In the last two Chrome releases, Google fixed 1,072 security bugs. That’s more than the previous 23 milestones combined. AI wrote most of the fixes.
That chart is what software productivity gains actually look like when AI moves from experiment to pipeline. Two years of flat patch rates, then a spike that dwarfs everything before.
Nobody wants to be a model selector.
Most people do not want to decide whether a task deserves Fable, Sol, Opus, Sonnet or Haiku. They want the job done properly, quickly and without the invoice arriving as a small technical mystery.
That is where Token Blending comes in.
The idea is straightforward. Start an agentic task with the biggest, smartest and most expensive model. Let it understand the problem, make the difficult calls and create the plan. Then hand the defined work to a cheaper, faster model to execute.
The same code file now costs 30% more to process than it did three months ago. The price per token didn’t change. The tokenizer did.
Anthropic’s post-4.6 models - Sonnet 5, Opus 4.8, Fable 5 - use a new tokenizer that cuts text into more pieces than the 4.6 generation did, and you pay per piece. A detailed analysis by Playcode, who measured every frontier tokenizer on identical files, found the new Anthropic tokenizer produces 1.36 to 1.73 times GPT’s token count on the same content. TypeScript is the worst case at 1.73x.
To prepare minutes I recorded a not-for-profit board meeting this week - ninety minutes, soft audio, everything sensitive - and let Google’s new Eloquent convert it locally. Nothing touched a cloud server. Eleven thousand words, on-device, done.
The original Voice Memos recording on my iPhone was quite soft. Board meetings are what they are. But Eloquent handled it better than I expected - the transcript came back clean, the filler words and false starts were stripped out, and I had a working draft of the minutes within a short time. Further refinement with GPT-5.5 and the final document was done. (Yes I am flirting with other models. Anthropic token inflation is becoming an issue - a topic for another day).
Text to speech (TTS) is a brilliant service for those who would otherwise be literally speechless. Lost Voice Guy on Britain’s Got Talent rather humorously demonstrated the challenge.
But TTS has broad use. DIY personal podcasts. Read some text when on a walk or in the car. Share a personal update with someone who is more of a listener than a reader.
The good news is you don’t need to pay for a service like ElevenLabs to get quite good TTS. At least not on a Mac. (Don’t ask me about Windows — that’s for someone else to untangle).
I read Austin Henley’s post Automating my job away on a Friday night and did the laziest possible thing with it.
Henley’s whole piece builds to one prompt. A friend who runs a startup tells his team don’t do anything three times - if a task comes round more than twice, automate it. Henley took that to its logical end and pointed his coding agent at its own history.
So I copied his idea, swapped “Copilot” for “Cowork”, and pasted this in:
Twelve months ago the most capable model you could rent was Claude Opus 4, at $15 in and $75 out per million tokens. Today the flagship Opus costs a third of that: $5 in, $25 out. So the price of frontier AI collapsed over the year, right?
Not even slightly. It went up. You just have to know where to look, and who is paying.
Last time it was the cables lying to you. You binned the mystery leads, bought the certified ones with the speed and watts printed on the side, labelled the survivors. Good. You fixed the drawer.
Now look at the laptop itself, because it is about to play the same trick on you. Two ports, same oval socket, same confident silver moulding. One is fast. One is not. And nothing on the outside tells you which is which.
Open the drawer where you keep your cables. Go on. Somewhere in that tangle are two USB-C cables that look identical, came in similar boxes and feel the same in your hand. One will charge your laptop at full speed and drive a 4K monitor. The other can barely run a mouse.
The connector tells you nothing. That is the whole problem.
The work I’ve been doing between Claude Cowork and Excel lately has been considerably more involved than what follows. But this example makes the pattern clear - and it captures exactly why the combination saves so much time and frustration.
I had a list of 20 ASX stocks in an Excel file. Two columns: ticker code and company name. I wanted two more: market cap and 12-month return, pulled from most recent data.