Series hybrids are great for packaging, though. Parallel and series-parallel commit to certain packaging decisions like having a transmission, or a long, monolithic unit, because it's the mechanical coupling that buys them smaller motors and potentially better efficiency. Series hybrids don't care about any of that, so even though you have bigger motors and potentially higher losses, you have more freedom over where things go.

Personally, I think there's a massive untapped market in converting old cars to hybrid engines. You wouldn't try to upgrade the old engine, you'd design a smaller and more power dense package and rip all of the original gear out. Because electrification lets you cut the size of the engine down so aggressively, this is probably a feasible strategy. As you pointed out, series hybrids are probably best suited to this because of their packaging flexibility. As others have pointed out, there's tremendous potential there for replicating original driving characteristics using software and the electric motor. And if we're being honest, off-road vehicles probably should get rid of the transmission and low range, because electric motor torque is just better. As is, the potential for cars is enormous, but we're getting the worst possible outcomes thanks to legislation.

And yes, I'm aware that that statement might seem to fly in the face of much of the past two years of industry development, where specialized models have been in vogue. I think they'll settle to being appropriate for low cost "good enough" applications, but I'm less convinced they'll have anywhere near the fidelity of larger frontier models.

Yeah, they aren't perfect or always necessarily the best in a given area, but to compare them to IBM is probably missing the forest for the trees.

Off-policy versus on-policy refers to what data the model is trained on. On-policy training is where the training data is collected by the policy. Off-policy training is where the data was collected by a different sampling process (e.g. we have a standard dataset that we're going to use for supervised training).

There's also an additional economic concern that rarely gets mentioned: because no one has cracked continual learning, keeping models up-to-date and filling in gaps in performance requires retraining on an ever growing dataset. Granted, you aren't starting from scratch each time, but the scaling required just to stay relevant looks daunting.

I don't know where any this goes on a societal level, but I've believed since the release of deepseek r1 that access to frontier models would eventually be locked up behind contracts, since the only moats protecting the models themselves are purely artificial. It remains to be seen how effective China is at pushing the envelope, and whether they are interested in providing unfettered access. And on top of that, it remains to be seen how well these models actually turn out to scale in the long run.

In my grandfather's day, one income was enough to support a household, and there was less free work being done on the job, which meant fewer hours and being less drained at the end of the day. And yes, people spent less time commuting, meaning they had more time and energy for socializing after work. But communities were also more decentralized, and population centers had fewer people in general. A big part of the problem is that modern cities can be massive, and invariably funnel people to a handful of work districts, which just doesn't scale. When you double the distance to the CBD, you quadruple the number of people coming in (give or take, it's not exact because we tend to increase density close to the CBD as a response to this). Take it from someone who's lived in a place where cars aren't really necessary, the logistics of urbanization are still a crap experience when you're crammed into a train carriage during rush hour. It's common for people to commute for 90 minutes on public transport in Asian megacities, for example.

The idea that cars needed all this infrastructure that other alternatives didn't just doesn't match the reality of the history of the automobile. And yes, we've leaned on those advantages in the century since, which has also created vast areas where a car is necessary to participate in society, but we only did so because the advantages and utility were so undeniable.

It should probably also be pointed out that doing nothing has a cost too, and it's probable that the bill for doing nothing over a long period of time has come due. I, like most people, never bought the WMD claims leading up to Iraq. I'm not sure what to think here. I certainly don't buy that Iran wasn't working towards getting the bomb after how well it worked out for North Korea. I can't claim to know the calculus involved in determining whether or not it's worth going to war with Iran to stop them from getting the bomb.

It's not just dirty talk. It's a whole new paradigm in verbal filth.

On the topic of sora, though: current models are astounding. I watched a clip of Leonidas, Aragorn, William Wallace, Gandalf etc. all casually riding into a generic medieval town together, and if you showed that to me a few years ago, it would have seemed like magic. We're not far off from concerts featuring only dead artists, and all video and image testimony becoming unreliable. Maybe Sora was a victim of timing or mismanagement, because I don't see how this isn't still a seismic shift in the entertainment industry.

I suppose the other side of it is that if you add what the model has figured out to the training set, it will always know it.

As for advances where there is a hypothesis, it rests on the shoulders of those who've come before. You know from observations that putting carbon in iron makes it stronger, and then someone else comes along with a theory of atoms and molecules. You might apply that to figuring out why steel is stronger than iron, and your student takes that and invents a new superalloy with improvements to your model. Remixing is a fundamental part of innovation, because it often teaches you something new. We aren't just alchemying things out of nothing.