Purposeful Business

Warby Parker, Toms, and several other well-known consumer facing companies have emerged with purpose at the core of their missions and cultures. Meanwhile, the common objective of business is to maximize profit and shareholder interest. Does an intersection between the two exist? This was one of many questions raised at a recent group lunch on purposeful business. It was a fascinating conversation with much more left to discuss, so I wanted to share a few of the interesting areas that we explored:

One person made the point, which I agree with, that industry structure and profitability define a company’s ability to “give back.” In a competitive industry with thin margins, companies cannot afford to give anything away. Conversely, companies in industries with monopolistic characteristics (e.g. eyewear) and/or high margins (e.g. branded apparel) can redirect dollars to a purposeful cause.

But what does "giving back" even mean? Companies in highly competitive industries can still pursue purposeful missions if consumers preference such behavior. Purpose, therefore, would not come at the expense of a company’s bottom line. If this is true, then ‘purpose’ is a sound business decision and 'giving back' is a smarter, more profitable strategy. Purpose provides differentiation in an otherwise commoditized industry.

To what extent do the monetization of and strategy behind ‘purpose’ blur the distinction between profit and purpose? It is hard to think of companies that have not benefited from adopting a social mission. Though difficult to measure, the purpose initiatives of Warby Parker and Toms surely have helped them grow. Relatedly, how should we understand the difference between a B-Corp and a C-Corp? Is it that they have different objectives or that the source of their differentiation and strategy are of a different nature?

How does the monetization of purpose affect our understanding of what is authentic and what is not? Does the distinction between authentic or inauthentic purpose matter? Warby Parker and Toms are paragons of the B-Corporation. They are viewed as authentic brands, in part, because there are so few examples of companies that do this. I think their authenticity emerges because profit is not the sole driver of their purpose. They certainly have monetized purpose, but it seems to go beyond pure profit. I have defined authenticity as not the opposition of making money, but rather as the motives underlying purpose. Disaggregating motives behind corporate initiatives is hardly a science, which is why branding and communications will have heightened importance in the profit-purpose world. For better or worse, customers have an innate distrust of big companies, even if their startup counterparts have the same motives.

If consumer behavior shifts to favor socially-conscious companies, companies will accordingly adopt social missions at scale. How does ubiquity change our understanding of authenticity?  

How do we measure the impact of a social purpose driven strategy on a company’s bottom line? By how much did it increase sales? How did it improve employee retention?

Should 'purpose' driven companies receive preferential tax treatment? How do we define purpose? Is it relative? That which may have been purposeful a decade ago may be mundane today. 

What role does regulation play in nudging consumer behavior?

Machine Intelligence: Discussion Points

Our discussion on machine intelligence spanned short term opportunities and long term disaster scenarios. Some of the points we discussed are included below.

Intelligence is not only defined by the speed with which answers are determined, but also the quality, scope, and nature of the cognitive skills. 

The application of machine intelligence to every industry is a hot theme across organizations of all types. The sudden adoption of it may not be realistic however due to limits on technological capability and human acceptance. Technology will improve over time, but will psychological barriers (e.g. how could a computer drive my car?)? Are younger generations who have grown up around technology more welcoming? Are there certain fields (e.g. political decision making) that we would never want a machine doing?

Companies have tried testing customer appetite for machines in low fidelity ways such as having a human do the processing behind the backdrop of an 'intelligent machine.' Beyond being an efficient way to test a concept before deploying resources to build a product, it also helps collect data that can be used to train the machine and develop data sets.

In charting the adoption of machines, we have moved from human-only to more machine-assisted / human-assisted approaches. For example, a plane is in large part auto-piloted with humans there to assist where necessary. Will this shift to purely machines eventually? Will we accept this? How do we accept this? Will someone have to prove that the error rate for machine-only approaches is lower than machine+human? How does one actually conduct that test?

Another axis to evaluate adoption is based on complexity and harm. High volume tasks that are low risks seem ideal to tackle first. High volume allows for sufficient machine training, and low risk implies that machine errors have minor consequences. Scheduling meetings and booking travel are examples of applications that meet these criteria. 

Existing approaches seem to be very narrowly defined, with programs applied to small micro tasks (e.g. travel booking, calendar planning, etc.). These are difficult to build comprehensively, and so there is likely going to be a growth in companies offering one-task solutions. Eventually it will be difficult for a consumer to manage these separately, and “dispatcher” layers (e.g. command centers like Siri) will be built on top.

Talent will be distributed across small startups, large tech companies, in academia, etc. Big companies do have advantages here, including ability to poach and pay top talent, willingness to fund research, access to necessary data sets, competition and a business need to develop machine intelligent solutions, etc. Big companies are likely to be hot spots for this activity, and they are trigger happy on acquiring new companies in the space ('acquihire'). As an investor in one of these startups, you may determine whether to accept or reject the offer based on the standalone prospects of the startup (e.g. can and are they making money?).

From where will general intelligence come? Will it be from a team that is working on cracking general intelligence? Could we accidentally come across the discovery? Imagine someone were building an intelligent machine for an application in agriculture, for example. Could his machine unexpectedly be an expression of general intelligence? Perhaps the elements required to unlock this are not all that far off from where we are, in other words.

Intelligence is not just about the 'thinking' or 'brain' aspects. It requires sensing and learning. It requires the 'body' as well.

Machines and machine intelligence will replace jobs? Will those jobs be replaced? If so, with what? If not, what are the consequences? What does automation and machine intelligence imply for the nature of work? Free time? Social life? Family dynamics?

Long(er) term outlook: Are the threats of superintelligence fact or fiction? There is no consensus about the end state but many concerns are valid and should be openly discussed. Machine intelligence is currently limited to niche applications, but as it develops into general expressions of intelligence, the rate of its development will skyrocket and almost immediately become superintelligent. While machine intelligence promises the hope of cracking the code on difficult problems we need solving, computer programs with great intelligence may dangerously consume resources when solving problems. Will machine intelligence necessarily evolve exponentially? Are we ignoring the fact that the difficulty in 'unlocking' the next step of innovation also rises over time.

The Next Billion: Discussion Points

I was recently part of a fascinating discussion about what has been termed 'the next billion' describing the next wave of people to come online (predominantly from emerging markets) and the opportunities/challenges this presents. Sharing the main points below: 

There are several issues we discussed including connectivity, access, devices, usage of devices and data, monetization strategies for apps, ROIs for different players in the space, etc.

+ Connectivity: Balloons, mesh network of buses (avoids real estate issues), satellites, etc. What return can be expected from these investments and will they be successful? Internet is a utility, and charging for infrastructure may be difficult. Therefore, is the set of solution providers limited to large companies (e.g. Google, Facebook) with the capital resources to fund connectivity at a loss in exchange for some hope/ability to monetize subsequently in the form of apps?

+ Devices: Costs matter in this market, hence the race to deliver lowest cost devices. Will Apple be sidelined? Can they avoid brand dilution while foraying into these markets? Is open source more likely to win than closed systems?

+ Usage of devices and data: Usage is very different in the developing world for a couple of reasons. First, there is less income to spend on data and in-app purchases, affecting engagement and product design. Should all apps be lite versions of their developed world counterparts? Can we utilize cheaper ways of downloading or sharing data?

Second, because of the lack of financial infrastructure (e.g. credit cards), the carriers hold the keys. Identification and payment occurs at the carrier (e.g. when phone is issued, when minutes are topped up), thus app stores are carrier controlled. Implications for Google and Apple? Implications for apps’ ability to monetize (opportunity costs are not just financial, but also the hassles associated with running out of minutes). Financial infrastructure is very much an important driver here. mPesa is an example of carrier (Vodafone) power in Kenya.

+ Monetization: A few ways to pay for apps: money, usage (e.g. data), attention (e.g. ads). Ability to charge for apps / in-app upgrades / use significant data is limited due to limited disposable income. Attention is premised on subsequent ability to monetize. Advertisers effectively subsidize data costs on behalf of the consumer (the same thing happens here) in exchange for leads and purchases. But the returns have to be realized at some point. When? Will users with limited disposable income respond to heavy advertising in the same way?

+ Correlation between GDP / GDP forecasts and the value of a user. “A user is a user” is not true. Developed world users are very different than developing world users. Even within a country this is not true depending on how wealth accumulates and is distributed. Per user metrics (related to valuation and otherwise) should adjust for this.

+ At first, many companies are likely to focus on logistics/infrastructure (e.g. the Uber / Instacart type companies) in emerging markets than heavy advertising models.

1099 Economy: Discussion Points

Generally speaking, we do not make enough time to discuss topics deeply and understand the fundamentals of the world around us. For that reason, I helped put together a group of people interested in exploring different topics through regular discussion. Our first conversation was about the 1099 economy. We did not (and likely will not) take proper notes, but I wanted to share a list of topics and questions we discussed because it was so interesting.

+ Leveraging Free Time - Is time 'free' out of desire or because no other opportunities exist? How will this change over time? As the economy improves? What is the real trade off between stability and flexibility?

+ Birth of the Uber-preneur / Flexibility vs. Exploitation of Labor that can’t get full time jobs with benefits

+ Regulatory risks that these companies face. From an investment perspective, how do you over come this? Adapting policy to match new technologies,

+ Benefits – who pays the burden? Is it fair to skirt the line and call your workers 1099s instead of W2s? As more people work as 1099s, does the burden fall on society if people are not covered (insurance), for example? How will W2 benefits change over time as a result?

+ Who represents the interests of 1099s? Unions across 1099 companies? Every man for himself?

+ Should Uber and other 1099 players be required to pay benefits if the time a worker spends on their platform exceeds a set amount of time (e.g. 20 hours)?       

+ Lack of loyalty in workforce and implications of maximal size of 1099 based companies

+ What skills are 1099s developing? Room for professional mobility. Do workers care?

+ What opportunities exist across all 1099 companies?

+ Does scale matter for these companies? Regional (yes). National (maybe for Uber, but not for most others)

+ Currently disrupting the service economy. Will it move into the knowledge economy soon?

+ Is automation the natural extension of  outsourcing to the 1099 market / abroad?

+ One of the reasons why firms exist is to reduce transaction costs between people. Technology changes this equation. Can this be extended to all divisions of a business? Can all functions be outsourced? What is the value of institutional memory? How can this be saved while outsourcing? When is it smart and not smart to outsource?