Solving for Carbon Dioxide

This graph is a record of Carbon Dioxide in the earth’s atmosphere from Nasa’s Mauna Loa observatory in Hawaii.

Carbon Dioxide warms the planet. The most problem of a warmer planet is the melting of ice caps and an increase in water levels.

But, the other emerging story around Carbon Dioxide is the effect it has on nutrients in plants. Politico recently wrote about the work of Irakli Loladze on how Carbon Dioxide reduces minerals in plants and replaces it with carbohydrates. The article concludes with the following 2 paragraphs.

What he found is that his 2002 theory — or, rather, the strong suspicion he had articulated back then — appeared to be borne out. Across nearly 130 varieties of plants and more than 15,000 samples collected from experiments over the past three decades, the overall concentration of minerals like calcium, magnesium, potassium, zinc and iron had dropped by 8 percent on average. The ratio of carbohydrates to minerals was going up. The plants, like the algae, were becoming junk food.

What that means for humans — whose main food intake is plants — is only just starting to be investigated. 

Researchers who dive into it will have to surmount obstacles like its low profile and slow pace, and a political environment where the word “climate” is enough to derail a funding conversation. It will also require entirely new bridges to be built in the world of science―a problem that Loladze himself wryly acknowledges in his own research. When his paper was finally published in 2014, Loladze listed his grant rejections in the acknowledgements.

The whole article is fascinating. And, so is the discussion around Loladze’s original paper. His central thesis is that excess Carbon Dioxide for plants is like junk food.

(That’s Lozadle tossing sugar on vegetables to illustrate his point)

So, what are we doing about the Carbon Dioxide problem? The most promising piece of technology is a concept called the artificial leaf – a ground breaking invention by two Harvard researchers. More on that on my Notes by Ada note on Medium or LinkedIn.

Just to be clear, though, this isn’t about saving the planet. Solving for Carbon Dioxide will be critical if we are going to find a way to survive on this planet. We don’t read about this stuff in the news because climate change is a dirty word these days.

Maybe we’d have a higher success rate if we stopped referring to all of this as efforts to “save the planet.” Maybe we should call it “save human beings from extinction” instead?

Product Leadership vs Product Management

This is a series of posts that is a synthesis of ideas from 4 sources – Marty Cagan’s workshop on Product Management, Product Leadership (the book), conversations I’ve had with experienced product managers and my own observations. I’d like to explore what building products is all about, the various folks and forces at play and tools and ideas that might help get the job done better.

Product Leadership and Product Management: Product leadership isn’t just about leading a team of product managers. Instead, every technology product manager has 2 aspects to their job – product management and product leadership. This is analogous to the management and leadership of a business. While often discussed in the same breath, they are very different. Leadership is about doing the right things or effectiveness while management is about doing things right or efficiency. Similarly –

  • Product leadership is the time spent on deciding which products to build to add value to customers. The challenges here revolve around “customer/user discovery” or finding “product-market fit.”
  • Product management is running the process of building products as efficiently as possible.  The challenge here is generally around optimizing funnels.

Folks in smaller organizations tend to spend most of the their time wrestling with product-market fit. Thus, smaller organizations requires product managers who are comfortable wearing the product leadership hat. In larger organizations, senior product leaders or product managers leading “venture bets” tend to spend more time wearing the leadership hat.

In summary, wearing the product leader hat involves spending time wrestling with questions around product-market fit while wearing the product manager hat involves spending time wrestling with optimizing funnels. 

Leading a product: A successful product is one that is valuable, usable and feasible.

When you are deciding whether to build a product, you work within these constraints by thinking of the market, the customer and the/your company. This process requires the product leader to go through a process of customer discovery (in lean start up parlance) to ensure she is building a product that has hope of finding product-market fit. Or, put differently, the product leader tries to find a customer to validate her hypothesis that her product solves a real need and is, thus, valuable. Once the value is ascertained, she can begin scoping a product that is usable and feasible.

This is best visualized when you think of the primary tool a product leader uses. For the product leader, a product strategy document is a great tool to align people around the vision. A good product strategy document includes the following –

  • Problem Statement/vision: Describe the problem we’re trying to solve and, in the process, paint the picture for what we’re trying to achieve.
  • Principles: Clear guardrails that help us make decisions.
  • Strategy/Hypothesis: Answer the questions – “where do we play?” and “how do we win?”
  • Vision Roadmap: Outline what we’ll need to build in the coming quarters/years to solve the problem.

Of course, visionary product leaders don’t just write a great product strategy document and leave it at that. But, building a compelling vision with clear product principles and a strategy are the first step. We’ll cover the rest in later posts.

Managing the product: Once you agree on what to build, you put on your product manager hat to lead the process of building the product. In doing so, you take responsibility to balance the perspectives of the business (value), design (usability) and engineering (feasibility).

The primary tool product managers use is a product roadmap in some form. Again, we’ll cover roadmaps in detail at a later time.

The 3 axes of value, usability and feasibility are very useful as you think of skills product managers tend to build. A model (that builds on this and that) I’ve found helpful is that of “explorer, scientist, driver.”

  • Explorer PMs lead with design thinking. They are very curious about users and the market and build instincts for what matters to users and what doesn’t.
  • Scientist PMs lead with analytical/engineering expertise. They know their funnels and dig deep into their data to find insights and product improvements.
  • Driver PMs lead with business acumen. They’re great at moving the organization to build products that their customers are ready to buy and understand what it takes to go-to-market with them.

This model brings forth a couple of interesting insights. First, I’ve noticed that great product folk tend to have 2 of these 3 traits and learn to build teams that balance their weaknesses.

Second, different types of products tend to require different expertise. For example, B2C products tend to require more of an emphasis on usability and feasibility while B2B products tend to place more of an emphasis on value and feasibility over usability. My hypothesis is that this means PMs who prefer the explorer hat work better on consumer products while PMs who prefer the driver hat work better on business products. This also points to what folks need to do to learn complementary skills. If you want to build your explorer/design skillset, work on consumer products. And, if you want to work on building out your business acument, work on a B2B product.

Finally, the ability to lead with analytical insight is increasingly becoming table stakes.

A question for reflection, then – how much time do you spend wearing the management and leadership hats in your jobs? Does the ratio feel right to you?

Horses, cars, and the disruptive decade

There’s an interesting, oft-repeated, story among technology geeks about the difficulty of getting forecasts about disruption right. In the mid 1980s, AT&T hired McKinsey & Co. to forecast cell phone adoption in the US by the year 2020. After racking up what must have been a multi million dollar bill, McKinsey said the answer would be 900,000 subscribers.

They were off by a factor of 120x.

Currently, the forecasts from leading energy analysts and think tanks on Electric vehicle adoption shows ~30% adoption in 2040. Clean energy guru Tony Seba believes things will work out different. He predicts that all new cars will be electric by 2030. In addition, all new cars will be, at minimum, semi autonomous by 2030. And, finally, all new energy will be solar by 2030.

It is very hard for us to predict the future because we are wired to think about improvements linearly.

But, technology growth is never linear and generally occurs due to a combination of technological innovations that combine to make new things possible. The current artificial intelligence wave was not made possible by deep learning algorithms alone. Instead, it took parallel computing (GPUs) and the ability to process big data that made deep learning algorithms effective.

Energy guru Tony Seba calls this “technology convergence.” Convergence happens when a group of technologies come together to make new things possible.This is why technology adoption follows “S curves” — they don’t occur linearly.

To illustrate this, this was 5th Avenue in New York City in 1900. There is one car in the photo.

In 1913, it was hard to spot the horse.

My belief is that we will see the adoption of electric vehicles and solar accelerate in the next decade. Autonomous vehicles will follow closely. It is an exciting time as it will actually enable us to make significant progress to our climate change goals as a global community.

It is a decade that will be similar to prior periods of technology upheaval. For the longest time, the big oil cartel and coal powers will all seem to dominate.

Until they won’t.

Longer version of this note on Medium or LinkedIn.

Building reputation and incentives into marketplace products | Thinking Product

This is a “Thinking Product” post where I have more outstanding notes questions than concrete thoughts or a framework. I haven’t given the subject of reputation in marketplaces much thought. But, I thought about reputation this week as I took four Uber rides during a day of travel.

The driver side of the marketplace. I read an interesting post today titled “Give me my reputation back” in which Gavin Kelly lays out a case for portability of reputations. He writes –

The popular image of this segment of our economy is of free-wheeling, hyper-flexible freelancers who come and go as they please. Gig-workers can, after all, work through whichever platform they wish, for as long as they wish. The free-market distilled. 

Yet this is a partial account. It overlooks a barrier to mobility: the non-portability of their customer ratings and reviews. This is no side-show. You can’t, as Henry Ford said, “build a reputation on what you are going to do.” Ratings crystallise hard-won reputations; they are the passport to future earning power. Lose them and, regardless of experience or prior standing, you are pretty much starting from scratch.

This state of affairs is all the more odd given that, to avoid being treated as legal employers, platform-companies like Uber present themselves as mere online notice boards used by independent businesses to pick up trade. Strange, then, that these businesses can’t move these reviews with them.

I think this is a valid thought and one that is similar to the argument that we ought to be able to take our data on centralized platforms and move it. I don’t expect the gig economy companies to take action. But, our regulators need to pay more attention.

The rider side of the marketplace. Uber has been more upfront about the rider rating (i.e. the average rating you receive from drivers) and you now see it the moment you touch the options menu. I had a few thoughts here and questions here –

  • Rating manipulation: Uber says it doesn’t reflect individual changes to ratings, for example. But, it is pretty easy to tell. For example, I received three 5 star ratings and one 1 star rating on Wednesday. It was easy to tell because I saw an immediate change in my rating and the last change involved a large fall from 4.74 to 4.64. So, is it possible to manipulate your own rider rating? Here’s an example – what if I gave a 5 star rating and a generous tip to the driver right after I finish the ride? Wouldn’t the driver know immediately and reciprocate? Similarly, what if I “got back” at the driver who rated me one star by giving him a one star rating? Could Uber update ratings after a 24 hour period instead? (I did neither – but am curious)
  • Feedback for a one star rating: I was really curious about the reason for my one star ride. I was waiting for the driver, greeted him, stayed quiet until he needed directions within our apartment boundaries and got off. I wondered if the rating was a mistake and asked Uber support if there was a reason for this. But, Uber support just gave me a list of generic tips. What if the rating system persuaded both riders and drivers to give at least a line of feedback if they gave an extreme rating – e.g. one or two stars?
  • Introvert bias?: I would be really curious for studies on the correlation between introversion and Uber rider ratings. If I’m taking an Uber after a work event or a social occasion, the last thing I want to do is have a conversation with my Uber driver. But, an extrovert would be have differently and my hypothesis would be that extroverts have higher Uber ratings, on average.
  • Kids bias?: Another bias I’m more certain of is that against parents traveling with young kids – especially if the driver isn’t a parent himself/herself. How do you correct for such biases in these rating systems? Do you bother?

As we move toward a world with more marketplaces enabled by mobile phones, I wonder what the consequences of such rating and reputation systems will be. I’ve heard great things about an episode of Black Mirror where everyone is obsessed with their overall rating. What happens in a world where we feel constantly watched and judged?

While I was curious about ratings and reputations in marketplaces during my Uber day, I definitely felt judged when I got my one star rating. For some reason, I’ve had issues with the Lyft app on my phone over the past few months. But, the one star rating with no explanation pushed me to uninstall and reinstall the app so I could use Lyft next time.

I’ll be back with more notes and questions after using Lyft on my next travel day in the coming months. :-)

China and AI

One in 3 billion dollar companies is now founded in China. Thinking about what’s going on and what lies ahead inevitably leads to a discussion around the Chinese government’s focus on AI and why the discussion matters to us. I thought I’d focus on 3 notes I took away.

First, the Chinese government’s goals in investing in artificial intelligence are likely both around leading technology while also using AI to build the world’s most powerful surveillance state. Check this video on the Daily Mail’s website. (still below)

Second, we will all feel the consequences of living in a world where facial recognition becomes commonplace. Consider a few examples –

  • FindFace, an app in Russia, compares snaps of strangers with pictures on VKontakte, a social network, and can identify people with a 70% accuracy rate.
  • Facebook’s bank of facial images cannot be scraped by others, but the Silicon Valley giant could obtain pictures of visitors to a car showroom, say, and later use facial recognition to serve them ads for cars.
  • Even if private firms are unable to join the dots between images and identity, the state often can. China’s government keeps a record of its citizens’ faces (as detailed above); photographs of half of America’s adult population are stored in databases that can be used by the FBI. Law-enforcement agencies now have a powerful weapon in their ability to track criminals, but at enormous potential cost to citizens’ privacy.
  • Employers can already act on their prejudices to deny people a job. But facial recognition could make such bias routine, enabling firms to filter all job applications for ethnicity and signs of intelligence and sexuality.
  • For example. researchers at Stanford University have demonstrated that, when shown pictures of one gay man, and one straight man, the algorithm could attribute their sexuality correctly 81% of the time. Humans managed only 61%. In countries where homosexuality is a crime, software which promises to infer sexuality from a face is an alarming prospect.
    (Note: the researcher went on record to say this study was all about proving a point)

Finally, it is tempting to disengage from the futurist debates around AI. For most of us, we’re working hard at our jobs, then trying to put in a good shift at home and take care of our health along the way. Maybe, if we’re lucky, we get to have a hobby or two. On the side, we hear all this buzz about various billionaires fighting each other on the prospects of AI. Is it going to lead to humanity’s doom? Is it going to bring forth the utopia where we work on better kinds of jobs? Why should we care?

In a thought provoking essay on how to think about these futurist debates, Cathy O Neil makes a telling point (lightly edited) —

“For the average person there is no difference between the singularity as imagined by futurists and a world in which they are already consistently and secretly shunted to the “loser” side of each automated decision. For the average person, it doesn’t really matter if the decision to keep them in wage slavery is made by a super-intelligent AI or the not-so-intelligent Starbucks Scheduling System. The algorithms that already charge people with low FICO scores more for insurance, or send black people to prison for longer, or send more police to already over-policed neighborhoods, with facial recognition cameras at every corner — all of these look like old fashioned power to the person who is being judged.

Ultimately this is all about power and influence. The worst-case scenario is not a vindictive AI or Sergey Brin not getting to celebrate his two-hundredth birthday. In the worst-case scenario, e-capitalism continues to run its course with ever-enlarging tools at its disposal and not a skeptical member of the elite in sight.”

Well said.

Longer note on Medium or LinkedIn.

Centralization and Decentralization — blockchain and Kindleberger

We’ve seen a change in sentiment in the mainstream media around the centralization of power amongst the big tech firms. In response, a lot of the discussion in the hacker communities has been about the power of the blockchain and Bitcoin to shake up the current establishment and decentralize everything.

In this week’s Notes by Ada note, I share my skepticism of the purist utopian vision for the blockchain. I think the technology is without doubt ground breaking. Here’s an example of why I’m bullish about the technology –

  • Imagine you are a refugee in a new country. You have no official ID or financial history in the country — so, banks aren’t exactly queuing up to give you an account.
  • But, as a refugee, the government would like to give you some aid to get you started. However, they’d also like to keep tabs on that money to make sure you are spending it responsibly.
  • Without easy access to money, you could be stuck in bureaucratic hell for a long time.
  • Enter Moni — a prepaid mastercard service that links your mastercard to the blockchain.
  • Your prepaid card doesn’t need any bank. The government directly adds credit and knows they can track any issues in your spending via an incorruptible database on the blockchain.
  • Assuming you have good intentions, you use this money to get your life started, get a job and are hopefully on your way to building a better life.

This is not fiction. The Finnish government is already testing this with asylum seekers and the United Nations is exploring using this technology for one billion people worldwide who have no legal identification. Powerful stuff.

But, we often confuse the technology breakthrough with its potential second order implications.

Technology breakthrough: A blockchain is a decentralized network with information. The breakthrough in the blockchain is in the ability to have a decentralized database that is not owned by anyone. This was not possible before and means we can now have shared incorruptible databases.

First order implication of the technology breakthrough: Databases controlled by middle people (e.g. banks) were sources of trust for various transactions in the economy. Now, you don’t need to have these middle people. Instead, you could, for example, execute a pre-agreed contract via the Ethereum blockchain. As long as certain conditions are met (e.g. money is transferred to account B), ownership can be transferred too.

Example question about its second order socio-economic implication: Blockchains could render important pillars of our financial system obsolete. Maybe this will remove the need for banks, central banks and governments?

In the many discussions about blockchains, I see people mixing the technology breakthrough and its potential second order implications. Here’s the deal — the breakthrough and its first order implications are here to stay. But, all of the implications being dreamed up right now are not necessarily going to pan out the way many of the purists imagine it.

More on Medium or LinkedIn. Medium says it is a 13 minute read – so you have fair warning. :-)

Connecting aspects of great products and great product strategy | Thinking Product

I started the Thinking Product series by sharing my hypotheses for the 3 core aspects of great technology products and great product strategy.

This evolving theory, like all theories, is necessarily imperfect. There’s a ton of nuance that goes into building technology products – e.g., products for enterprises and consumers are designed very differently. But, theories are important because they end up simplifying things. And, that’s particularly important as we begin exploring a new topic. I started this series with this image.

And, over the past weeks, we’ve explored each of these pieces. We began with aspects of great products.

  1. Nail job to be done
  2. Well designed
  3. Sticky

Then, we looked at great product strategy.

  1. Growth – i.e. bringing new users
  2. Onboarding – i.e. converting them to power users
  3. Retention – i.e. making them stay (with a note about the dark side of engagement)

For each of these, we explored 1-3 key questions that should help drive our thinking.

So, today, I wanted to bring this all back in an overview image of sorts. There’s a strong parallel between the core aspects of great products and great product strategy. That is by design of course – they exist together and feed into each other. So, when we look at them together, we arrive at the following 3 core principles –

  1. Find a niche segment of users with a problem and focus on solving it. (Nailing job-to-be-done and growth)
  2. Use the onboarding period to convert new users to power users. (Delight to use and Onboarding)
  3. Continuously improve ability to surface and drive value. (Stickiness and retention)

We have many exciting topics to explore as we dig into the nuance. But, these will likely serve as the building blocks through our journey.

For the next few posts, we will take a break from products and product strategy and move to discussing my hypothesis for the building blocks of great product management and product leadership.

 

Food and technology

We’ve made a lot of progress in meeting the world’s food needs.

We’ve done this by using more technology to increase the yield of our lands.

While the benefits have not been as evenly spread as we’d have liked, things are getting better.

But, we’ve done a poor job in some areas – especially in our treatment of animals.

I think we’re going to see tremendous progress in food in the coming decades. We’re going to see more Soylent inspired alternatives to unhealthy food. Drones and artificial intelligence are going to help us further improve land yield. But, I’m most excited about two other innovations.

The first is large scale vertical farming that start ups like Plenty and InFarm are working hard on. Vertical farming can greatly improve yield, can be done indoors and uses sensors to optimize the growth of plants.

And, the second is lab grown meats from the likes of Memphis Meats. Once we figure out how to produce this on a large scale, it won’t just be a more humane method of meat production. It will also be significantly better for the environment. Studies show that clean meat could potentially be produced with 96 percent less greenhouse gas emissions, 45 percent less energy, 99 percent less land use and 96 percent less water use than meat made through animal agriculture.

In a remarkably prescient note, Winston Churchill had predicted this 80+ years ago.

It has taken us a while. But, I’m optimistic we’ll get there soon.

(A longer, more detailed version of this note is on Medium or LinkedIn as part of “The Notes by Ada” project)

Mesh Wi-Fi systems

Traditional Wi-Fi set ups don’t scale well with multiple devices. You lose a lot of speed for each step you take away from the router and have to install extenders if your home happens to be longer than it is wide.

And, let’s face it – extenders really suck. You need to create a new Wi-Fi network and only get pitiful speeds even after doing so. You’re likely reminded of this every day – especially if you live far away from family and use video calls to stay in touch.

Mesh Wi-Fi technology gets rid of all these traditional set up limitations by replacing the hub and spoke router model with a mesh that blankets your home with the high speeds that you’d get using a LAN cable.

This video does a great job explaining why mesh Wi-Fi is great.

We live in times when having good internet access is way more important than having cellular access. And, a mesh Wi-Fi system is a game changer. We pay for a 100 MBPS connection and I couldn’t believe my eyes when I saw a 110 MBPS speed outside our home (!) – where we’d never have been able to connect to our extender even.

A real game changer. I couldn’t recommend it strongly enough.

Retention – feat the tragedy of the commons | Thinking Product

There are two questions to ask when we think of retaining users  –

  1. Are we continuing to drive value for our users via continuous improvement or better surfacing of value?
  2. Are we reminding users of the value they can drive?

Both these questions are important but drive contrasting approaches. The first question focuses on improvements within the product while the second is essentially a notifications/reminders driven strategy.

1. Are we continuing to drive value for our users via continuous improvement or better surfacing of value? 

As mentioned above, there are two ways we drive value here. First, we keep making improvements within the product. For most enterprise products, this continual part of the roadmap is typically driven by customer feedback. For consumer products, it is generally driven by rapid experimentation and copying successful features from other consumer products. And, while consumer product management is arguably more gut driven than enterprise product management, great consumer products are often molded by their users. A seminal example of this is the story behind Twitter’s hashtag.

The first hashtag appeared thanks to Chris Messina, a user who suggested using # for groups. Interestingly, he got inspiration for this from another social network (perfect illustration of my point above) – Jaiku- and from tags on Flickr. In time, Messina’s idea spread and Twitter, after a period of resisting it, came around to it.

Second, most products generally do a poor job surfacing value to users. That’s because our assumptions about the most valuable aspects of our products are rarely what our users value. This is where analysis of usage data goes a long way in improving the product.

Overall, however, this question is all about continuously improving our products to drive value for our users and is the surest proxy for long term product success.

2. Are we reminding users of the value they can drive?

Any savvy mobile product manager today has a sophisticated notifications strategy. She has a point of view on when to surface a push notification versus a badge and when to use email to drive users back to the product. This isn’t limited to small companies trying to get traction. We see the largest companies use notifications a fair bit to drive usage as well. For example, Facebook gets very aggressive with email notifications if you don’t visit your profile or feed. I use Facebook primarily for my blog’s Facebook page. But, that engagement clearly doesn’t count for Facebook – so, I receive an email reminding me of what’s going on on Facebook every day.

Twitter, recently, shifted its notification strategy pretty drastically as well. This was what a notification email from Twitter looked like until the first week of July.

And, this is what happened once they shifted strategy.

Now, I have to click on “Take a look,” head to Twitter to see what happened. Twitter has been having difficulties with user growth and this is clearly a metric mover. The big question with such changes, of course, is whether it will continue to be a metric mover in the long run.

Notifications are important. When done right, they are good reminders of the value a product can drive for the user. However, too often, they are used as short term metric movers that only end up annoying users. The problem with notifications is classic “tragedy of the commons.” If a company has 5 teams who all want to drive up their numbers, how long before they all surface frequent notifications and compel the user to turn off notifications?

The bottom line – a retention strategy that is driven by notifications is a poor retention strategy. I think retention strategies work best when 80% of the effort goes into driving real value and the remaining 20% (and not any more) focuses on reminding users via notifications.

If our users aren’t listening to our many reminders of how great we are, maybe we ought to revisit our assumptions.