I/O 2026 and the assistant that lives in everything

Google's developer conference happened this week and the shape is consistent with where the company has been heading since 2024: a capable model, deployed everywhere they already have users, priced aggressively because they own the silicon.

the distribution advantage, compounding#

The thing no competitor can replicate is that Google can ship a capability into products that billions of people already open daily, on launch day.

That is worth more than a benchmark lead and it is becoming more visible each year. A model that is marginally better but reaches users through a signup flow loses to a model that is marginally worse and is already in the search box.

The strategic implication for everyone else — including the other frontier labs — is that raw capability is not the competition anymore. Distribution, price, and integration are.

the search question, again#

Every year this conference makes the same thing more true: informational queries are increasingly answered on the results page rather than by sending someone to a site.

For anyone who publishes on the web, the consequences are now well past theoretical:

• Referral traffic to informational content keeps falling. This is measurable and it is not recovering.
• Your documentation is being summarized by a system you do not control, and users are acting on the summary.
• The feedback loop is broken. You cannot see what people asked, what answer they got, or whether it was right.

I do not have a satisfying answer. The mitigations available to an individual project are marginal: write documentation that is hard to summarize badly, keep a machine-readable changelog, make error messages self-explanatory so they do not require a search at all.

The structural problem — that the economic model funding web content is being removed without a replacement — is not solvable by any individual publisher, and the people who could solve it have no incentive to.

the developer surface#

The genuinely useful announcements, as always, are the boring ones:

Model pricing and the cheap tier. The cost-per-capability at the small end continues falling, and Google's TPU position means they can price below what competitors renting accelerators can match. If you run high-volume inference, the arithmetic is worth redoing quarterly.

Longer context, better retention. Incremental and real. The practical question is always the degradation curve, not the maximum, and that continues to improve.

Agent tooling in the cloud. Same category everyone is building: runtimes, memory, identity, observability. Evaluate against a real problem rather than a demo.

the thing to actually do#

The recommendation has not changed in two years and I will keep repeating it because it keeps being right:

Have an eval set in your repository. Fifty examples from your real domain, with expected outputs, run against every candidate model.

Every conference like this produces a new model that is claimed to be better. With an eval harness, evaluating that claim for your workload takes an hour. Without one, it takes a week of impressions and you will get it wrong.

This is a day of setup that pays back on every model release, forever, and the number of teams that have done it remains surprisingly small.

the honest summary#

A very good model, deployed extremely well, in a company with structural advantages that are getting stronger.

Whether that is good for the web is a separate question, and I keep arriving at the same uncomfortable answer.