Episode Description
Apple’s latest decision to team with Google and use Gemini signals something bigger than a headline. It highlights how far Gemini has come, and why utility is becoming the real battleground for AI in 2026.
In this episode, we sit down with Logan Kilpatrick from the Gemini team at Google DeepMind to talk about what this moment means for developers, builders, and teams choosing tools today. We get into the evolution of Gemini, why multimodality and long context mattered early, and how Gemini is increasingly showing up where people already work through products like Workspace, Gmail, and Docs.
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Danny Thompson
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Leon Noel
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📧 Have questions for the show? Or are you a business that wants to talk business?
Email us at dannyandleonspodcast@gmail.com!
Subscribe if you want honest, no-hype conversations about where software development is heading in 2026.
We also dig into what’s next for image generation and editing, including the leap in text accuracy, practical features creators want (like layers and transparent backgrounds), and the role of technologies like SynthID in building trust as AI content becomes more common.
Plus: vibe coding, reasoning models, prompt expansion, how developers can ask for more ambitious outcomes, and why proactivity and proof-of-work experiences are shaping the next wave of AI products.
If you’re building with AI or trying to stay ahead as a developer, this episode will give you a clear view into where things are going.
0:00 Apple’s decision changes the AI narrative
1:11 Meet today’s guest: Logan Kilpatrick (Gemini team)
1:52 Apple quote and why this is big for Gemini
2:19 Are the model wars over, and are utility wars next?
4:20 Gemini in real workflows: Workspace, Gmail, and daily utility
5:26 AI hype vs reality: why products must meet users where they are
6:42 Balancing frontier progress with “do not break the product” responsibility
8:25 Nano Banana and the leap in text accurate images
9:28 Infographics, presentations, and where image generation is going
10:00 Why text rendering became a key metric for model quality
12:19 What users want next: transparent backgrounds, layers, better editing
13:52 Trust and safety: SynthID, watermarking, and standardization challenges
16:06 What developers ask for most: quota and scale
17:38 Logs and the idea of an agent that finds missing product features
19:31 How people should use AI differently: native form factors, proactivity
21:19 Proof of work and why Deep Research builds trust fast
24:53 Prompt expansion, reasoning models, and why “prompt wizardry” is fading
26:40 Ask for more: why ambition is now the best prompting strategy
29:36 Frameworks vs over optimizing prompts: when it matters, when it does not
32:52 What “vibe coding” means now and how it is evolving
34:08 Teaching vibe coding on day one of school
36:01 Ask Danny, Leon, and Logan: feeling behind in tech
39:02 The moving finish line, authenticity, and turning the gap into a roadmap
41:25 Closing