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Sr. Content Developer at Microsoft, working remotely in PA, TechBash conference organizer, former Microsoft MVP, Husband, Dad and Geek.
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Get caught up on the latest technology and startup news from the past week. Here are the most popular stories on GeekWire for the week of Aug. 16, 2026.
Sign up to receive these updates every Sunday in your inbox by subscribing to our GeekWire Weekly email newsletter.
404 Media put an Apple AirTag in a rare book and tracked it to an Amazon warehouse in Las Vegas, where a team cuts the bindings off books and scans the pages. The reporting method has roots in a Seattle non-profit group. Read More … Read More
A year after GeekWire asked where Seattle’s superstar AI startups were, two local venture capitalists offer an answer: they’re here, but they build rockets, geothermal plants and autonomous off-road vehicles rather than software. A guest opinion from Ascend’s Nate Bek and Plug and Play’s Ben Eisinger. Read More … Read More
Qualtrics laid off employees across Seattle, Provo and international offices Wednesday, three months after closing its $6.75 billion acquisition of Press Ganey Forsta. The company declined to disclose how many jobs were cut, but a WARN notice reviewed by GeekWire indicates at least 50 cuts were made at its Seattle headquarters. Read More … Read More
After the U.S. Navy abruptly canceled a key unmanned warship program, defense giant Anduril quietly vacated its shipyard site on Seattle’s Lake Washington Ship Canal—even as its broader regional footprint continues to surge toward 1,000 local engineers. Read More … Read More
Expedia Group is parting ways with at least eight vice presidents and senior vice presidents as it reorganizes its product and technology organization around AI, according to an internal memo obtained by GeekWire. The memo from the company’s product and technology chiefs also names five leaders who were promoted. Read More … Read More
GitHub was down for more than three hours Monday morning, breaking the website, code review tools, automated build systems and Copilot. It’s the latest reliability problem for a platform whose capacity planning has been outrun by AI coding tools. Read More … Read More
An AWS director who ran the launch of Oracle Database@AWS has joined Oracle. Also in this edition: Seattle Children’s names a Stanford informatics leader as CIO, Zillow fills a newly created chief legal and policy role and a former Convoy executive is Uber Freight’s new product chief. Read More … Read More
GeekWire toured Anduril’s unmarked Bellevue office, where the defense company is building augmented reality glasses, digital night vision and edge computing hardware for the U.S. military. Senior vice president Tom Keane says the regional engineering workforce could easily grow from 560 people to 1,000 in the not-too-distant future. Read More … Read More
Endurance Energy is running full-tilt in its cavernous new Seattle facility as it prepares to deploy a 100-kilowatt generator to an oceanic volcano off the coast of Oregon. The company is betting that the technology can one day electrify entire cities. We got a look inside. Read More … Read More
The King County Library System opened a $5.2 million sorting installation in Renton that handles the 25,000 items moving through its 50 libraries each day. Robotic arms destack totes every 15 seconds and route materials into 186 delivery chutes. Read More … Read More
Jen Abel is the co-founder of JJellyfish and GM of enterprise sales at State Affairs. She is widely regarded as one of the sharpest practitioners in enterprise sales, and for that reason, this is her third visit to the podcast. In our first conversation we went deep on founder-led sales; in our second we mapped the $1M–$10M playbook. This time we do something I’ve never seen on another podcast: walk step by step through the full enterprise sales cycle. Most people think it’s five steps. Jen shows it’s closer to 15.
In our in-depth conversation, we discuss:
1. Why the standard five-stage CRM pipeline is a forecasting tool, not a sales process, and what the real 15-step cycle looks like
2. The “pincer model” for landing the first meeting at the executive and N-minus-one level simultaneously
3. How to craft a two-to-three-sentence message around giving them “alpha”
4. How to run an intro call that extracts maximum intelligence before you ever show a demo
5. The two-to-three-day pilot structure, how to define success jointly, and when to charge for a longer pilot versus giving it away
6. Navigating pricing, procurement, redline negotiations, and the final signature without losing momentum
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Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Mercury—Radically different banking, now with Command
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Where to find Jen Abel:
• LinkedIn: https://www.linkedin.com/in/earlystagesales
• Website: https://www.jjellyfish.com
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Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
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In this episode, we cover:
(00:00) Introduction
(02:49) Why Jen is giving away her enterprise sales playbook
(05:54) Example: selling AI legal tech to SpaceX
(09:11) Step 1: Landing the meeting
(18:44) Step 2: Running the intro call
(30:00) Step 3: Running a follow-up call
(36:30) Step 4: Prepping the pitch/frame for demo
(38:36) Step 5: Running the demo
(46:45) Step 6: Post-demo debrief
(48:59) Step 7 through 9: Preparing for and running the pilot
(01:03:44) Why the standard five-stage CRM pipeline doesn’t work
(01:04:49) Step 10: Post-pilot session
(01:07:48) What a healthy enterprise win rate looks like
(01:11:45) Steps 11 through 14: Navigating procurement and getting the signature
(01:18:01) How to think about expansion
(01:19:18) How buyers can say no without wasting everyone’s time
(01:21:53) Why enterprise sales is a lot like product management
(01:22:27) Closing thoughts
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Referenced:
• State Affairs: https://stateaffairs.com
• The ultimate guide to founder-led sales | Jen Abel (co-founder of JJELLYFISH): https://www.lennysnewsletter.com/p/master-founder-led-sales-jen-abel
• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr
• SpaceX: https://www.spacex.com
• Lemlist: https://www.lemlist.com
• Palantir: https://www.palantir.com
• Jason Lemkin on X: https://x.com/jasonlk
• We replaced our sales team with 20 AI agents—here’s what happened | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents
• Building a world-class sales org | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/building-a-world-class-sales-org
• Careers at State Affairs: https://stateaffairs.com/careers
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
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Lenny may be an investor in the companies discussed.
Starting August 25, we're running a free three-part live series on Microsoft Reactor that takes you from "what is a hosted agent?" to a deployed, orchestrated multi-agent application, all built in VS Code with the Microsoft Foundry Toolkit.
Building an AI agent usually means context-switching between a portal, a terminal, a notebook, and three browser tabs of documentation. You write some instructions, deploy, wait, test in a playground, discover the agent hallucinated a policy that doesn't exist, and start over. The feedback loop is measured in minutes, not seconds.
The Microsoft Foundry Toolkit for VS Code collapses that loop. You generate an agent from extension using the Command Palette, configure the model and instructions, debug it locally against Agent Inspector, and deploy to Microsoft Foundry Agent Service.
We've all written the perfect post-mortem, root cause, timeline, remediation, only to get back: "So… is the website down or not?"
Session 2 builds a single-purpose agent that takes this:
"The API latency increased due to thread pool exhaustion caused by synchronous calls introduced in v3.2."
…and returns this:
Executive Summary:
- What happened: After the latest release, the system slowed down.
- Business impact: Some users experienced delays while using the service.
- Next step: The change has been rolled back and a fix is being prepared before redeployment.
Session 3 steps up to a multi-agent workflow: four agents that collaborate to parse a résumé and a job description, extract the real requirements, score alignment, identify gaps, and generate a personalized learning roadmap backed by Microsoft Learn resources.
This is where you see orchestration patterns, agent-to-agent handoffs, and how to keep a multi-agent system debuggable .
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Date and Time |
Speaker |
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1️⃣ What Is Microsoft Foundry Toolkit for VS Code? What's New and How to Get Started |
Foundation session touring the toolkit's workflow: agent creation, model configuration, local debugging with Agent Inspector, and deployment to Microsoft Foundry Agent Service. |
Tuesday, August 25 12:00 PM UTC |
Junjie Li Senior Product Manager, DevDiv, Microsoft |
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2️⃣ Build Your First Microsoft Foundry Agent: Executive Briefings from Technical Updates |
Hands-on build session creating a Python hosted agent, authoring instructions, testing safety boundaries in Agent Inspector, and deploying to the cloud. |
Tuesday, September 1 2:00 PM UTC |
Bethany Jepchumba Cloud Advocate, Microsoft |
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3️⃣ Build a Multi-Agent Career Copilot: Resume-to-Job Fit Analysis |
Orchestration session building a 4-agent collaborative team that parses résumés and job descriptions, scores alignment, finds gaps, and generates learning roadmaps. |
Thursday, September 3 3:00 PM UTC |
Shivam Goyal Microsoft MVP (AI) |
Every session maps to our workshop in the companion repo with 50+ languages supported, so you can build alongside us or catch up afterward:
Sessions run on August 25, September 1, and September 3. Register once for the series and you're in for all three, and you'll get the recordings even if you can't make it live.
Bring your questions and see you there. 🚀
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Jurassic Park (1993) has about 60 visual effects shots containing computer-generated elements.
In 1993, photorealistic, cinema-quality CGI video took much longer and cost much more to produce than it would today. The high-end Silicon Graphics computers used by Industrial Light & Magic took 10-12 hours to render each frame. A lower-end smartphone could do it in 2-3 seconds today.
As the technology got faster – and the software got better and easier to use (think of what kids can do on laptops today) – the cost of CGI shots plummeted.
This was great news, because now studios needed far fewer CGI artists and technicians to make their movies. And that’s totally what happened. All the visual effects for Avengers: End Game were done by one guy called Barry working Tuesdays part-time.
Jurassic World has 1,000 computer-generated shots – 16x as many – and each frame contains about 1,000x as many polygons being animated and rendered.
But, bafflingly, Jurassic World only has a 6.9 rating on IMDB, compared to Jurassic Park’s 8.2 rating – even though Jurassic World is obviously 16,000x as good.
Could it be, perhaps, that more CGI with more polygons produced by more computing power isn’t necessarily what excites audiences? I remember every CGI shot in Jurassic Park. I struggle to remember any specific shots in Jurassic World, CGI or otherwise.
Maybe the creators of that sequel were so preoccupied with whether or not they could do more CGI shots with more elements and more detail, they didn’t stop to think if they should.
During my career, the computing power available has similarly exploded. The laptop I use day-to-day is easily 10,000x as powerful as the 486 I started on, and the tools I use are far more advanced. Compiles that used to take 15-20 minutes now finish in 15-20 seconds. I can run 10,000 unit tests in 2-3 minutes, whereas in the 90s I’d run a suite that big overnight.
But, like with CGI in movies, this has not produced a proportional increase in productivity or in value created. It could be argued that the very low cost of computation today has made little difference at all. Sure, there are way more developers now, creating way more software. But is it software anybody really wants or needs? Can society absorb that much software that fast?
The high cost of CGI in Jurassic Park meant that the filmmakers had to make every shot count. Just a couple of years earlier, the makers of Terminator 2: Judgement Day (IMDB rating: 8.6) had to make even more with even less.
Recently-published research found that the more elements in a shot, the less viewers paid attention. The brain only has so much visual bandwidth, and modern CGI-heavy action movies tend to overload viewers with visual information – making the overall experience more “Meh” than “Wow” (thanks to Rob Bowley for the inspiration).
And FX budgets haven’t shrunk with the cost of compute, either. They’ve ballooned. The average Marvel fare now has the equivalent of a whole town working on dozens of elements in thousands of shots. Jevons paradox in action.
So all this extra compute was for naught, Jason? Not quite.
There are some shining examples where the power was used not to create More StuffTM, but to create better stuff, and to create it cheaper.
Monsters (2010) cost famously little to produce, and the 200-or-so CGI effects shots really were done by one guy (called Gareth, working full-time) on consumer hardware. The whole movie cost about £300,000 to make. Here’s the thing, though – Gareth Edwards knew what he was doing.
He knew how to use the technology, having busked as a VFX artist in television. But, more importantly, he knew about directing, and about cinematography, and about editing, and about story. It might not be the greatest sci-fi movie of all time, but for the price of a semi-detached house, it’s pretty miraculous.
James Cameron is another director who started his career doing a heck of a lot with not very much for famously frugal producer Roger Corman, and is equally well-versed in key technical disciplines as well as the creative ones. Ever wondered why the Avatar films – being almost entirely CGI – look so much better than most modern FX-packed movies?
The evidence seems to suggest that the most valuable way we can leverage all this extra computing power is not necessarily to create more, but to iterate and refine faster to bigger impacts. What’s come down – should we choose to use it – is the cost of getting it wrong and trying again (and again).
Compiling and running tests faster doesn’t mean I can ship more features. It means I can iterate features more times and learn more from that for the same cost.
And what matters more than ever are human skill, judgement and taste. That will always be in short supply.
Otherwise, we’re just shipping Jurassic Worlds faster than the audience can absorb them.
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