Budgeting in Effective Altruism

This post is a slightly cleaned-up version of an email conversation I had with the brilliant and friendly Kelsey Piper, after reading her blog post last July on budgeting as an EA. Since it was originally an email discussion, some parts might be vague or unclear, for which I apologize in advance. Published with permission.

In summary: Earning-to-give breaks down into #1, having a large income; #2, giving a large proportion of that income; and #3, choosing an effective and underfunded place to give to. The current connotations of ETG emphasize #2 > #1 > #3, but the order of problem difficulty is #3 > #1 > #2. A large portion of the US, especially wealthy people, already has #2 down (US charitable giving is ~2.5% of GDP). But >99.9% fail at #3 – either only donating to non-weird things that rapidly become overfunded, or donating to scams/cults/pseudoscience and other ripoffs.

Me: Hi Kelsey, I just read your Tumblr post on budgeting. I mostly agree with what you’ve said, but I’m pretty sure it’s counterproductive to talk about going on a budget for purposes of donating – there’s already huge piles of money sitting around unused everywhere, that nobody really knows what to do with. That may sound weird, but let me give some examples of people in our social network.

As you’ve probably heard, Dustin Moskovitz is worth about $9 billion, and he plans to donate the large majority of that to charity through Good Ventures/Open Philanthropy Project. Private foundations are legally required to donate 5% of their assets each year, so for an $8 billion endowment that would be annual spending of ~$400 million or more. It would take ~20,000 people getting high-paid jobs in Silicon Valley, and donating 10% of their income a year, every year, for the rest of their lives, to equal one Dustin Moskovitz. And there’s every reason to think that Dustin isn’t a one-off special case. At least 137 billionaires have signed Bill Gates’s pledge to donate half their wealth, there are several other billionaires that people within EA are already discussing philanthropy with, and the EA idea is in the middle of a big publicity/coolness boom that shows no signs of slowing down.

But even forgetting about the ultra-wealthy…. last week, I was talking to one of my friends at Google, whom I’ve known since he started working there some years ago. Within the company, he isn’t especially important or famous or anything; he just has a normal Google job with a normal Google salary. He mentioned, to my surprise, that since he started working he hadn’t bothered to cash in any of his Google stock, which by now must be worth half a million dollars or more. He has a family, he lives in the Bay Area, and as far as I know he isn’t especially frugal; he just never had a good enough reason to spend any of it. After you reach a certain point, there just isn’t that much to spend more money on.

And even within the realm of spending less to donate more… doing some rough math, there are at least half a million people in the Bay Area who own houses that are worth over $1,000 per square foot. If one of them sold their house, and moved into a new house that was just 100 square feet smaller – a barely noticeable change – they’d have over $100,000 to donate. That’s the same donation size as someone making the US median income of ~$40,000 taking the 10% giving pledge, and then sticking to it every year for the next two and a half decades. (Of course, you wouldn’t literally switch houses for 100 square feet because of transaction costs, but just trying to illustrate the general point.)

In spite of all that, I do think there are cases where donating makes sense even if you aren’t that wealthy. In particular, if what you donate to is so strange or so new or so unpopular that virtually nobody else would be willing to fund it, then donating is likely a reasonable idea (and I have donated several times on that basis). But overall, it seems likely that given the ginormous overall wealth of the Bay Area, for someone who has any use for marginal dollars beyond buying luxuries that they don’t care much about, budgeting to give more is penny-wise and pound-foolish.

Kelsey: I think that people who make significant lifestyle changes consonant with their identity as an EA are likelier to get the right answers to hard effectiveness questions (giving a painful amount of money away made me value rationality much more and in a much more direct, immediate, pressing way; there’s various evidence that people are less biased when there’s money on the line). I don’t want people who don’t give identifying as EAs. I think it turns the movement into virtue-signaling faster than almost anything else could. If I were in charge I’d set an actual “if you don’t give at least this much you’re not an effective altruist” threshold because I really do think that our most likely failure mode is becoming a movement as meaningless as the “green!” label on food, and an expectation of giving (from everyone middle-class and up) prevents that.

Me: I largely agree, but I think the situation is more complicated than the world-model implied by what you’re saying.

Forgive me for the silly metaphor, but say there’s a big asteroid hurtling towards the Earth. (I’m using this as a metaphor for x-risk, but also for “ordinary” bad things like poverty, disease, aging, and so on.) We need to build lots and lots and lots of nukes to blow up the asteroid before it hits us. There are basically two components to a simple nuclear weapon: there’s the highly enriched uranium (HEU), and then there’s a casing with explosive charges, which collides two pieces of HEU against each other at high speed. Making the casing is far from trivial, but a competent team of electrical and mechanical engineers can probably figure it out, at least well enough. On the other hand, making the HEU is enormously difficult. Even national governments with huge laboratories and thousands of scientists and multi-billion-dollar budgets often fall flat on their faces. Metaphorically, the casing represents raising money for improving the world, and the HEU represents the ability to convert money into utilons with reasonably high efficiency.

In this metaphor, GiveWell’s original model corresponds to a group that hires two teams: one of them starts work on making casings, and the other goes around to all the world’s nuclear research labs, calls them up, and asks if they have any HEU just lying around which they aren’t using for anything. On the one hand, this is certainly a good idea. On the other, it’s not really tackling the hard part of the problem; you’re just piggybacking off other people’s existing solutions. I’m hugely impressed by how Holden recognized this and took OPP in a different direction, and how they’re now tackling the hard part of the problem head-on (at least, as head-on as anyone else has).

In the metaphor, you seem to be saying that the project to stop the asteroid should be composed 100% of experts on casings, who are actively helping to manufacture them. That’s certainly better than a project which does nothing, which (as you said) is the default failure mode. But it puts emphasis, and therefore things like social status awards, on the half of the problem that’s by far the easiest to solve. It’s also (breaking the metaphor) dangerously close in memespace to making the movement about showing off self-sacrifice, which is a failure mode that humans are probably evolutionarily adapted to fall into. I think this is why people keep suggesting things like giving blood, donating kidneys, and so on despite them not being plausibly effective.

The flip side is that, unfortunately, I think you’re right when you say that not having a donate-to-enter threshold makes it easier for the movement to degenerate into meaninglessness. It’s easy to judge whether someone is donating, and then not award status to people who don’t; no one really knows how to award status to [figuring out how to turn money into utilons in a non-domain-specific way] in a way that’s resistant to cheating. But I also think that, if we’re aiming to solve a significant fraction of the world’s major problems, we should kinda expect to have to tackle murky, difficult problems that nobody really knows how to handle yet.

Kelsey: Hmm. I hadn’t thought about that before (finding ways to convert money into utilons being the Hard Problem). I guess because I’ve always sort of thought of the economy as being a fairly efficient money-to-utilons machine. But I agree that we need more people doing research about what is effective; maybe they should all be people like Holden, who first earned a lot of money he wanted to give away and then pivoted to figuring out how to give it away? This admittedly involves wasting person-years of work doing something that, in your model, is mostly signaling. But I don’t think it’s totally signaling, the research isn’t actually a limiting reagent on the good money can do, just a multiplier – and it might actually also involve learning skills that make one a better researcher, too. Maybe we should point people towards careers that involve making high-stakes decisions with tight feedback loops, to hone the skills we eventually want them to use on figuring out multipliers.

And suffering is an attractive failure mode because it’s costly signaling of commitment, and you can’t actually do without costly signaling of commitment, if commitment is important. You can at least demand that the costly signaling not compromise future ability to do good? I hope? If someone donated a kidney I’d trust them more with my money (well, with the lives of currently existing humans). I wonder if that emotion is justified.

Me: I think “the economy” is mostly just a bad category – it takes a huge number of dissimilar things and throws them together in the same box, to the point where measurements of “the economy” (GDP, unemployment, inflation, etc.) are at best rough guesses and at worst outright lies. Economics contains a fair amount of useful knowledge within it, but IMO it really needs an overhaul to about half of its ontology. This isn’t really that surprising, for a science at such an early stage – you could think of it like, say, chemistry in the 17th century. There are lots of observations and rules and procedures that basically work, but there are still central concepts like “transmutation” that need to be thrown out, and other ones like “valence electron” that haven’t been discovered yet. (Not that I know how to do that – I have guesses, of course, but this’ll be a major decades-long project just like the invention of modern chemistry was.)

I think a better metaphor is to see the world as a collection of machines. A “machine” isn’t a literal mechanical device, but a collection of devices, procedures, memes, writings, traditions, institutions, Schelling points, and so on that operate together to reliably produce certain results. Some machines work well; others work surprisingly badly; and a great many simply fail to exist or haven’t been invented yet. You could say that entrepreneurship, in a broad sense, is the creation of a new machine; FDR and Florence Nightingale were entrepreneurs by that definition. Machines can also be destroyed, and of course they constantly evolve in response to the forces around them.

The way you produce happy lives for a large number of people – a larger number than you could help directly with your own muscles – is to build a set of machines that, taken as a whole, reliably give people what they want. (What exactly they do want is a whole other complex topic, and a central question to eg. MIRI’s FAI theory. But for now, we can just say that eg. no one ever wants to get infected with malaria.) In some cases, these machines already exist, and you can freely make use of them when setting up your own stuff. Eg. if your plan is to help people by setting up a gold-mining operation in Kenya, there already exists a very efficient machine to buy, sell, transport, refine, distribute, and price gold that you can take advantage of. You can more-or-less just bring big sacks of gold dust to downtown Nairobi, and hand them off there – you can trust that someone else will take care of utilizing them in the most efficient known way. However, this machine only exists because of a number of background conditions:

– fungibility: one ounce of gold is the same as any other ounce
– perfect information: it’s easy to tell if a bar is made of gold or not
– cheap shipping and distribution: the cost of transporting and distributing an ounce of gold is far less than the gold itself
– practical contract enforcement: there exist organizations which would be meaningfully punished if they just stole all your gold, so they don’t do so
– (a bunch of others I won’t get into)

By contrast, if tomorrow you discovered a cure for cancer, by itself that would be more-or-less useless. There’s no machine for evaluating and pricing and manufacturing and distributing cancer cures. You’d have to build one yourself, and that’s a huge amount of work and requires lots of different skills – dealing with bureaucracies, hiring and managing employees, raising funding, conducting human trials, and on and on and on. If you don’t happen to have those skills, then people will keep dying of cancer. (One example I have personal familiarity with is Dr. Eric Lagasse’s work on liver regeneration – we tried to build a machine for distributing this to patients, and fell flat on our faces, despite being IMO smart and capable in other domains.)

There isn’t any limit on how powerful a machine can be – the easiest historical example is Gutenberg’s printing press, the important part of which wasn’t really a “press” so much as a new set of techniques for making and using metallic type. On the other hand, trying to build an arbitrarily powerful one faces two fundamental constraints. The first is that, to be very powerful, it has to be fundamentally dissimilar from anything that many other people are trying to do. If it were similar to ones that tons of other people were already building, eg. how to make a better lithium-ion battery, odds are someone else would have built it already. The second constraint is that the vast majority of really original ideas are terrible; if you just naively disregard existing constraints, then you’ll probably fail, because reversed stupidity is not intelligence. (Paul Graham and Peter Thiel talk about this at length in Startup Ideas and “Zero to One“, respectively, though it’s a counterintuitive enough idea that you have to sort of see it from many angles to understand it well, kinda like the proverbial elephant with the blind men.) So to succeed, you have to know something that other people don’t; to do that, you have to know how to recognize which things you don’t know; and knowing how to recognize which things you don’t know is just really really hard. Eliezer’s Sequences are the best attempt I’ve seen so far to teach it (Artificial Addition is one particularly good example), and I like to think I’m pretty smart, and even so I don’t think I really understood it until having read them three or four times over about six years.

In keeping with the analogy, any given machine, once built, usually only works within a given set of operating parameters. You can make your car put out 100 kW instead of 50 kW by pressing the gas harder, but you’ll never make it produce 10,000 kW, because it’s designed to top out at 200 kW or thereabouts. Similarly, any given charity or type of charity can only handle so much money before it clunks out. And charities (or any other machine) that can operate productively under a load of even one percent as much money as the developed world has – tens or hundreds of billions a year – are more-or-less nonexistent because of various scaling issues. You’ve probably read that humans are evolutionarily adapted to work in small groups, from a handful up to 100 or so; the further you go beyond that, the more you’re stretching the cognitive abilities of the poor saps who have to run the thing beyond their natural design limits. One of the very few well-understood ways around this is to avoid tackling the scaling problem yourself, by just redistributing the money to others in some simple, well-defined way. But precisely because this is one of a very few well-known ways around a critical bottleneck, it’s one that’s extremely popular, and you’d therefore need a huge amount of resources to substantially add to what’s already being done (IIRC, even ignoring existing aid altogether, there’s already over $300 billion per year in direct remittances to the very poor from friends and family).

Hence, under this framework, the two largest ways to contribute at the margin are:

– to build a new machine where the type of machine is relatively well-understood, and the bottleneck is that the existing machines can’t scale well and the type of labor required to build new ones is scarce; this covers both creating new charities to address tropical diseases, and most “ordinary” software entrepreneurship, as well as many other things
– to build a new machine where the type of machine isn’t well-understood, and the bottleneck is the skill and background knowledge to have the required insights into what blanks need filling in; Eliezer is one example of someone we know who’s AFAICT succeeded at this, but successes here are necessarily much rarer than in the first category

By “build”, what I really mean is “contribute to building in a relatively non-replaceable way”; there are usually many different types of skills required, hence many opportunities to contribute. And it’s certainly true that one opportunity is “provide the initial rounds of funding”. However, in order for your financial contribution to be non-replaceable, you yourself must have the same types of unusual cognitive abilities as the people running the organization – the ones that make them able to succeed when most others couldn’t. If you yourself only have ordinary-programmer cognitive abilities, and not (for example) figure-out-which-organizations-aren’t-likely-to-get-torn-apart-by-internal-conflict abilities, then on average your funding will just go to the same place as the ordinary programmer’s. And so either you won’t fund the organization at all, or lots of ordinary programmers will fund it too and your funding won’t mean much on the margin.

And you can’t outsource your judgement to an organization-evaluator – because if your ability to judge the judgement of organization-evaluators is the same as an ordinary programmer’s, then lots of ordinary programmers will follow the recommendations of the organization-evaluator and you get the same problem. The ability to contribute by offering funding is, to a first approximation, only valuable insofar as the funder personally has unusual abilities, not possessed by any billionaire or by more than a small fraction of Silicon Valley career software developers, to judge which things need more money and which need less. (And if you do have that ability – not meaning Kelsey-Piper-you here, but hypothetical-abstract-you – and don’t already have a good chunk of change to contribute, why not become an accountant? All the important-to-humanity organizations I’ve been closely involved with have been in desperate need of good accountants. Again, it’s not accounting itself that’s valuable here, but accounting combined with highly-unusual-for-accountants-judgment-of-which-organizations-to-contribute-to.)

Citations in Math Academia

Many Internet commenters have criticized MIRI for not producing enough research, relative to their budget – not writing enough papers, or not getting those papers peer-reviewed, or not getting enough citations. However, MIRI’s specialty is math and computer science, which might have lower citation counts than experiment-heavy fields like chemistry or biology. For a quick sanity check, I looked up a few non-MIRI mathematicians as points of comparison.

Grigory Margulis is probably the most accomplished mathematician I’ve personally met. He’s a Fields Medalist, won the Wolf Prize in 2005, and is doing pure math as a Yale professor full-time, so it seems reasonable to assume he’s in the upper quantiles of productivity. A Google Scholar search for the last five years turned up eight papers that he’s co-authored; by my count, those eight papers (combined) have 35 citations, of which nine are self-citations. All of those papers had multiple authors, so it took well over five person-years of total effort to produce them.

But of course, a Fields Medalist isn’t a representative math researcher. One friend of mine recently got a math Ph.D. from an elite university; as a grad student, they spent years doing math research full-time, and they also did a lot of part-time research in undergrad. They wrote several papers while in grad school, plus (of course) a dissertation, but these don’t appear to be on Google Scholar; presumably they’re still awaiting publication, or they weren’t published in a place Google indexes. They also published two papers before grad school, of which only one was peer-reviewed; these two papers have 13 total citations, of which five are self-citations.

Another friend of mine got a math Ph.D. some years ago, from a less elite university. They wrote four papers which appear in a Google Scholar search. Of those four, one wasn’t a math paper, and was published long after they graduated; one was their dissertation; one was posted on arXiv, but doesn’t seem to have been formally published; and one was published as a conference paper. Excluding the non-math paper, the remaining three papers have eight total citations.

Another friend of mine just got a math Ph.D. from an elite school, and is taking an academic job after graduating. They’ve written a number of papers, given talks, etc.; but again, a lot of these don’t appear on Google Scholar. Three of their papers are on Google Scholar, but all three appear to be arXiv papers that haven’t been formally published, and the three papers have three total citations.

But all that might just be selection bias in who I know. Using random.org to pick two math postdocs – one from Stanford, one from Berkeley – their CVs list a combined total of eighteen papers, of which seven have been formally published, four are listed as “accepted” but not yet published, and the remaining seven are on arXiv or self-hosted. Of these 18 papers, the most cited one had a total of 13 citations, of which four were self-citations.

(Disclaimer: I’m not a math academic; comments/corrections from people who are appreciated.)

Universities vs. Scientology

A handy guide:

university_scientology

New York Times Makes Up Facts About SF Housing

Last Saturday, the New York Times published an article titled “High Rents Elbow Latinos From San Francisco’s Mission District”. (They later changed the title to “Gentrification Spreads an Upheaval in San Francisco’s Mission District”, without acknowledging the change.) The article opened with this sentence:

“SAN FRANCISCO — Luxury condominiums, organic ice cream stores, cafes that serve soy lattes and chocolate shops that offer samples from Ecuador and Madagascar are rapidly replacing 99-cent stores, bodegas and rent-controlled apartments in the Mission District, this city’s working-class Latino neighborhood.”

The statement is clear: “luxury condos are rapidly replacing rent-controlled apartments”. The problem with this “fact” is that it’s not true. The New York Times’ writer, Carol Pogash, made it up out of thin air to sell her story. As any journalist will tell you, nothing sells like conflict… and if that doesn’t mesh with the truth, well, the truth be damned.

How do we know that? Because San Francisco Planning keeps very detailed records of all city housing. We can just look up every Mission development since 2010, and see how many older apartments they replaced. The final total is… zero. Yes, zero. In the last five years, since rents started rising after the 2008 crash, not a single Mission apartment has been torn down for “luxury condos” (or any other new housing). But the NYT wants people to think rent-controlled apartments are being “rapidly replaced” for new development, so that’s what they’ll print, even though this has never once actually happened.

Here’s the full list of Mission housing developments built since 2010:

480 Potrero Ave., 84 units, under construction as of 2014; replaced a vacant lot
35 Dolores St., 37 units, under construction as of 2014; replaced abandoned auto shop
899 Valencia St., 18 units, under construction as of 2014; replaced a gas station
685 Florida St., 4 units, under construction as of 2014; replaced a vacant lot
39 San Carlos St., 3 units, under construction as of 2014; units over ground-floor garage
85 Brosnan St., 3 units, under construction as of 2014; replaced an office building
930 Shotwell St., 2 units, under construction as of 2014; replaced a vacant lot
1076 Hampshire St., 2 units, under construction as of 2014; replaced a storage building
1515 15th St., 40 units (7 affordable), built 2014; replaced an abandoned gas station
2421 16th St., 12 units (1 affordable), built 2014; replaced a vacant lot
3418 26th St., 11 units, built 2014; replaced a vacant lot
1600 15th St., 202 units (40 affordable), built 2013; replaced an abandoned factory
3500 19th St., 17 units, built 2013; replaced a surface parking lot
200 Dolores St., 13 units (2 affordable), built 2013; replaced a vacant lot
141 Albion St., 3 units, built 2013; replaced a vacant lot
857 Alabama St., 2 units, built 2013; replaced an abandoned storage building
3135 24th St., 9 units, under construction as of 2013; replaced a parking garage
1280 Hampshire St., 3 units, under construction as of 2013; replaced a garage
299 Valencia St., 36 units (4 affordable), built 2012; replaced a vacant lot
411 Valencia St., 16 units (2 affordable), built 2012; replaced a garage/auto repair shop
179 San Carlos St., 3 units, under construction as of 2012; replaced a vacant lot
555 Bartlett St., 60 units (9 affordable), built 2010; replaced a paint store
2101 Bryant St., 26 units, built 2010; replaced a hole in the ground
736 Valencia St., 8 units, built 2010; replaced a vacant parking lot

Theranos interview (2005)

An interesting May 2005 interview with Elizabeth Holmes, founder of the mysterious Theranos. Original audio here.

Dr. Moira Gunn: This is the Tech Nation Podcast from IT Conversations. The world we know is changing. This is BioTech Nation.

Dr. Moira Gunn: The Center for Drug Evaluation and Research at the FDA has just reported that in 2004, over 400,000 Americans reported adverse drug reactions, and 100,000 Americans died. On the benign side, other research shows that 40% to 60% of all patients don’t benefit from the drugs they’re prescribed. I asked Elizabeth Holmes, the President and CEO of Theranos: why can’t we figure out who will have an adverse drug reaction, and why certain drugs aren’t going to work?

Elizabeth Holmes: I think that part of it has to do with the fact that there is no mechanism in place to deal with monitoring patients on an individualized basis today. When we began Theranos, what we focused on was creating a customized medicine tool that could be used in the home by every patient. So that every day, a patient can get real time analysis of their blood samples, and look at not only how drugs are reacting in their body, but how other metabolic or physical factors contributed to how well a given drug works in them as an individual.

This is different from the traditional process of sending a patient into a clinic at random time points, trying to get a sample of their blood and then analyzing, at that second in time, what the drug is doing. Because it gives you a much better and much more complete understanding of all of the factors that contribute to how well a drug works or does not work. Like if the patient’s taking other drugs, which happen to cross interact. So, the ability to begin bringing monitoring, as we call it, into the home, we believe could fundamentally change the way that both patients are treated as well as drugs are developed.

Dr. Moira Gunn: I guess right now it’s called the RDX Metabolic Profiler?

Elizabeth Holmes: That’s absolutely right.

Dr. Moira Gunn: Well, we’ll get those marketing people on that. I think it may be called something else by the end, something handy by the end. Now tell us, exactly how big is it? What does it do? What do you got to do if you’re using it?

Elizabeth Holmes: So, it’s a handheld device and it’s fully integrated. The only thing you have to do is hold your finger, or you could actually use any part of your hand or your arm, up toward the device and it takes a very small sample of blood. So small that you can barely feel it. Thanks to the art of glucose monitoring, small blood sampling has really …

Dr. Moira Gunn: So it extracts a little from your hand?

Elizabeth Holmes: Exactly. It’s a little tiny needle that pulls a little tiny drop of blood, and when it gets the drop of blood, basically it runs it through, what we call, a biochip which separates out all the cells and other types of analytes in your blood which could traditionally clog a biosensor. And then, in real time it runs many different chemistries. Looking for different, in this case, targeted markers. Like the drug concentration, or concentrations of other proteins that may be in your blood that are indicative of either risk…

Dr. Moira Gunn: Adverse drug reaction.

Elizabeth Holmes: Exactly.

Dr. Moira Gunn: Okay, so once it … identifies that? What does it do? Just tell you?

Elizabeth Holmes: So, when you do that-

Dr. Moira Gunn: It has a big screen that says, “Sit down”?

Elizabeth Holmes: No. The patient doesn’t see anything. It’s a very small handheld device. So once the device begins working, it’s a real time event in which the blood sample is analyzed. When it separates all the cells out and it identifies the markers it’s looking for, the first that happens is you get a signal. It’s basically reflective of a concentration, or the presence or absence of certain cells you may be looking for.

And when that happens, the electronic aspect of the device takes hold and transmits that data to our website. Where we’ve written what is basically biostatistics algorithms to correlate that information, and profile it. So, we’re actually in the process of redesigning our websites, so that patients and physicians can log in, and a nurse can monitor this data and then say to the patient, “You know, you’re fine.”

Again, the backdrop to all of this is, when a drug is prescribed, we are coupling the system with the drug. So if you know, when you go to get a drug, that you have risk of an adverse event, or you’re not sure, or you’re nervous about it, you can monitor yourself for a month, and then evaluate whether or not that drug is the best drug for you.

Dr. Moira Gunn: Okay. I got to ask it, does it hurt to have the drop of blood extracted?

Elizabeth Holmes: I can tell you, personally, I hate needles. They make me want to faint, and I am fine doing with doing this drop of blood. We’re talking really really small. You can barely see it. If you poke yourself in the arm or on the palm of your hand, it doesn’t hurt as much as if you do it on the fingertip, because there’s many nerves in your fingertip. So it’s more of a pain site, whereas your arm has thicker skin. So you actually get an even smaller drop of blood out, but you don’t hit the nerves that make you feel pain.

Dr. Moira Gunn: And you’ve got plenty of blood to do your assay.

Elizabeth Holmes: Oh yeah. Absolutely. That’s the beauty of the technology, is that we’re really talking about miniaturization.

Dr. Moira Gunn: Now a lot of people are saying, “Well she’s President and CEO. Where are all the engineers that built this?” This is built around your patent.

Elizabeth Holmes: Yeah. That’s true. Part of the culture of our company is to make sure that we are fully integrated, so people who are working on business development and people who are working on marketing, everything, revolves around the engineering aspects and the technology aspects. And continually striving to be, really, the leader in creating an industry around these personalized monitoring systems.

So, yes, I am actively involved in the technology and the technology did come from… sort of an integration of work I’ve done in different technical fields. And the concept that if you could bring different technologies together, you could maximize the power behind them. I think it’s very clear that this is a wonderful time for the convergence of the electronic and IT infrastructure with biosystems. In our case, it’s to create biosensors.

Dr. Moira Gunn: If this is a job interview I couldn’t ask you, but I can ask you. And it’s the radio, so people are surprised. How old are you Elizabeth?

Elizabeth Holmes: I’m 21.

Dr. Moira Gunn: You’re 21.

Elizabeth Holmes: Yes.

Dr. Moira Gunn: So, you were at Stanford. What were you studying there?

Elizabeth Holmes: I was studying Chemical Engineering, but I was also involved with Electrical Engineering, and with some biosensing projects.

Dr. Moira Gunn: Okay, so you were doing all this. Did you actually build a new technology while you were there? Or did you drop out to do that?

Elizabeth Holmes: I actually did build other new technologies while I was there. I was working on a project for a major pharmaceutical company, a wireless biosensor. And I was working on another microfluidic project, basically dealing with fluids in very small volumes, which is relevant to what we’re doing now.

I actually left Stanford to go work in Singapore. Background on, I guess, story of my life. I have been studying Mandarin for a long time and have spend some time studying in China. I wanted to go back to Asia, but was interested in biotech. And so I went to Singapore, because there’s a tremendous amount of resources that are being poured into research there. I got the opportunity to help develop a novel approach in microarray, and was looking at that technology and thinking about the type of sensors I’ve built at Stanford. And realizing that if you could integrate the ability to do high-throughput screening, meaning the detection of many different types of markers into a little titanium chip like the ones that we had expertise building here… you would really have a powerful sensor, and truly a platform with respect to the ability to say, “Look, we’re going to work toward monitoring anything, anytime.”

Dr. Moira Gunn: What’s the status of the device today? Is it still just a prototype? Where are you?

Elizabeth Holmes: Okay. Our first applications are actually in monitoring acute pain killers. And that device is going into the production phase. We hope to release it, actually, to a pharmaceutical partner around mid to late this year.

Dr. Moira Gunn: So you’re almost there. You’re almost in manufacture.

Elizabeth Holmes: Oh, absolutely. I think it’s a iterative process, because what we look at is the ability to monitor different things just based on changing this little cartridge that slides into your handheld readers. We’ve got the reader, now we’re developing a series of different cartridges for different purposes.

Dr. Moira Gunn: Now, how much money have you raised thus far in venture capital funds?

Elizabeth Holmes: So, in venture capital funds we raised just over 6 million, and then we’ve also raised money from private investors.

Dr. Moira Gunn: Okay, and you’re 21?

Elizabeth Holmes: Yes.

Dr. Moira Gunn: Okay. I’m going to tell my 2 children they better get off their duffs, Elizabeth. I have one more question for left for you.

Elizabeth Holmes: Absolutely.

Dr. Moira Gunn: What are you going to do when you’re 30?

Elizabeth Holmes: This. We have ideas, and actually the way that we structured our company is to build what we call an innovation division. And already, we have next generations of this product in prototype form in-house. And that’s with respect to miniaturizing the system to make it faster. To make it more high-throughput. To put it into all sorts of different types of devices that can take use to the point where this is automated. and you don’t even have to touch your finger on the device.

Dr. Moira Gunn: No pain. Elizabeth this-

Elizabeth Holmes: No pain.

Dr. Moira Gunn: No pain.

Elizabeth Holmes: That’s the objective.

Dr. Moira Gunn: This has been terrific Elizabeth. Come back and see us anytime, and we really look forward to seeing you.

Elizabeth Holmes: All right. Thank you so much.

Dr. Moira Gunn: Elizabeth Holmes is the President and CEO of Theranos. For BioTech Nation I’m Moira Gunn. You’ve been listening to TechNation podcast from IT Conversations. For more information visit our websites at technation.com and itconversations.com. I’m Moira Gunn. Thanks for listening.

The Chinese Robber Fallacy

The Chinese Robber Fallacy is where you use a generic problem to attack a specific person or group, even though other groups have the problem just as much (or even more so).

Suppose you’re racist against Chinese people. You can go on the Internet and say:

“Man, screw the Chinese. The Chinese are thieves.”

And when someone replies: “Hey, is that really true?”

“Yeah! Just look at <example> and <example> and <example> of these robberies by evil Chinese criminals.”

“Sure, but that’s just anecdotal evidence.”

“Our statistics say that Chinese people commit an average of <big number> thefts a year. That’s a lot! How could you trust a Chinese person?”

“But don’t non-Chinese rob people too?”

“Maybe, but if so, that doesn’t make the Chinese any less guilty, does it? First we should deal with the Chinese criminal problem, and then if we’re successful, maybe we can move on to other types of theft.”

“Are the Chinese really the first group we should target in our anti-theft campaign?”

“Hey, quit trying to change the subject. Are you trying to deny the immorality of stealing people’s hard-earned property? Why, just go into Chinatown and walk around for a while, you’ll see a Chinese mugger soon enough, it’s right there in front of our eyes… “

Checking Out The Necronomicon

(Song by Raymond Arnold. To the tune of ‘Winter Wonderland’.)

Dusty tome, lies forgotten
Cover worn, pages rotten
A curious book
I’ll just take a look
Checking out the Necronomicon

Creepy words, pages turnin’
As your brain, is a churnin’
Insidious memes
Infecting your dreams
Haunted by the Necronomicon

In the graveyard I can make a promise.
That is not dead which eternal lies,
Soon I’ll reunite with brother Thomas!
(For with) strange aeons even death may die…

More I’ve read, the more I’m listenin’
In my head, voices whisperin’…
‘Tonight is the night’,
‘The stars are all right’,
Time to use the Necronomicon

In the graveyard we could raise an army,
Send it out to ravage all the land…
Sure, the thought may seem a bit alarming,
(But if you) read the book, I swear you’ll understand!

Later on, we’ll conspire
As we dream by the fire
To face unafraid
The plans that we made
Studying the Necronomicon

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