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Why Reddit Is So Hard to Monetize
Alexis Ohanian is the co-founder of the social news aggregation website Reddit. Shortly after graduating from UVA, he and classmate Steve Huffman founded the company with startup capital from Y Combinator, and in 2006 it was bought by Condé Nast. Ohanian stayed on for three more years until he retired to pursue a Kiva fellowship in Armenia. In 2009 he founded Breadpig, an "uncorporation" that creates and sells "geeky products" and donates all its non-sustainable profits to charity.
Question: Why is Reddit so hard to monetize? Is it hard to sell ads on Reddit?
Alexis Ohanian: I think there’s a lot of education that comes with a site like Reddit. And it is also – it is a different sell. So not only do you have to go to the agency and say, “okay, here’s how this site Reddit works.” And when they ask a question like, “Well what happens if some user votes up a story that says, ‘this brand is lame.’ And you have to say, “Well the smartest thing they can do is have someone reading reddit or who is willing to be notified when this happens and gets in on the comments and says, -- they create the user name that’s like BrandXRepresentative, and then they get into the comments and don’t – you know, they don’t stoop to like trollish flame war levels, but they actually have a discussion. And what’s so frustrating is so many small companies, I’m talking one or two you know small business – one or two person small businesses have done this so incredibly well on Reddit. I mean, soap companies, hot sauce companies, random software, hosting companies have done this brilliantly, brilliantly. And the only reason they – I mean, their benefit to dollar ratio is phenomenal, and the only reason they can get away with this and the only reason they can do this so effectively is because they actually use Reddit. Like they actually understand the community, they are participants in it.
And it really frustrates me whenever some social media guru gets on stage or tries to convince some company that they need to be hired when, even in the – especially in the large companies, there must be someone there who is spending a good part of their day actually in the Reddit community, make her your social media guru. That’s it. Just tell her, listen, you’re getting some new responsibility now, here are the things you’re allowed to say, here are the things you’re not allowed to say. Anytime our company comes up, have a discussion. Say it and give them the freedom and I’m sure that’s scary as all hell for some every publicly traded company, and I don’t know jack about running one of those. But it’s been great watching smaller businesses have a field day because they are – businesses are being saved on Reddit because people come onto that site with candor and there are business people just like some suited or publicly traded company. They are working for a for-profit company, but they’re doing it as Redditors. And the difference that makes is just phenomenal.
And so even though we try to work really, really hard to work with these advertisers and some of the much better than others and been willing to go that extra level to do a bit of leg work on their end or have a bit of more openness on their end. And I think it’s paid off tremendously.
Question: What was it like working with Condé Nast?
Alexis Ohanian: Okay. So for the three years that I was there and that Steve was there, we had a ludicrous amount of autonomy. And I mean, we would come in for quarterly meetings and we would present a couple of slides that showed traffic and that showed, fortunately, that went up and it showed what we were going to work on, what we did work on and what we will work on. And that was it. We’d have some discussion and then we’d go back to California. And that was wonderful. That was really, really splendid. I think we, you know, we made an agreement and the reason why it was such a palatable acquisition for us was because he had heard so many horror stories. You can’t walk two feet in the Bay Area or at Silicon Valley or in any startup community rather – any startup community and not hear about a horror story about some acquirer crushing the souls of the company the bought. And this oftentimes happens at tech companies.
So imagine what we were thinking when a publisher, like **** wanted to acquire us. We thought, well what do they know about tech? you know, they have no idea about how deal with, you know, engineers how to deal with all of that stuff, but to their credit they said, “Listen, you guys are doing something well, that’s why we want to buy you. So logically, we’ll let you continue doing what you’re doing under the auspices of our company now. You’ll get health insurance; you’ll get a salary, which was nice. And you’ll actually operate as you did before except you know, you’ll have a nice office instead of your crappy living room. And we said, “Okay. That seems like a good deal.” And for the most part, absolutely lived up to it; absolutely lived up to it. And I think – I can assure you, Steve would; not have stuck around for three years if that weren’t the case, and he did. And I did too.
So, what I think – when I think things got tricky though is when – when it came to actually monetizing Reddit. That is something they’ve still not been able to figure out. The team has come up with some great stuff in the way of self-serve sponsored headlines, Reddit Gold, different other ways to bring in revenue and merchandise, but it is a tricky proposition, but also a really frustrating one because Reddit has a fantastic audience. One with 300 million page views a month. I mean, it’s absurd how much traffic Reddit has. And so it’s just a matter of finding a way to say, “all right, we have 300 million impressions of a very, very like connected, like very thoughtful, very just impressive audience, how do we advertise to them in a way that isn’t screwing them as a user and at the same time providing enough value to an advertiser to want to do it.
And I think it can be done, but we’re still working on that.
After being purchased in 2006 by publisher Conde Nast, the company has walked a fine line between attempting to monetize its traffic and remaining true to its community.
If machines develop consciousness, or if we manage to give it to them, the human-robot dynamic will forever be different.
- Does AI—and, more specifically, conscious AI—deserve moral rights? In this thought exploration, evolutionary biologist Richard Dawkins, ethics and tech professor Joanna Bryson, philosopher and cognitive scientist Susan Schneider, physicist Max Tegmark, philosopher Peter Singer, and bioethicist Glenn Cohen all weigh in on the question of AI rights.
- Given the grave tragedy of slavery throughout human history, philosophers and technologists must answer this question ahead of technological development to avoid humanity creating a slave class of conscious beings.
- One potential safeguard against that? Regulation. Once we define the context in which AI requires rights, the simplest solution may be to not build that thing.
Duke University researchers might have solved a half-century old problem.
- Duke University researchers created a hydrogel that appears to be as strong and flexible as human cartilage.
- The blend of three polymers provides enough flexibility and durability to mimic the knee.
- The next step is to test this hydrogel in sheep; human use can take at least three years.
Duke researchers have developed the first gel-based synthetic cartilage with the strength of the real thing. A quarter-sized disc of the material can withstand the weight of a 100-pound kettlebell without tearing or losing its shape.
Photo: Feichen Yang.<p>That's the word from a team in the Department of Chemistry and Department of Mechanical Engineering and Materials Science at Duke University. Their <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.202003451" target="_blank">new paper</a>, published in the journal,<em> Advanced Functional Materials</em>, details this exciting evolution of this frustrating joint.<br></p><p>Researchers have sought materials strong and versatile enough to repair a knee since at least the seventies. This new hydrogel, comprised of three polymers, might be it. When two of the polymers are stretched, a third keeps the entire structure intact. When pulled 100,000 times, the cartilage held up as well as materials used in bone implants. The team also rubbed the hydrogel against natural cartilage a million times and found it to be as wear-resistant as the real thing. </p><p>The hydrogel has the appearance of Jell-O and is comprised of 60 percent water. Co-author, Feichen Yang, <a href="https://today.duke.edu/2020/06/lab-first-cartilage-mimicking-gel-strong-enough-knees" target="_blank">says</a> this network of polymers is particularly durable: "Only this combination of all three components is both flexible and stiff and therefore strong." </p><p> As with any new material, a lot of testing must be conducted. They don't foresee this hydrogel being implanted into human bodies for at least three years. The next step is to test it out in sheep. </p><p>Still, this is an exciting step forward in the rehabilitation of one of our trickiest joints. Given the potential reward, the wait is worth it. </p><p><span></span>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a>, <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank">Facebook</a> and <a href="https://derekberes.substack.com/" target="_blank">Substack</a>. His next book is</em> "<em>Hero's Dose: The Case For Psychedelics in Ritual and Therapy."</em></p>
What would it be like to experience the 4th dimension?
Physicists have understood at least theoretically, that there may be higher dimensions, besides our normal three. The first clue came in 1905 when Einstein developed his theory of special relativity. Of course, by dimensions we’re talking about length, width, and height. Generally speaking, when we talk about a fourth dimension, it’s considered space-time. But here, physicists mean a spatial dimension beyond the normal three, not a parallel universe, as such dimensions are mistaken for in popular sci-fi shows.
An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.
- 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
- Early treatment is available but there's been no way to tell who needs it.
- Using clinical data already being collected, machine learning can identify who's at risk.
The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.
In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.
That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.
70 data points and machine learning
Image source: Creators Collective/Unsplash
Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:
"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."
The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.
Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."
Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.
Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.
On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.
Image source: Külli Kittus/Unsplash
Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."
"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.
The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.