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Decision models like Jev don’t explain their answers. That makes context your problem
Plenty of teams have adopted AI. Far fewer organizations have. Atlassian’s Dr. Molly Sands explains why 👇
On Nov 12 in San Francisco, Molly joins Matt Marshall and Snowflake’s Will Allen to dig into why individual AI wins aren’t adding up to organizational ones, and what the top 14% of AI-native teams do differently.
For leaders driving AI, technology and operations strategy. Space is limited. Request your invite:
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Thinking Machines launches Inkling 2 Small with near-frontier performance on enterprise benchmarks
Only 14% of companies can kill a rogue AI agent within minutes.
On Beyond the Pilot Ep. #20#, Box CTO Ben Kus breaks down the “lethal trifecta,” sandboxing compromised agents, and why swapping models usually breaks the harness, not the model.
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AI economics: How to decide which AI investments are worth scaling
Are you free next Thursday? Join AI and IT decision-makers after work in SF for three candid conversations, food and drinks. Reserve your spot:
OpenAI launches GPT-6 Cyber with stronger exploit research and higher API price than Anthropic and Google
A model can only do so much if the infrastructure can’t keep up. When does dedicated GPU capacity make sense — and how much should IT teams manage themselves? Here’s what CIOs need to weigh as AI workloads grow. @AMD @Azure
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AWS launches agentic AI Kiro bots with memory and support for recurring development jobs
89% of executives say AI is making their people faster. Only 6% can point to clear ROI.
“6%, that’s like almost no one, right?”
Dr. Molly Sands of Atlassian’s Teamwork Lab on why faster individuals aren’t adding up to faster companies. She’ll go deeper with us in SF on Nov 12.
Request an invite:
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Why AI model choice is now an infrastructure decision
Your people are faster with AI. Is your organization? Dr. Molly Sands of @Atlassian’s Teamwork Lab on what the top 14% of AI-native teams do differently.
After work, Nov 12, SF.
Reserve your spot:
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Ex-Googlers' new Apache 2.0 model Ripple-65B Preview claims the U.S. open coding lead on two major benchmarks
Companies are paying more for AI and can’t always say what they’re getting. In our survey, 33% of those operating GPUs don’t measure utilization, and 40 of 156 judge spending by a financial return.
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Leading AI adoption? Your plans can only move as fast as your teams’ skills. Paul Bingham of @WGU joins AI and IT leaders after work on Nov 12 in SF to talk about closing that gap.
Reserve your spot:
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AI teams agreed on the wrong answer in 75% of debates — and lower token bills hid the failure
What the 'Blue Wave' in the 2026 midterm election means for the American AI industry and businesses
Hard questions at 5:30. Drinks after. New VentureBeat research, leaders from @Snowflake, @Atlassian and @WGU, and an open floor. SF, Nov 12.
Reserve your spot:
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While proprietary models from #OpenAI# and #Anthropic# dominate enterprise use, inference token spend has been gradually shifting to open-weight models.
#AI# #GenAI# #AIAgents# #GenerativeAI# #LLMs#
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'Like gene editing for neural networks': MIT researchers used GPT-6 Astra to edit Qwen's open weights directly, boosting math performance
