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Some weeks feel like a normal sprint; this one feels like a roadmap review where the roadmap includes orbit, oncology, and another ‘quick’ infra fix that somehow becomes the business. A YC baby unicorn is already shipping compute to space on the thesis that launch costs will eventually behave like cloud credits. Eli Lilly is quietly doing the grown-up version of ‘AI will change drug discovery’, turning a model lab into a milestone-priced distribution channel.
Meanwhile, everyone else is trying to claw back GPU margin with automation, shave nine clicks off diagram edits, and pretend funding geography is a ‘go-to-market choice’ instead of gravity. Consider this your Tuesday standup: velocity is up, trust is… still in QA.
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Six bullets of updates
💰 South Korean fabless AI chip startup just nabbed an extra $400M post-Series C to fuel production and R&D momentum.
🌐 Over 70% of UK startups are outside London, but most venture funding still funnels into the capital.
🧬 Synthesized patient data could power digital twins for better clinical AI, solving data shortages across healthcare.
🚀 ScaleOps secures $130M to automate AI infrastructure, aiming to slash GPU costs by up to 70% and boost performance.
🤑 CEO Nikesh Arora signaled confidence by snapping up $10M in shares, sparking a 6% jump amid AI disruption fears.
🤖 Nearly 70% of Americans have tried AI tools, but most still question their trustworthiness, citing worries over transparency and regulation.
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Starcloud’s $1.1B bet: GPUs in orbit
Photo by Muhammad Nishfu on Unsplash
Apparently, the fastest way to cheaper compute is to leave the planet. Starcloud just raises $170M bet and hits $1.1B valuation, only 17 months after YC demo day, off the back of a single H100 in orbit and a roadmap of bigger GPU sats, Blackwell hardware and even bitcoin miners.
The bet is simple: if Starship delivers ~$500/kg launch costs by 2028–29, a Starcloud‑3–class craft could approach $0.05/kWh and finally make orbital data centers competitive. Until then, the economics remain brutal — power, cooling, synchronization for thousands of GPUs, and dependence on someone else’s rocket schedule.
For space-compute and infra founders, this round signals that “AI + hardware + regulation/physics risk” is fundable at unicorn valuations long before unit economics exist. The edge accrues to teams that bank real operational data early (even from sacrificial first-gen hardware) while chips, launch, and laser-link networks slowly make the physics pencil out.
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Idea to Exit (and the Most Common Mistakes Founders Make)
Most startup timelines are fiction.
We broke down the real founder journey, from team-building to M&A, with lessons from real founders who went through the process.
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88% resolved. 22% stayed loyal. What went wrong?
That's the AI paradox hiding in your CX stack. Tickets close. Customers leave. And most teams don't see it coming because they're measuring the wrong things.
Efficiency metrics look great on paper. Handle time down. Containment rate up. But customer loyalty? That's a different story — and it's one your current dashboards probably aren't telling you.
Gladly's 2026 Customer Expectations Report surveyed thousands of real consumers to find out exactly where AI-powered service breaks trust, and what separates the platforms that drive retention from the ones that quietly erode it.
If you're architecting the CX stack, this is the data you need to build it right. Not just fast. Not just cheap. Built to last.
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🎭 In VC, decks are theater; 3 founder traits predict success faster than slides—minutes, not months.
🧭 As scope widens, execs make fewer, better calls; despite teams getting interrupted every 2 minutes.
🖼️ A forked Excalidraw extension auto-exports frames to SVG locally in VSCode, cutting 9 clicks and ~45s per edit.
Startup Investor Finder
Slidebean’s Investor Finder helps startups find and track the right investors faster. Filter by stage, industry, and location, organize outreach, and manage your fundraising pipeline in one streamlined workspace. No random spreadsheets, no scattered LinkedIn tabs; just a focused system to turn research into real fundraising traction.
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Lilly just put a $2.75B price on AI drugs
Photo by Myriam Zilles on Unsplash
The AI drug gold rush just found its first blue-chip refinery. Eli Lilly is effectively productizing an external model lab, signing a $2.75 billion rights deal with Insilico Medicine to commercialize AI-designed therapeutics, with $115 million paid upfront and the rest tied to milestones and royalties.
This isn’t a one-off bet; it formalizes an earlier AI software licensing tie-up into a pipeline-scale partnership covering 28 programs, nearly half already in clinic. Lilly keeps late-stage and regulatory muscle; Insilico keeps the discovery “compiler” and leverages Lilly’s Gateway Labs network as distribution.
For AI biotech, the deal sets a reference price: platform economics, milestone-heavy risk sharing, and geography arbitrage — Insilico builds models in Canada/Middle East, runs preclinical in China — bundled into a single, globally marketable asset.
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Startup Events and Deadlines
Startup Funding Rounds in the AI Era l TODAY l Webinar
How to Pitch an AI Company to Investors | April 07 l Webinar
PearX | April 12 | Apply
Curinos FinTech Incubator | April 20 | Apply