Google’s Pragmatic Pivot?

There is a lot of commentary online about Google falling behind in the AI race, especially considering that its long-promised 3.5 Pro never came. Since then, they’ve launched a couple of new models in the Flash series. While 3.5 and 3.6 were criticized heavily, Gemini 3.7 Flash seems to be getting some favorable reviews—at least going by the vibes on Twitter more than anything else.

Personally, I’ve never used Google models for coding all that much in my vibe coding experiments. My usage has been very limited because they more or less suck for someone like me who’s not a coder. Tools like Claude Code and Cursor often just get what you want with some poking and prodding. Gemini, on the other hand, is just really bad on most things when it comes to coding.

That being said, Gemini still has a lot going for it on vision tasks. I’ve found it to be the best and most reliable model for historical archival projects—like my projects digitizing old books at akshara.ink and collections of letters at Paper Lanterns—and even for modern documents. Even the cheapest models like Gemini 3 Flash Preview and Gemini 3.1 Flash-Lite are really good at this, and they’re very cost-effective. While there is some chatter online about the latest Flash series models regressing a bit on vision tasks, they still perform really well.

There has also been a lot of talk that Google seems to have ceded the race to build the latest frontier models, and instead is focusing on areas where it can actually add value—especially things like document processing in the enterprise that rely heavily on vision.

Today, they also launched the Gemini 3.5 Transcribe model. I put it to the test, and it’s quite effective. Voice has become my primary mode of writing and typing; I’ve built myself several voice-first productivity utilities, including a personal journaling app. For a long time, Gemini 3.1 Flash-Lite has been my go-to workhorse model for that. Now that I’m playing around with 3.5 Transcribe, it seems pretty good, and I’m planning to switch it across all my utilities.

At the same time, I saw two more things that make me lean further into this theory: that Google has decided not to play the game of building the biggest model, but rather focus on niches where it can monetize quickly with cheap workhorse models that are easy to adopt.

First was a tweet about GlucoFM, a foundation model for continuous glucose monitoring:

GlucoFM (Continuous Glucose Monitoring Foundation Model) A lightweight, self-supervised model developed by Google Research for continuous glucose monitoring (CGM). Instead of treating glucose data as a single stream, it uses a dual-stream architecture that separates slow background metabolic baselines from short-term transient spikes (like meals, exercise, or sensor noise). This allows it to predict downstream metabolic outcomes—including diabetes risk, insulin resistance, and beta-cell dysfunction—without requiring large-scale, heavily labeled datasets.

Then I saw a post on LinkedIn about Google DeepMind rolling out two models for Indian agriculture:

Agricultural Foundation Models (AnthroKrishi / India & Global Deployment) Two landscape-scale AI models built by Google’s AnthroKrishi team to turn remote sensing and satellite data into farm-level digital public infrastructure:

• Agricultural Landscape Understanding (ALU): Automatically maps individual field boundaries, farm sizes, and water bodies from satellite imagery (now integrated directly as a layer in Google Earth).

• Agricultural Monitoring & Event Detection (AMED): Tracks in-season crop activity, sowing/harvest timelines, and health markers across 15-day refresh cycles to power credit scoring, crop insurance, and state-level farm advisories (like Telangana’s ADeX and Karnataka’s water management).

So it seems credible that Gemini has decided not to play the game of burning billions just to win the frontier model war, and instead is focusing on areas where it has built deep, practical capabilities. Or at least, that’s my reading as an amateur observer of what AI companies are up to.