tansey

Academic here. It's actually great for us. You're usually the first to see the implications for your niche area and have a chance to get a hot paper out rather than a moderately impactful one.

Somebody has to pay for the trial. Drugs are expensive and the amount needed to dose a single person is orders of magnitude more than mice. So who funds the study?

If it's the government or philanthropic fund, you have to put in a grant and show that it's competitive in terms of preliminary results etc.

If it's the drug companies, you have to deal with lots of complicated things with combination therapies. Drug companies don't like their drug paired with other drugs that aren't in their portfolio. They also need to see a way to make a profit on it, which means they need to evaluate whether this is the most likely successful trial, assess bang for buck etc.

If you want to go compassionate use, then you need to get the pharma to donate the drugs or insurance to cover it. This is spain, so I guess insurance is just the government since they have universal healthcare. I have no idea how that works but I am guessing it doesn't move fast.

PI at a cancer center here. These two ideas are not mutually exclusive. Cancer is indeed not 1 disease but many countless ones. At the same time, cancer diagnoses are based on site of origin and histology (how it looks under a microscope). But often what drives a cell of one tissue toward pathogenesis is the same mutation or other molecular malfunction as a cell of another tissue type. In those cases, we can develop drugs that target that specific component and it may work across both cancer types.

Unfortunately, there are countless ways things can go wrong in cells. There are also rarely drugs that truly span a large swath of cancer types effectively. That's because even though two cancers from different sites may have the same driver, how they respond to treatments can differ. The difference in cell state may allow one of the two cancers to adapt to the treatment, such as by activating an alternative growth pathway, whereas the other cancer type's cell of origin may not have such an easy road to therapeutic escape.

> Sugar basically doesn’t exist in nature without fiber

Honey?

Here goes nothing:

- bio startups rise, ag tech startups rise, food startups rise-- everything to do with engineering life.

- second and third tier cities rise in the US while first tier cities ebb. Places like Austin see continued growth, while cities like Baltimore and Cincinnati begin to really revitalize as local market become more important than global markets.

- mobility increases as people are less tied to their jobs/families

- Google slows on the innovation front. FB is increasingly weak as social networking just isn't as profitable anymore. Amazon keeps churning away. Netflix wins best picture at the Oscars.

- AI progress slows. The ML community fragments again. Neurips is no longer where the best ML researchers publish.

- At least one climate protest with over 5M people involved nationally, calling on Congress to act now

- China, mired in internal political upheaval, faces a lost decade. They will either no longer be one of the two biggest economies or they will be on a clear trend down but third place is a long way to fall

- twice as many people consider themselves vegetarian or vegan. Foods for this demographic have gotten much better and more diverse. Consumption of meat is still high, but the trend in 2030 will be clear: the meat industry is shrinking rapidly in the US and Canada. Other nations will lag here, meaning almost all of the innovation will happen in North America

- NYC will have built only two new subway stations

- a third political party will gain at least 5 seats in the House

- there will be a recession. Likely due to housing again. As the boomers die out, their millennial children inherit their suburban homes. Unfortunately they don't want to live in the suburbs and selling the house would pay off their student loan debt. But who is going to buy all these houses?

- political divide in the US heals, but it's not pretty and it all feels chaotic. Political parties weaken in favor of some new form of factions.

- CS undergrad enrollment declines but CS course requirements pervade nearly every STEM field. The common wisdom will now be that interdisciplinary jobs, not vanilla software engineer, are the growth sector, especially in bio/medicine/food/agriculture.

- Medicare age lowered to 50 as a transition toward single payer that will take another decade

- the world has missed its chance to avoid global warming. The schism in the debate will now be about what to do. Radical positions (open borders for mass refugees, a trillion dollar climate change R&D bill) will become more mainstream.

- Cities all over the US will be calling for federal infrastructure to build new train and tran systems, obviating the market demand for autonomous taxis. Uber and Lyft go under. Long haul autonomous vehicles are at 10% of all interstate traffic

- a common political point will be about how the US needs to stop subsidizing corn. As crops becomes more important in this decade (ag tech, rise of vegetarianism, climate change) it will be clear that corn is an over investment but political inertia will not let things change this decade.

- A dominant Canadian tech company will arise that rivals Google/FB or is at least rising rapidly

- towards the end of the decade, robotics is starting to reach the early-success stage. This will have impacts on all sectors as co-working spaces pop up with access to reprogrammable devices that can prototype commercial products. Think: Roomba for X. 2030 is still early days for this, but there's buzz, articles about robo-spaces Wired, etc.

The type of quants talked about in this article probably has a PhD in math, stats, CS (with a focus on machine learning), or something similar.

Yes. Exactly this. Building models that assess risks and potential gains. A quant is a catch-all term though, so one person may be working on models that predict some sector of the market and another person could be looking at online allocation algorithms for maximizing risk-adjusted returns.

This is why people do twin studies. Orphaned twins that were separated at birth are as close to a natural instrument as one could ever hope to get.

The problem with the idea of cheap screening tools is Bayes' theorem. If doctors go ordering this for most people since it's just a blood test, and if only 1% of people ever really have cancer when tested then the 1% false positive rate means there's only a 50/50 chance you have cancer given the test is positive.