Can you share more about who you are and what your firm does?
Apologies if this interpretation is off-base, but I don't think it's a good thing that there is a nascent cottage industry of "software dev firms" that are elbowing their way into these grants. That is another glaring warning sign that what the NSF is doing right now is grossly inconsistent with what it is _supposed_ to be doing.
But not even. At least in the domain I work in, there is virtually no interest of engaging with these NSF programs. Regardless of what's put in writing in the calls for applicants, there's still a significant prejudice that NSF - by being a part of the government - will be slow and ineffective at administering awards, and therefore it's a waste of time for any agile, fast-moving company.
On the flip-side, my academic colleagues are tearing out their hair trying to get some - any - funding to support their labs. I'm completely inundated with request from colleagues to provide an LOI or some other evidence that our company is interested in working with their lab on something. But that's even _less_ attractive for many private companies!
That would be a crappy trade; today, the public benefits multiple times over as commercialization of technology drives the innovation economy. Who cares about sharing a measly fraction of direct profits when we all get long-term growth of our investment and retirement portfolios at upwards of 10% annually?
I'm not the big Lebowski but this is the "opinion" of the majority of scientific societies in the US as well as watchdog organizations who have long followed science regulatory and spending action.
It's not exactly new. We've been through this with the crackdown on climate science during the W Bush administration, with sequestration during the early/mid 2010's, and with the budget shenanigans during the first Trump administration. Denying the painfully obvious impact of our contemporary science policy is like fiddling while Rome is burning.
Yes. Myself and colleagues used to get outreach fairly regularly to join new faculty at universities trying to establish themselves in China. I'm in industry these days, but some of my colleagues report a significant uptick in these types of outreach over the past year.
> This is a blunt instrument, yes. But things were going very poorly overall, and we needed a shake-up.
You do understand that what's happening isn't merely a "shake up", right? It's the coordinated dismantling of structural components of America's innovation economy in ways that will take decades to recover from, if it all (can't do much if emigrants take their talents to China instead of the USA).
Strong "cut off your nose to spite your face" vibes. Hope that works out for you. It's not working out for many of my colleagues, especially early-career scientists at federal labs who are hemorrhaging from the system, often moving completely out of science altogether. Great return on investment we're getting for all those GRFPs!
> Especially if they can play on their lib donor heart-strings about how mean the current administration is being to them.
Yes, like those famous liberals the Koch family who paid for prime real estate across from Stata.
It's just not as simple as you lay it out to be. Do you _seriously_ think that if hunkering down and paying out of the endowment to sustain nominal operations for a few short years was a viable strategy that they wouldn't be doing just that?
A sizable chunk of the endowment likely has legal restrictions that limit how funds can be spent. E.g., they could be earmarked for undergraduate scholarships or a specific lab at a specific department. The endowment isn't a slush fund.
It's also worth noting that the structural costs of research are far larger than what any single institution would be able to shoulder. For instance, MIT has extremely limited supercomputing resources under their own maintenance. Researchers would typically use such resources from centralized places funded by the NSF or DOE, where larger pools of money can be assembled.
And of course this doesn't even get into the reality that the annual operating costs of somewhere like MIT likely far exceeds the investment returns generated by the endowment.
You might as well argue that companies should never take venture capital - e.g. if they can't finance their growth through profits alone then they shouldn't raise any money. The whole point of grants or investment is to subsidize and incentive work which has payoffs on much longer timescales than what market dynamics can sustain alone.
It would actually be _more_ competitive, because what's driving the reduction in admissions is uncertainty in grant/funding availability.
That means fewer available slots overall. Kornbluth's comments don't explicitly state anything about _applications_, just _admissions_. Given the heightened economic uncertainty and poor job prospects for recent graduates, I'd expect more students to be looking for graduate school as a way to tide themselves over.
So a very, very bad picture for folks seeking graduate education and training.
> It's "extremely exact" but our models are not good enough. So... inexact?
That's not the common way that the phrase "inexact science" is used. All modeling involves approximations at some levels, but you wouldn't turn around and call it "inexact science."
> ... but there are still a lot of variables unaccounted for.
Such as... ?
This is the problem with throwing away colloquialisms like "inexact science." What, specifically, is a "variable" that is unaccounted for that would unlock improved forecast accuracy or to push thresholds closer to the predictability limits?
> This is why I think that ~90% accuracy for a few days in advance[1] is good enough for most people. A smartphone app won't miraculously make this better, no matter how pretty or "fun" it is.
I agree, which is why the other portions of your comment come off poorly.
> Weather forecasts have always been an inexact science
Weather forecasting is anything but "an inexact science." It's extremely exact up to the limitations and assumptions you impose on your model due to resource constraints.
And yes - I assume that this is what you mean by "an inexact science." But still in 2026 I regularly meet people who think that weather forecasting is the same as astrology, completely ignorant of massive amount of physical scientific understanding that goes into it.
> No. But I'd suspect a tabula rasa approach to weather–particularly given it hasn't been rolled out globally in one go–incorporates satellite data, local measurements, et cetera.
There most likely won't ever be such an effort - even in companies that are targeting verticalization of the "weather supply chain" (proprietary observations + models + decision support tools) - if only because it would be utterly foolish to exclude the vast amounts of data collected by government agencies and the wide variety of players in the weather enterprise. At best, verticalized weather companies can produce niche value over baseline from the single modality of proprietary data they collect.
The infrastructure for observing and forecasting the weather is incredibly sophisticated, and has been evolving for about 150 years at this point. The quality of contemporary numerical weather prediction likely doesn't leave much headroom towards the threshold of fundamental physical limitations on predictability. This is why there are groans and eye rolls from the weather community each time a new player steps forward with yet-another-AI-model-trained-on-ERA5-reanalysis and boasts some comically small improvement in average forecast skill.
With all that being said, there's likely an exciting frontier opening up as the AI models push towards encompassing data assimilation. But the applications that start to become extremely interesting there won't have any noticeable impact on average forecast quality for your typical weather app.
> Do any feel-like estimates take cloud cover into consideration?
No, usually not, because they're usually just simple toys combining a heat index and wind chill scale.
There _is_ an official metric used for estimating heat stress that accounts for cloud cover - the Wet-bulb Globe Temperature (e.g. https://www.weather.gov/tsa/wbgt). This is what is used, for instance, in literature analyzing the impact that future climate change might have on heat stress and mortality risk during heat waves. It's also used by some professional sports programs to monitor risk for crowds and athletes, as well as commonly used by OSHA and other regulatory agencies looking at worker exposure to heat hazards.
It would just be regurgitating information from numerical forecast models.
If you're in the Northeast and have questions about the significant winter storm that is impending, please check out the National Weather Service's forecast and decision support materials for your community on www.weather.gov.
And therein arises the "moral hazard" issue. There is a legitimate concern that geoengineering could abate some of the concern over climate change and lead to further delays to reduce GHG emissions. And this is a serious problem because while we might mask global temperature change with these Approaches, they don't help resolve issues like ocean acidification.
The important, missing detail that breaks down this analogy is that we don't have a reference for a long period of vulcanism while anthropogenic emissions of greenhouse gases continue.
This is where the "termination shock" issue comes in. Given current CO2 emission rates, a 50 year geoengineering strategy would mask an additional 100-125 ppm of CO2 added to the atmosphere. If the geoengineering scheme was suddenly stopped, it's not entirely obvious what the response trajectory would be of the climate system.
Apologies if this interpretation is off-base, but I don't think it's a good thing that there is a nascent cottage industry of "software dev firms" that are elbowing their way into these grants. That is another glaring warning sign that what the NSF is doing right now is grossly inconsistent with what it is _supposed_ to be doing.