Welcome to Venture Logbook. Today in Trend Spotlight, we analyze how Google's Gemini 3 could create a new cloud-plus-AI world, and in Startup Unboxed, we explore a pre-founder's journey toward true founder-market fit. We'll also define "model inference" in our Tech Dictionary, giving you the key signals and mindsets for this new era in a 5-7 minute read.
Trend Spotlight
Gemini 3 launched right before Thanksgiving and immediately trigged an intense wave of discussion across X and Threads.
I still remember that three years ago Google was the one declaring code red over the rise of ChatGPT from OpenAI. Now the roles have flipped, with reports that OpenAI has issued its own internal code red memo as Google surges ahead with Gemini 3.
Beyond the fact that Gemini 3 is topping all the obvious leaderboards and benchmarks, what really stands out is how well it's being received across very different fields.
Marc Benioff, the CEO of Salesforce, even posted that he has used ChatGPT every day for the past three years, but after just two hours with Gemini 3 he "can't go back," calling its progress astonishing.
The engineers around me are calling it the best front-end model they've used (you know, it takes a lot to earn that kind of praise from engineers.)
Most importantly, I found that posts from general users on social platforms are full of "vibe coding" examples. For the first time, people without any technical background are able to smoothly build the exact app or web solution they want to solve their own, REAL problems.
Google's moat doesn't just come from its compute power, like the TPU driven improvements in performance and cost structure, but from how seamlessly its entire ecosystem fits together.
On the TPU side, Google now treats TPU as a true full stack accelerator, powering everything from foundation model pre-training to large scale inference across the whole pipeline.
On the ecosystem side, it is not only about its own Workspace suite, but about the tight integration of models with Google Cloud. In the future users will not only be able to generate the app they want in real time, but also deploy it to the cloud with a single click and let others use it immediately, creating a new flywheel and room for entirely new business models.
Could this be the beginning of a cloud‑plus‑AI world where inference itself turns into an Airbnb‑style shared business model?
Startup Unboxed #4
This time's "unboxing" isn't actually about a startup (at least not yet). I often get first calls from brilliant engineer friends before they officially decide to start a company.
Today, this "pre‑founder" reached out for a coffee chat to learn more about what it really means to build a startup. His background is in computer vision engineering. After the company he joined was acquired by a unicorn company, he started exploring what opportunities might exist in the AI startup.
We talked through three possible directions, all grounded in his own experience. I've noticed that founders who have worked for a few years are fundamentally different from young dropout founders, and that shows up in the kinds of problems they want to solve and how they think about building product.
Younger founders (especially Gen Z) often bring ideas with a strong disruption vibe, but not always a clear direction. In contrast, founders who have worked before usually come in with several thought‑through directions that tie closely to their own skills, and they are actively looking for real market feedback and guidance.
There's no "better" or "worse" between these types; what I care about is how I can actually help them.
Of the three directions we discussed, I was especially interested in one agriculture‑related idea. He shared the story of his upbringing and how he started noticing very real, on‑the‑ground problems in traditional farming. He also had a view on how computer vision could be used to solve some of those issues.
The questions he raised also made me think about how both angels and institutional investors choose where to invest in the real world:"Isn't agriculture a sector that very few people are really backing?"
I'm not an expert in agriculture, but I am an expert in computer vision, and I know this technology can absolutely be made to work in this context (of course, there are still a few practical prerequisites on the hardware and deployment side).
Based on what I've observed over time, when a project is tightly connected to problems that show up in your own life—and that you genuinely want to solve—founders sometimes reach a level of obsession. That obsession is often what carries a founder through the hardest stretches of the journey; maybe that's what people simply call determination.
So in the end, I encouraged him not to start a project only by guessing what investors supposedly want. He should build a product and a company he, as a founder, can truly be passionate about. That, in many ways, is what people often mean when they talk about Founder‑Market Fit.
🖌 Startup Unboxed is a series where I meet startups, journal the takeaways, and share my thoughts.
ML/AI Inference
Model inference is the stage in AI or machine learning where a trained model is used to make predictions or decisions on new data it has never seen before.
It happens after training, when the model is actually put into use to do things like classify images, score risks, or detect fraud in real time or in batches. Training adjusts the model's internal parameters; inference keeps those parameters fixed and applies them repeatedly.
Consider training a new employee at a bank: during onboarding, they study many example applications and learn rules for who should be approved or declined. Once training is over, every new loan application they review is "inference". They apply what they already learned to decide yes or no on each new case. They are not rewriting the rules each time; they are simply using their learned mental model to make fast, consistent decisions on new incoming work.
Explaining to Grandma: When you recognize a friend's face, it's because over time you've trained yourself, so when you see someone across the street your brain instantly says "that's Sarah" even if you've never seen her in that hat before; in that moment, your brain is doing model inference.
🧠 Tech Dictionary helps you decode common tech terms so clearly, even your grandma would get it. Quickly find out what matters & why. So you're never lost in the tech talk.
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Disclaimer: This newsletter is for informational purposes only and does not constitute investment advice.