Looking at Eugene Wei’s Invisible Asymptotes

Invisible asymptotes, a recent blog post from Eugene Wei dissecting social networks, product design philosophy, and the celling for Amazon, is tremendous. I spent 7 years in Vietnam growing multiple social networks to millions of users, and thus developed a philosophy about how people engage with each other changes generationally and how new social networks emerge. Wei’s insights are spot-on (it’s true that I say this because his opinions largely match mine, but his are much better articulated and detailed).

It’s an incredibly long article, but well worth reading. Some of my highlights:

Defining invisible asymptote:

For me, in strategic planning, the question in building my forecast was to flush out what I call the invisible asymptote: a ceiling that our growth curve would bump its head against if we continued down our current path. It’s an important concept to understand for many people in a company, whether a CEO, a product person, or, as I was back then, a planner in finance.

On Twitter:

In the case of Twitter, I think the theory is wrong. Given the current configuration of the product, I don’t think any more meaningful user growth is possible, and tweaking the product as it is now won’t unlock any more growth. The longer they don’t acknowledge this, the longer they’ll be stuck in a Red Queen loop of their own making.

On Snapchat and generational communication preferences:

I’ve written before about selfies as a second language. At the root of that phenomenon is the idea that a generation of kids raised with smartphones with a camera front and back have found the most efficient way to communicate is with the camera, not the keyboard. That’s not the case for an older cohort of users who almost never send selfies as a first resort. The very default of Snapchat to the camera screen is such a bold choice that it will probably never be the messaging app of choice for old folks, no matter how Snapchat moves around and re-arranges its other panes.

More than that, I suspect every generation needs spaces of its own, places to try on and leave behind identities at low cost and on short, finite time horizons. That applies to social virtual spaces as much as it does to physical spaces.

On Facebook:

This is one of the diseconomies of scale for social networks that Facebook is first to run into because of its unprecedented size. Imagine you’re in a room with all your family, friends, coworkers, casual acquaintances, and a lot of people you met just once but felt guilty about rejecting a friend request from. It’s hundreds, maybe even thousands of people. What would you say to them? We know people maintain multiple identities for different audiences in their lives. Very few of us have to cultivate an identity for that entire blob of everyone we know. It’s a situation one might encounter in the real world only a few times in life, perhaps at one’s wedding, and later one’s funeral. Online, though? It happens to be the default mode on Facebook’s News Feed.

On Instagram:

There is a purity about Instagram which makes even its ads perfectly native: everything on the service is an audio-visual advertisement. I see people complain about the ad load in Instagram, but if you really look at your feed, it’s always had an ad load of 100%.

I just opened my feed and looked through the first twenty posts, and I’d classify them all as ads: about how great my meal was, for beautiful travel destinations, for the exquisite craft of various photographers and cinematographers, for an actor’s upcoming film, for Rihanna’s new lingerie line or makeup drop, for an elaborate dish a friend cooked, for a current concert tour, for how funny someone is, for someone’s gorgeous new headshot, and for a few sporting events and teams. And yes, a few of them were official Instagram ads.

On understanding the root customer need from data and feedback:

True, it’s often difficult for customers to articulate what they want. But what’s missed is that they’re often much better at pinpointing what they don’t want or like. What you should hear when customers say they want a faster horse is not the literal but instead that they find travel by horse to be too slow. The savvy product person can generalize that to the broader need of traveling more quickly, and that problem can be solved any number of ways that don’t involve cloning Secretariat or shooting their current horse up with steroids.

Intuition “genius” vs data – no one you know is a genius, but a bunch of people you know think they are:

Lastly, though I hesitate to share this, it is possible to avoid invisible asymptotes through sheer genius of product intuition. I balk for the same reason I cringe when I meet CEO’s in the valley who idolize Steve Jobs. In many ways, a product intuition that is consistently accurate across time is, like Steve Jobs, a unicorn. It’s so rare an ability that to lean entirely on it is far more dangerous and high risk than blending it with a whole suite of more accessible strategies.

Just like athletes, there is a prime in one’s professional life. Michael Jordan can’t play in the NBA right now at aged 55  – just because someone had great product insight ten years ago, this doesn’t mean they have it today:

The reason I recommend a healthy mix of intuition informed by data and feedback is that most product people I know have a product view that is slower moving than the world itself. If they’ve achieved any measure of success, it’s often because their view of some consumer need was the right one at the right time. Product-market fit as tautology. Selection bias in looking at these people might confuse some measure of luck with some enduring product intuition.

However, just as a VC might have gotten lucky once with some investment and be seen as a genius for life (and the returns to a single buff of a VC brand name is shockingly durable), just because a given person’s product intuition might hit on the right moment at the right point in history to create a smash hit, it’s rare that a single person’s frame will move in lock step with that of the world. How many creatives are relevant for a lifetime?

Jeff Bezos talks about how every day is day one – every new day, there is a baseline to move forward and redefine what you can do. Continually win customers, don’t defend (lock them in) them from competitors.

You can’t overserve on user experience, Thompson argues; as a product person, I’d argue, in parallel, that it is difficult and likely impossible to understand your customer too deeply. Amazon’s mission to the be the world’s most customer-centric company is inherently a long-term strategy because it is a one with an infinite time scale and no asymptote to its slope. …

By discovering their own limitations early, they are also quicker to discover vectors on which they’re personally unbounded. Product development will always be a multi-dimensional problem, often frustratingly so, but the value of reducing that dimensionality often costs so little that it should be more widely employed.

Game Planning with Best Bike Split for the Tierra Bella Bicycle Tour

I’m a regular listener of the TrainerRoad Ask a Cycling Coach Podcast. In it, the hosts discuss using Best Bike Split quite a bit in preparation for their events, particularly races. Best Bike Split can take your riding profile, including your power and aero profile, event goals, and course map to create segment-based power recommendations.

I was really interested in trying out the service for Tierra Bella (more on that experience here), and the free version will let you do power-based goals. Paid versions let you to optimize for time and speed-based goals and really drive down into the customization.

In my plan, I set an Intensity Factor (IF) of .75 (actual result was .72) as my goal.

Based on this, BBS predicted:

  • Ride time of 7:14 (actual: 8:13)
  • Net Power of 175 (170)
  • Variability Index of 1.05 (1.14)
  • Training Stress Score of 405 (430).

I didn’t know how well I’d adhere to the plan, so my approach was to spend most of the time in aero down in the drops to get some (I had set up the BBS plan to simulate me primarily riding on the hoods) free speed. The plan called for me to be around 160-170 watts most of the time, which I felt I could maintain in the drops. I would have been unable to maintain 200 watts in the drops for longer periods, however, due to being much more compressed on the bike (legs get closer to the stomach, making breathing more difficult).

Even though I wouldn’t be putting in max power (and thus not maximizing air resistance benefits) on the ride, spending a lot of time in aero position would have cumulative effects.

Why was I so slow compared to Best Bike Split?

There are definitely a numbers of factors. I was able to maintain BBS’ power goals on flats and moderate climbs pretty easily, but I was not as good on sustained climbs and descents. BBS would ask for around 100 watts on descents, not much, but as I’m a shaky descender who didn’t know the roads and who also wanted to avoid the hoards of cyclists climbing the other way (I was on small lane roads with below-average road conditions), I focused on safety rather than speed. I am guessing BBS may have an option to tweak this. On sustained climbs, I found that getting up to 90% FTP after miles 50+ was getting tougher and tougher, especially as the weather moved from 43 degrees F (at 7AM) to a peak of 95 (during the Hicks climb) without airflow. I had to be careful about not overexerting myself – I didn’t want to bonk (run out of energy) with 40 miles to go.

Because I didn’t overextend myself on the climbs and didn’t work on the descents, I was pretty confident I had some energy left to use after reaching the last rest stop with about 12 miles left in the 124 mile (200KM) ride. I did these last segments with 15% higher power than BBS’ goals, making up for some of the lost intensity from the previous 100 miles.

If I look back at my other metrics, this is what I think happened. Overall, my per minute intensity was less (.72 vs .75 IF) than projected, but I worked for much longer, leading to a higher total stress (9 vs 8 hrs, 430 vs 405 TSS) score. Since I had descents of essentially zero effort but then tried to make up for these at the end of the ride, this led to more variability in the power, leading to perhaps less efficient use of the power in terms of speed. I lost out on a lot of potential gain by not putting power on the descents, but also taking them relatively slowly. For example, the Henry Coe descent is 10 miles.

There’s one stretch in the ride that I think can serve as a good example of what BBS thought would happen, and what actually did. BBS does consider weather conditions such as wind direction based on historical data, with premium membership including more data.

From the Henry Coe descent on to Bailey / Morgan Hill, Best Bike Split gave me a 10 mile flat segment of 156 watts on the hoods. I actually spent the entire time in aero.

BBS Prediction Wind

Best Bike Split Prediction (#102)

BBS Prediction

In the first image, you can see that Best Bike Split was expecting a tailwind (see lower right corner of image), which explains why I’d be doing 20.78 MPH at only 156 watts. Keep in mind that this prediction was for me on the hoods, sitting more straight up with increased air resistance, not in the drops (aero) position.

Strava Output

In reality, looking at my Strava record above, I was consistently over the goal – my guess is I was around 15W above for the 10 miles and still 3MPH below BBS’s expected speed, again despite being in aero. This was a 5 minute loss on this segment alone.

I was able to integrate the BBS plan with my Wahoo Elemnt Bolt; one problem I noticed is that despite having the power levels pop up on screen, I would not get directional information on the display. Thus, cues would be for new power outputs, not road directions. I’m not sure if the original map for Tierra Bella is missing this information, but my guess is that BBS overrides it. As a result, you have to use the Elemnt’s map display – it took me a bunch of wrong turns on the course before I realized this. The disadvantage of having the map display on all the time is that you have access to fewer data fields. There could be ways to get both power and directional cues, but I will have to look into that more next time.

Overall, I liked working with the BBS plan. It gave me power levels to focus on while giving me the confidence that I wasn’t overexerting myself. I knew I could last the 125 miles.

Best Bike Split costs $20 per month or $120 per year to be a premium subscriber. Since I am not a racer and don’t do many events, it’s not worth subscribing, but I’d be open to paying per event (ex. $5-10 per event with more features than free but less than full premium).