I agree with you - there’s usually some adjustment for sampling bias, and this study says it is matching Census targets. But I had to go three levels down to see that info before I derive any meaning from the number.
My concern is that headlines like “x% of adults do y” get repeated without anyone (sometimes even journalists writing the article) seeing the methodology or nuance behind them. Context matters.
I agree it’s very possible subscriptions would become more common if targeted ads were restricted. Personally I’d rather pay money for a good product/service than 'being the product' for a bad product that monetizes my data.
Whenever I see a claim that "x% of adults do y", my brain goes:
- "x% of what? what is the denominator?". Without that number, the claim is meaningless.
- surely it cannot be the entire population, so it has to be a survey.
- how many people participated in the survey? what was the distribution?
Here is that info for this study. I found this in the PDF version of the study report [0] referred to at the end of the Northwestern page [1].
> Methodology
The Harris Poll conducted a total of 4,375 online interviews among the general U.S. adult (18+) population between January 5th and January 21st, 2026. Included in this overall total is a sample of 816 High-Net-Worth individuals (those with total household investable assets, excluding pensions, retirement plans and property, greater than $1,000,000).
Apparently it is Language Model, as mentioned in the announcement of NotebookLM in 2023 [0].
> Today we’re beginning to roll out Project Tailwind with its new name: NotebookLM, an experimental offering from Google Labs. It’s our endeavor to reimagine what notetaking software might look like if you designed it from scratch knowing that you would have a powerful language model at its core: hence the LM.
It's funny how similar that article's intro is to today's announcement.
Unlike most articles that butcher an analogy so badly that you wish they could have just described the concept plainly, this one uses the analogy really well. It carries it from start to finish without overstretching it.
This line captures the essence of the article and is going to stick with me forever:
> SaaS is the bread, not the bread machine.
And yes, SaaS companies that understand that they sell convenience and accountability will be the ones that survive this AI rush. New ones could emerge too.
Such a great read! I kept on nodding and chuckling the whole time reading it. I can see myself as the founder, especially 'spending time in the oven forums' lol.
I went to the /blog route to see other posts by the author, but alas, there is only this one! And that's a gem.
Thanks for the note.
While building the context bundle, there is a tool that detect gaps in the timeline. So when you ask 'kitchen renovation', and there are emails from 2019 and 2023, the tool flags these gaps and the LLM is made aware of it. So Memento asks which of these clusters you want to track as a project.
Later while generating the wiki, LLM handles conflicts and facts that evolved over time because it has the timestamp of the messages. So it shows that in the narrative.
Wwat we haven't implemented yet is to update the wiki when new emails arrive. That is the next one in our backlog.
We built Memento for two reasons - we were frustrated by the search in Gmail, and we wanted to organize email locally in a privacy-friendly way. But the outcome was much more than that - we learned how to build agents from scratch, optimize agentic search, and discovered many beautiful memories we didn’t know were hidden in our email.
We would love to know what you think of this project. All feedback is welcome.
Obviously the person who built and deployed the agent (the claw in this case).
If we treat this as a hard question, we risk treating AI systems as people rather than tools. This is exactly what Armin warned about in his "clanker" post last week.
As I understand, this skill is intended to understand AI-generated code and potentially reduce skill atrophy. So it asks the agent to pause after important milestones (eg: created a file, changed db schema etc ) and ask the user questions about how they would do it.
> today's best runnable-offline model is roughly 6–8 months behind today's frontier.
But it doesn't matter because frontier models were extremely good 8 months ago and we were doing real work with them. Now we have more capable open-source agents like pi and OpenCode which work well with these models.
More importantly, offline models is the best choice for privacy, on-device inference and no token/cost anxiety.