Plot twist: It's not actually scalable because no amount of tools and buzzwords can compensate for the lack of experience in proper architecture for scaling.
> For the uninitiated, Linear is a project management tool that feels impossibly fast. Click an issue, it opens instantly. Update a status and watch in a second browser, it updates almost as fast as the source. No loading states, no page refreshes - just instant, interactions.
How garbage the web has become for a low-latency click action being qualified as "impossibly fast". This is ridiculous.
> The moment of capturing a measurement is known as a metric event
Which suspiciously reads like a log.
In practice, a metric is an aggregate of events (the "metric events") when you're not interested in the individual event but, but in the aggregate itself. For practical reasons this is not implemented with logs but with more primitive technical events emission.
This is not fundamentally incompatible notions. If you do an electrocardiogram, you might be interested in your BPM, but it is deduced by the full log of each beat. The segregation we do in computing is more practical than fundamental.
> we form thoughts at 1,000-3,000 words per minute
I would like to know what this measures exactly.
The reason I often prefer writing to talking is because writing lets me the time to pause and think. In those cases the bottleneck is very clearly my thought process (which, at least consciously, doesn't appear to me as "words").
It _is_ equivalent to a back door, that's the point. The UK demand can be accessed more rapidly and properly by disabling the feature than by implementing a backdoor, since it is the same thing.
> So, what exactly has generative AI actually done? Where are the products?
The product is ChatGPT, actually.
If LLMs are a bubble, then you should expect most of OpenAI's revenue to come from its API (which is used by startups which have raised money to do "magic AI stuff", and the bubble would pop when investors would stop giving the money). But according to https://futuresearch.ai/openai-revenue-report, revenue from the API accounts only for 15%, the other 85% being the different subscriptions offers, including 55% of ChatGTP Plus subscriptions -- that is, _direct consumers_.
This doesn't prove that it isn't a bubble (the consumers could realize it's useless and then leave some time later), but it makes it less likely IMO.
Do they have an incentive to learn? If the reward process in the company also consider it "out of scope" (meaning the remaining work is scheduled as such), there is nothing to really learn.