its the upper bound to value. Net worth is calculated as market cap of your assets, but look at Elon Musk. Right now there’s not enough people to buy his net worth his liquidation potential is drastically lower than his net worth. Ergo it’s a lie. Some people understand that, most don’t. It’s abused and marketing in some cases.
the market cap is described in USD, but don’t get equal value of it and you definitely don’t get more.
If somebody would buy it for more, he would have already.
If somebody has a car and he gets 30_000 USD for it, that’s the value of it. If somebody wants a cart and he can get it for 30_000 USD that’s the price.
How do you plan to estimate value if you estimate it at the price somebody might pay for it in the future. It’s proofing too much.
I’m aware that buyouts happen at a premium, but I state that this is not a valid argument to conclude market cap => value. If that statement would be true. Every share would be worth infinite money, as theoretically a buyer could show up and bid for infinite money.
Nobody said market cap is > value. You seem to misinterpret basics. Buyouts happen at a premium because you can't buy all the shares at last trade or current offer or anything of that nature. You can't sell them all at last trade or current bid or anything of that nature either.
So, we are left with the last trade, or the mid point of the bid/offer, as the best proxies. It is what it is.
People in finance have largely (and correctly) agreed that the marginal buyer and seller represent the best guess for the value of the business with very few exceptions. There are various flaws with this, but you won't come up with a better method, I can guarantee it.
People in finance all profit from the same scheme.
I can come up with one right now. Instead of calculating it from the last price calculate it from some deepness in the order books. Realistically you cant drop just shares without moving the market so no company can just get 10% of shares dropped without losing a lot more in price impact. We only look at a small part of the market and the last price is practically the highest somebody on the world would pay for one more share. Nobody is buying for more. So its a upper bound not a good estimation.
This is a stupid suggestion. Go deep in the order book in which direction? There are both sell and buy orders. Last trade was both the highest someone would bid and the lowest someone would offer, by definition. Every trade is that.
The last price is also the lowest value that anybody in the world would sell one share for. Incredible that people never get this when it's the most basic thing: Every trade has somebody buying and somebody selling.
If I have 1 billion dollar in cash I can buy stuff for that 1 billion. If I have '1 billion' in stocks of one company I can not trade that 1 billion into cash. It is therefore not worth 1 billion.
If you have 1 billion in cash, you cannot buy a company where the market cap is $1 billion. You'll have to pay significantly more.
You seem to completely misunderstand that most shares are held by people who won't sell at the current price. To them - real owners who have put money to work - the current price is a floor.
And at some point you will go back and forth and realize: last trade is the best guess on true value, even if it's wrong sometimes.
> Last Price x Shares is not a perfect proxy to estimate the value of holdings it has <1 elasticity.
Nobody said it was a perfect proxy. Just the best proxy. The elasticity goes both ways - you can't sell all shares at current price, can't buy them all either.
I don’t think its the best proxy either. A proxy with elasticity <1 would be better. As again you keep arguing price to buy determines value. And I strongly disagree to me value is what I can do with it now.
When I check Google’s definition:
Market capitalization (often called market cap) is the total dollar or currency value of a publicly traded company's outstanding shares of stock.
But the dollar value is =/ the same amount in cash, as I can’t buy the same things as I can buy with cash.
What I’m not certain about is if that’s for the greater good. Put capital to work or for the greater loss, crypto hype rugpulls?
Then come up with a better proxy, that takes into account buyers and sellers and a real order book.
Again, nobody said "price to buy determines value" - the argument is that last trade (or midpoint of bid/ask) is the best proxy for value. Not a perfect one.
Of course this is the problem. It's amazing that for all the commentary around AI models the number of people who really understand even the basics approaches zero.
Talk to chatgpt about how a checking account works, and really understand what happens when you put some money in the bank, and when you transfer that money to another person at another bank, how it's accounted for, how the banks actually do that transfer.
This is a largely agreed upon problem. What isn't clear is why it seems to get sidestepped in discussion so frequently. No one knows how a molecule will interact with human biology. It can't be simulated computationally to generate data (too slow), there isn't a lot of data in the grand scheme of things, so you can't train a model to actually predict the interaction of a molecule with a human.
And nothing I've said here is an original thought or piece of insight.
One might argue that we have calibrated drug safety to such a high degree that the aggregate negative social impacts are far higher than the costs of side effects. Of course we don’t want another thalidomide catastrophe but I think there are large potential test groups to mine who are much less sensitive to the risks. For example, the elderly and those with terminal diagnoses. Right now we only permit drug testing in very limited cases for the latter group, and only when the treatment is related to their terminal diagnosis. I’m seeing older people taking all kinds of experimental anti-aging drugs right now, either off label, or novel. We’re not collecting any of this data empirically because what they’re doing is not legal.
Further, [we are getting much better at simulating drug interactions.](https://pubmed.ncbi.nlm.nih.gov/41155903/) I strongly suspect AI will offer new probabilistic models to funnel novel drugs into more targeted trials which are much more likely to succeed. If we had 90% certainty of success and high certainty of lower adverse reactions I think we could allow much less stringent human trials. That means much lower cost and much faster turn-around.
My wife is a doctorate in pharmacology, I made exactly this argument to her but she dismantled it pretty quickly.
a) We do test on terminally ill people, but after testing in other models.
b) Thalidomide was all roses compared to the horrors you can inflict.
c) Profit-driven industry makes extremely perverse incentives.
Still I do come down more on your side of the argument, and we have historically made massive leaps doing some horrific things.
I wonder if we can get better models based off of human cell-lines / tissues? Like CRISPR did?
> b) Thalidomide was all roses compared to the horrors you can inflict.
Just to expand on this for the audience. Even a perfectly reasonable synthesis of MPPP, a synthetic opioid, can produce MPTP[1], which will destroy all of your dopaminergic neurons and give you permanent brain damage. This was discovered entirely by accident. Or, Karen Wetterhahn, who got a single drop of organic mercury on her gloved hand and spent the next year suffering from her brain rotting from the inside out.
This only barely scratches the surface from what I can remember, but there's absolutely, unambiguously a lot of good reasons why these barriers are there. These are rules that have been put down through blood and tears.
> Profit-driven industry makes extremely perverse incentives
People who are specialists in something other than economics often make this argument but it's often a very weak argument, IMO. Liability means that you are incentivized to be quite cautious in pushing a potentially dangerous product. Particularly with pharmaceuticals a class action can be easily and successfully argued in court. Moreover, a relatively small number of dangerous flops will tank your reputation with consumers. I would also add that you can't make any more money selling drugs to people you have killed.
The worst money incentive for drug manufacturers is that market forces push them towards recurring treatments for diseases that effect a large number of people. However that seems less than perverse to me.
> I would also add that you can't make any more money selling drugs to people you have killed.
That assumes that people are playing a long game. If you're looking for your payday and exit, and there are many people out there who are only looking for the next payday, this argument falls apart.
> Moreover, a relatively small number of dangerous flops will tank your reputation with consumers.
One success the anti-vax people have achieved is to muddy the waters around this. Your public reputation is no longer correlated to actual performance.
> Liability means that you are incentivized to be quite cautious in pushing a potentially dangerous product
There's a long list of people throughout history who do not have this caution at all.
You seem to be taking a theoretical economics approach. Reality is different. People are not perfectly rational actors, and consequences are not consistent.
The antivax people are still fringe and by and large people trust that the drugs they take are safe and effective.
Payday and exit is pretty hard in biotech as you need to conjure up billions of dollars in capital, which doesn't often flow to people who payday-and-exit.
What happens in reality is that people try really hard to make safe and effective drugs, they mostly fail, and the ones that are approved are generally safe and effective.
What are you talking about? Shareholders are looking for sustainable projections of growth. Killing a bunch of people will tank your stock which is how executives get paid. You really need to think beyond their first thought o nstuff like this.
> There's a long list of people throughout history who do not have this caution at all.
None of them run pharmaceutical companies.
>You seem to be taking a theoretical economics approach. Reality is different
No I'm describing reality and you're arguing from populism.
PE is short for PRIVATE equity, there are no public shareholders in PE, no share price. When I critique that people here often don't understand basic things about the economy this is what I mean.
When it comes to PE there are myriad examples of shareholders taking a short term view. So are you suggesting then that shareholders of publicly traded companies are somehow different? I think that would be on you to justify but you've instead chosen to attempt to mock me for wrongfully assumed ignorance.
> Thalidomide was all roses compared to the horrors you can inflict
People frequently don't realize that thalidomide is a very good chemotherapy drug that's back on the market with the appropriate category X designation.
It treats multiple myeloma, graft-vs-host disease, various HIV associated disease and Kaposi's sarcoma, Leprosy and potentially TB.
d) elderly and infirm people are in a weakened state and so are less likely to recover from accidental complications your drug might introduce
e) sickness is itself a confounder for the toxicology effect of a drug. Suppose you take a drug and your liver shuts down - did the drug cause it or was it your underlying illness? This can lead to programs being prematurely shut down and actually prevent the development of treatments that might have gone on to succeed.
Testing a drug in a terminally ill person for something other than their illness is not going to produce meaningful data.
You misunderstand how hard it is to pull signal from a drug being put into a human body, which is why we need large numbers of people who meet extremely narrow criteria (and don't have things like a terminal cancer).
Your latter idea is a good one, and hopefully something AI will help with. As you imply: we will know empirically when it's ready because trial failure rates will trend toward zero. Today we're closer to 90% failures and not even clearly trending in the correct direction.
You're aware that, as a healthy volunteer, you could go sign up for a Phase 1 study today, right? You can even earn a pretty penny doing so.
It's quite a leap to go from the study you linked to thinking AI will create models that will be useful for new trials. The big leaps in AI have required enormous amounts of data. There is no such analogous data sets in biology and no foreseeable way to get it.
It really is hard to see AI improving the trial process meaningfully. We are more likely to see progress with better trial design and regulatory regimes.
You see, the only reason our society’s systems aren’t perfectly efficient, logical, and smooth, is because a software developer hadn’t briefly mulled over their challenges with a few casual thought experiments.
They use more sophisticated domides based off Thalidomide these days (2nd gen Lenalidomide, 3rd gen Pomalidomide, and next gen Mezidomide is in trials).
But yes, it was very effective as a treatment. When it was approved for cancer use, we actually had no idea how it worked, only that it worked really well. Thalidomide has saved a few orders of magnitude more lives than it took/damaged.
its pretty obvious why that gets sidestepped isnt it? There are multiple companies trying to go public with trillion dollar valuations on the premise that AI will change everything, it would be mad if they weren't spending hundreds of millions to billions in shaping public opinion that everything can be solved with AI.
Anthropic and others already have "AI-powered wet labs". Not sure to what extent they are able to automate lab operations. I recall seeing a post about specifically running automated in-vitro experiments controlled by agents, though. I don't think it's being sidestepped, I think it is being handled. Or at least they are trying; maybe the labs operate at a "Theranos-spraying-blood-everywhere" level.
> No one knows how a molecule will interact with human biology. It can't be simulated computationally to generate data (too slow),
Prior to AlphaFold that was the state of predicting protein structures. That model seemingly figured out the "language" used by biology for making protein structures from DNA. It's not unreasonable to believe there's a good chance something similar exists for protein interactions.
Even if it was coarse grained but could eliminate drugs with high likelihood of unfavorable reactions it'd still be valuable. Still not sure LLMs would be ideal for that work.
Alphafold solved a waayyy narrower problem that had lots of experimental structural data. Guessing how a drug interacts with an entire human is a whole different scale of thing. Even knowing protein interactions wouldn't tell you how those interactions propagate through an entire human system or whether a drug will be safe and/or effective.
If you phrase it in that way it seems intractable.
However the majority of a protein’s structure is functionally inactive. Only a relatively small region tends to be active and affect rate kinetics of various chemical reactions. Alternatively similar interactions affect the mechanical properties of other proteins.
We also know many of the pathways for most key biological processes. Of course we’re no where near simulating an entire human, however we could feasibly filter drug candidate drugs for strong affects on key biological pathways.
That’s not too different in a broad since than using surrogates like mice to test for toxicity across highly conserved pathways in metabolism, reproduction, liver function, etc. It’s a much more tractable problem as well.
We have a lot of experimental data for the effect of many proteins on important chemical pathways. Presumably drug companies have more information on how many drug candidates react with or interfere with key pathways.
My conjecture would be that we wouldn’t need fine grained interactions but that even coarse grained filtering could be valuable. As in sure drug XYZ is great for binding to serotonin but it also has high affinity to key stomach proteins. That could avoid expensive studies just to find out a promising anti-depression drug causes severe gastrointestinal issues in a lot of patients.
Even better if we could use genome variations to protein structure variations which could help differentiate between subsets of patients with or without severe side effects.
Phase 1 isn't the big problem. It's phase 2 and phase 3 where drugs fail hard and the trials cost the most and take the longest.
Honestly once you go through all the details... drug discovery feels like the one of the overhyped and unlikely to work areas for AI. Other areas of medicine (like diagnosis) are already seeing pretty good results, and anecdotally seems to work well and tends to fit within the realm of text analysis where most of the AI gains have happened.
True being limited to phase 1 would be the bigger limitation.
Another bigger impact might come by making regular researchers, lab workers, etc all more efficient by making data analysis more efficient or creating better tools. Possibly with less errors. That'd of course require big pharma to adopt them. We'll see on that point.
It's easy and fun for volunteers to put in the effort once. It's doing the work on an ongoing basis that is less fun and less motivating. The ongoing work --- done without modern tools --- is already that office's responsibility, so this should be too.
I took lazy to mean "waiting for the gov rather than doing it ourselves". I'm saying that may be true for the initial catch-up but it's not true for the work going forward.
Lol, it's a bad deal huh. Tell that to the guys who bought the IPO of Facebook, Google, Amazon, MSFT, Nvidia.
Just because something might be not great in aggregate, it doesn't mean any single IPO is a bad idea. Some people have insight into the future of a specific industry/business. If you can pick one, good for you.
People said similar things about facebook, and similar things about Nvidia stock recently (not ipo, but similar "why buy at this price"). Global GDP is over $100 trillion every year, there is a lot of business to do in the world, AI might be able to do a lot, it might be very profitable.
The number might be too high, but doesn't take more than a little imagination and some basic facts about the world to see how it could be worth 20 trillion in 10 years.
Under examined? No. This is a very common talking point.
A glut of models doesn't mean there is no moat. There are several dimensions on which to compete, and it might turn out that being able to produce models that optimize on different dimensions is a difficult and valuable thing to do. It also might not, but the "there is obviously no moat" only comes from people who don't use these things systematically.
How does one talk so confidently about something they know nothing about?
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