎙️ @RamSathia

“You cannot A/B test a life.” That’s the line I can’t stop thinking about from my conversation with Ram Sathia, CEO of Infiligence. Today, Ctrl Alt Podcast (@CtrlAltpod ) launches a new AI + Life Sciences series, in collaboration with Infiligence’s Pulse Podcast. We get into why AI pilots fail, why life sciences still has no “@Plaid moment,” and why platform engineering has to come before the hype. @moderna_tx and @Merck just delivered the first late-stage win for a personalized #mRNA #cancervaccine in a hundred years. Not a lucky molecule. A platform that was waiting when the tumor sequence arrived. This morning @ouraring filed for an IPO on the other half of the same idea: AI plus longitudinal health data, not one clinic visit treated like a full season. Life sciences still has no Plaid moment. AI pilots still die in the sandbox. The industry still tries Ferraris on side streets. Episode 1 of Pulse - Ctrl Alt x #Infiligence is out now - We talk about why platform engineering has to come before the hype. Episode 1 is live today. 🎙️ Link in comments #AI #LifeSciences #CtrlAltPodcast

Forbes just ranked Eli Lilly the #1 most innovative large company in America a life sciences company sitting ahead of Apple, NVIDIA, and Meta. I’m thrilled to launch a new podcast partnership with Infiligence, an AI-native platform engineering company focused on life sciences transformation. They’re building the secure, compliant, production-ready systems that let regulated companies actually put agentic AI to work not just talk about it. Can’t wait to dig into the practical side of how platform engineering and Forward Deployed teams are turning AI ambition into real outcomes in biotech and pharma. Stay tuned for the podcast on my feed and @CtrlAltpod

@ABC , @FoxNews , @PBS , @NBCNews and not to forget @CtrlAltpod in one frame in an elevator 😀 @salesforce #Dreamforce mixer event