😺 🎙️ We're LIVE now with Google DeepMind's Logan Kilpatrick

Welcome, humans.

We’re going LIVE right now with Google DeepMind’s Logan Kilpatrick to talk all things Gemini 3 Flash—a model that outperforms Gemini 2.5 Pro (their last top model) while running 3x faster at less than 1/4 the cost.

It's frontier-level reasoning at Flash-level speed, and it's rolling out globally right now.

We plan to explore what this capability actually means for developers, knowledge workers, apps in production, and anyone trying to figure out how AI fits into their workflow.

What we'll cover:

• 🔥 Live demos – Logan will show us Gemini 3 Flash in action, from coding to multimodal understanding
• ⚡ What's now possible – Use cases that weren't practical with previous models (or weren't possible at all)
• 🛠️ Building together – We might wire up a tool live if Logan's game (we've got ideas)
• 💰 Intelligence too cheap to meter – We'll dig into the economics: when AI gets this powerful and this affordable, does it change the hiring calculus?

On that last point: right now, data shows AI is raising wages for AI-impacted roles because workers who use AI effectively can command higher salaries. But what happens when frontier intelligence costs $0.50 per million tokens? When does “intelligence as a commodity” flip from “AI makes workers more valuable” to “why hire a human?” We’ll see if we can get Logan’s take on this topic!

Key specs on Gemini 3 Flash:

• Outperforms Gemini 2.5 Pro across most benchmarks
• 3x faster than 2.5 Pro
• Less than 1/4 the cost of Gemini 3 Pro
• 1M token context window
• Advanced visual and spatial reasoning with code execution
• 78% on SWE-bench Verified (agentic coding)
• Rolling out globally in Gemini app, AI Mode in Search, and developer platforms

Logan has been at the center of Google's push to make frontier AI accessible to millions of developers. If you're shipping products, building with AI, or just trying to wrap your head around where this is all going, this conversation will give you clarity.

Click here to join us live, ask questions in chat, or catch the replay after!

ICYMI: Sam Liang of Otter.ai On How Most enterprise knowledge dies in meetings (And What to Do About It).

Did you know meetings are your company’s most expensive activity? By Sam Liang’s measurements, your company spends 50% of payroll on meetings… and he has a plan to fix it.

You spend thousands—sometimes millions—paying people to sit in conference rooms, and 90% of what's discussed is forgotten within a week.

There's no ROI calculation, no way to measure value, and cross-functional teams are constantly reinventing the wheel because nobody knows what was decided three meetings ago.

Sam Liang, CEO of Otter.ai, built a company that's transcribed over 1B meetings and crossed $100M in annual revenue by solving this problem.

But Otter isn't just a transcription tool anymore… it's becoming an enterprise knowledge base that turns voice data into actionable intelligence.

In our latest episode, Sam breaks down how meetings went from the most wasteful activity in business to a goldmine of structured data:

• (4:48) Building a meeting-centric knowledge base: Why voice data is actually more valuable than written documents.
• (6:14) Calculate the cost of your meetings: Why this is your #1 expense (and you're not tracking it).
• (9:11) The Google Doc transcript problem: Transcripts sitting unused are as useless as no transcript at all.
• (17:34) Real-time leader dashboards: How executives track projects and team performance through meeting data.
• (18:49) Send your AI avatar to meetings: Yes, this is real… and it can ask questions on your behalf 🤯
• (21:45) AI agents that talk back: Otter can already answer questions mid-meeting by pulling from Salesforce… and soon, it will proactively contribute too (we might end up with fact check bots in every meeting!)
• (26:08) The dialect problem: Sam explains how voice AI handles accents, tone, and subtextual context in real-time voice AI.
• (37:25) 2027 prediction: Will your avatar attend board meetings while you're at the beach?
• (43:02) $1 billion in customer ROI: One financial firm says every 20 Otter seats = 1 full-time employee saved

Bottom line: If your team spends 30-50% of their time in meetings (and you do), that's your biggest expense. Otter proved you can measure it, optimize it, and turn those conversations into a searchable knowledge base that actually gets used.

Listen and our watch now on YouTube | Spotify | Apple Podcasts.

Stay curious,
The Neuron Team

P.S. Sam mentioned they're building real-time sales coaching that surfaces answers on your screen during customer calls—basically like having a cheat sheet during an exam. If you're in sales or customer success, (42:09) is worth a listen. 📞

Dive deeper with these resources:

About Otter.ai:

• How Otter reached $100M ARR and built its AI meeting agent suite
• Otter's HIPAA compliance for healthcare organizations
• Try Otter.ai

About Gemini Flash:

• Build with Gemini 3 Flash - Official launch announcement
• Gemini 3 Flash for Enterprises - Enterprise use cases
• Gemini 3 Flash - DeepMind model page with demos

Stay curious,
The Neuron Team

That’s all for today, for more AI treats, check out our website.

ICYMI: check out our most recent episodes below!

• Pavan Davuluri of Microsoft on the Future of Windows
• (YouTube, Spotify, Apple)
• Tim Davis of Modular on breaking NVIDIA’s chokehold
• (YouTube, Spotify, Apple)

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