This is good psychology for the labs. When Buffett invested in Apple he loved citing how most people would rather give up their second car than their Iphone.

Edit: S1 states both are being leased so the 20-25B initial investment probably more relevant

This is probably less pandering to genz and more speaking their users language.

This echos the comments here about enjoying not writing boilerplate. The there is that our minds are programmed to offload work when we can and redirecting all the saved boilerplate to going even deeper on parts of the problem that benefit from original hard thinking is rare. It is much easier to get sucked into creating more boilerplate, and all the gamification of Claude code and incentives of service providers increase this.

I am not claiming it was intentional, but it certainly magnified the media attention. Maybe luck and not 4d chess.

1, to win consumer growth they have continued to benefit on hyper viral moments, lately that was was image generation in 4o, which likely was technically possible a long time before launched. 2, for enterprise workloads and large API use, they seem to have focused less lately but the pricing of 4.1 is clearly an answer to Gemini which has been winning on ultra high volume and consistency. 3, for full frontier benchmarks they pushed out 4.5 to stay SOTA and attract the best researchers. 4, on top of all they they had to, and did, quickly answer the reasoning promise and DeepSeek threat with faster and cheaper o models.

They are still winning many of these battles but history highlights how hard multi front warfare is, at least for teams of humans.

They have also come back to the Potomac and Washington DC which is nice.

Folks at Perplexity AI are doing a great job for general info AI charged browsing that's comparable to Bard. Our startup Promptloop has a web browser model targeted specifically at market research and business research. There are certainly many different ways to connect internet and model.

The principle use of the spreadsheet for both power users and casual users is a lot around justifying answers by being able to see the data. The goal is not speed or scale.

If you and sending a report to a superior - clear and visible rows, columns, and "math" makes it faster and easier to trust information for non-technical users.

This same logic is why the spreadsheet is best serving customized, one-off, or changing operations without a huge amount of data. This makes up a huge chunk of day-to-day business decisions, especially in finance where the math is much easier.

I don't think the spreadsheet lasts forever, but its successor will likely be something that presents an easy way to get answers from a set of data that is visible or at least able to be quickly understood, regardless of whether its fast, scalable, networked etc.

Non-exact outputs are actually a feature and not a bug for other use cases - but this takes a bit of use to really see.