… Innovating

One of the most important takeaways from school is learning how to think. We go in thinking that we will be taught knowledge, but really what we need to be able to do is take in knowledge and do things with it. We need to analyze if we believe it is true and properly contextualized. Can we convince with it, create new ideas with it, or critically evaluate how truthy it is, in the famous word of Steven Colbert.

One of my favorite classes in business school was Technology Strategy. At its core it taught us how to think. We were taught critical tools that can be used to evaluate and come to conclusions and develop new ideas and concepts. One of the tools we wee taught is a type of trend analysis. Applying it to topics broad and narrow, it can quickly succinctly coalesce many different elements of a topic into a few manageable categories and then allow the user to make trend predictions based on what they observe in the field.

Defining the Topic

The topic can be broad or narrow. I’ve seen trend analysis applied to concepts as broad as work or food service and as narrow as Star Wars. Whatever you want to analyze, go for it. I recommend thinking bigger rather than smaller. That allows you to capture more and not miss something that falls outside the scope of your topic but is related either directly or indirectly.

Step 1: Manifestations

Once you have your topic, start jotting down a list of everything and anything going on in the field. If your list gets long try categorizing these “manifestations.”

Here’s a quick example:

That took me about sixty second. With some more concerted thought and organization it is possible to have an extensive list of what’s going on in the world of beverages, under some broad categories like Products, Availability, and Consumer Preference. Also notice how I defined the category as Beverages, not alcoholic beverages or non-alcoholic beverages. This allows me to explore the interplay between these two types of beverages, and also go deeper into social and cultural trends.

Step 2: Establish Axes

I believe this step is the most important, and can require some refinement after you initially define your axes. Establish two axes that broadly cover the topic at hand. They should be vague and broad and allow for a wide variety of trends to populate a spectrum.

Topic: Beverages

Axis 1: Consumption

Axis 2: Preference

The axes are continuums, but they need poles. The poles are the logical extremes of each axis and they help to constrain the eventual trends that emerge.

For Consumption: Non-Descript, Public

For Preference: No Choice, Endless Choice

Again, this is my cursory attempt at creating an example, but jotting down ideas early in the trend analysis process is important. You do not want pre-conceived notions or the opinions of others to taint your analysis too early. These axes and their associated poles can be refined later, but it’s important to quickly establish some boundaries for the analysis.

Step 3: Quadrants

With two axes, four quadrants naturally emerge. Give these quadrants descriptions based on their relation to the poles. The descriptions do not need to be technical, although they can be. They can be very matter of fact (as I do below) or more succinct.

 

Step 4: Relate the Quadrants

It will help you and your audience if your quadrants are easily relatable. You’ll need to get creative here, but it’s fun. Choose something from pop-culture or history and assign each quadrant a category from what you choose.

Relation: Car

Quadrant 1: Beverage choice as a fashion statement <> Unique Cars

Quadrant 2: Beverage consumption as status <> Sports Cars

Quadrant 3: From the water cooler right back to your desk <> Mass Market Imports

Quadrant 4: Customized Thirst <> Classic American Brands

 

Step 5: Name the Quadrants

Based on the categories assigned to each quadrant, give it a name corresponding to something from the real world in that category.

Unique Cars: Tesla

Classic American Brands: Chevrolet

Mass Market Imports: Honda

Sports Cars: Lamborghini

Feel free to also assign funny taglines or phrases associated to each quadrant and category. For instance, “Honda: The Civic – Everyone seems to have one, but you drive right on by them.”

Step 6: Sort the Manifestations

You now have a well-defined framework to begin to truly analyze the topic and determine what is to come. Take your list of manifestations and roughly sort them into the quadrants.

Tesla

Kombucha – “Questionably good for me, but I love it when people ask me what it is”

In-a-Can – “It tastes just as good in a can and I want you to know that I know that”

Flavored Sparkling Water – “Don’t you dare take the last pamplemousses!”

Lamborghini

Sessionable – “I’m here to be seen and need to be able to keep up all day”

Spiked Sparkling Water – “I could just make a vodka soda, but I prefer to pay more so I can hold a skinny can in my hand”

Honda

Seltzer – “Low calorie bubbly goodness is enough for me”

Free at Work – “I’m glad that they want me to be happy and hydrated beyond coffee and the water cooler”

Rosé – “I like it, ok. I don’t care if the wine snobs think it’s crap.”

Chevy

Fermented beverages growing in popularity (functional beverage)

Craft/Micro – “I just need better, plain as that”

Naturally Low Calorie – “I get what I need with no unnecessary frills”

With your trend landscape clearly defined and all of your manifestations organized, all that is left is to decide what trend (or quadrant) will prevail. Determine this based on the emerging manifestations and what you believe will prevail.

For beverages, are you going to go with the Honda? Probably not! The Chevy, Tesla, and Lambo all offer appealing attributes, so you can choose one of those quadrants and run with it or pick and choose from all three of them.

Final Step: Hope Your Right

If you want to put a confidence interval around your predictions, it’s infinite. You may be right, and you may be wrong. Experience certainly helps. This technique really just serves to organize thoughts and hopefully clear away some of the clouds surrounding what might be going on within a certain field or topic.

For me, I’d put my money on the Tesla-beverages. People are always on the hunt for new flavors. Coca Cola is launching new flavors of Diet Coke. Trends come and go, but consumers’ appetite for new flavors seems to persist. However, they want choice, and they want people to know that they are making bold choices. I’d put my money on customization and the ability of people to publicize their choice. Imagine a vending machine or kiosk where you could select what exotic flavorings you want in your bottle of Coke or your six pack of seltzer, it mixes it automatically for you and then puts a vibrant label on the containers. Maybe you could even make your selections via an app and it automatically posts to your Instagram or you Snapchat with your personalized flavor mix.

… Creative Economy

This is NOT an article about how AI, automation, and robotics are coming for non-knowledge jobs. That is happening, but this is an article about how AI is coming for traditional knowledge economy jobs too, and how it will change our economy and society, and I think for the better!

A few days ago, I was watching NCIS with my mom (it’s always on some channel). As per usual, a clue came in, and with a few tips and taps on a computer they had traced it back to its source, cross-referenced it with a database, and sent the results to the field agents’ phones. In all, the scene lasted about 30 seconds. My mom said, “How can they do they so fast and by only typing? It takes me 20 minutes just to remember my password.”

NCIS is dramatized television. There are very few, if any, people or organizations with that level of computing sophistication and coding skill. However, it’s close enough to how we think computers work to be believable. More remarkably, we’ve at least thought we’ve been at the cusp of this level of computational sophistication for nearly 20 years. I remember watching 24 with my dad in the early 2000’s and very similar tip-tap-success was going on back then in that show. Yet we all know, by sheer fact of our daily lives, that working with digital information is cumbersome, time consuming, and does not always end in success.

Societally, we’ve convinced ourselves that we are living at the leading edge, if not the pinnacle, of the Digital Revolution. The advent of AI is just around the corner, and our 40+ years of digitization are poised to pay off into more leisure and more accurate and easy computing for all of us. On the contrary, I contend that we are merely at the beginning of the Digital Revolution, and there are still many years of work ahead of us before we can enjoy the tip-tap-success that we see on television.

Data remain very compartmentalized. Throughout the digital age, companies, governments, and other entities created databases, data protocols, and computing and data languages ad hoc. Even within large organizations different databases exist to house purportedly the same data, and sometimes these databases contradict each other. Furthermore, data are often user generated, so discrepancies propagate over time. Remember when they rolled out the electronic medical record (EMR) at your office and you could not find the field for pulse until someone told you to look for “heart rate”? And is the accounting system in dollars or thousand dollars? These are the discrepancies that real life NCIS confronts when they perform data analysis, and it takes far more time than we seem to think to sometimes get less than clear results.

A few weeks ago, I met my friend at a food hall in Midtown. I couldn’t help but look around and try to imagine what everyone did for work. Most patrons were young workers in business casual. Being Midtown, I imagined a lot of bank and finance workers, with a smattering of consultants, business people, and people in media and publishing. I know how they spend their days. I used to be a finance consultant. They spend all day pulling together data from disparate sources and collating them into something that their superiors can use to make decisions. “Where’s the data?” “Who has it?” “Is it any good?” These were my daily lines. Most work time for these “Midtowners” is spent replicating data, models, and results. Much less time is spent deciding what they make of results and numbers. I had entire projects where I figured out how the data came together and simply documented the process. Despite all of our advanced statistics and calculus classes, most people in these “Midtown” jobs are just performing basic arithmetic, if that.

 

However, there is reason to believe that the Digital Revolution will soon be accelerating. Emerging innovations like blockchain and internet of things (IoT) are streamlining the collection, storage, and sharing of data. The rate at which we generate data is accelerating, so having clear protocols for the sharing of data is key if we want to continue to move up the digital curve. If we continue to generate astonishing amounts of data but do nothing about their balkanization then making connections between data – the tip-tap-success we see on NCIS and 24 – will be more and more difficult, not easier, as we often assume as default for digital processes.

Over time, as fiefdoms of data come crashing down and the Digital Revolution truly does bring us closer to tip-tap-success, all of these Midtowners in clerical and finance roles will find themselves with a lot of free time on their hands (so will the consulting firms). Banks will finally be able to cut lose the throngs of high paid workers that spend their days knee deep in Excel, jockeying numbers for the few actual managers in firms whom make decisions. Managers will be able to easily retrieve <tip> the data they need, perform some manipulations as they see fit <tap>, and then make decisions based on their results <success>.

This Digital Revolution is a necessary prerequisite for the full advent of AI. Data are the fuel for AI. Machine learning algorithms require vast quantities of data, and preferably data that update in real time, so the algorithms can truly learn and improve upon themselves. As it stands now, all of the world’s data are too balkanized for machine learning algorithms to pull them in and turn them into the true putty that will lead to cognition-level algorithms. However, when it does, it’s not only the Midtowners that need to worry about their jobs. Managers – true actually make decisions of import managers, will begin to see their judgement challenged by algorithms. When there is little uncertainty in what has transpired in the past and what the forecast prognosticates for the future, there is little room for what we now think of as managerial judgement in decision making.

When I discuss this future with business people they see it as a hard pill to swallow. This is a natural response, but I’m apt to point out that there are excellent companies that are already working on AI for managerial decision making. As consumers, we are most familiar with Alexa or Google Home as voice-enabled personal digital assistants. However, Salesforce has Einstein, which helps sales and marketing teams with routine tasks. They’re already working on more advanced business applications for Einstein, and before long you’ll be able to ask Einstein, “Should we acquire a company or build a new capability in-house?” We are taught analytical frameworks to solve these questions in business school, so once we have the requisite data packaged into something that a machine can consume, why couldn’t, and why shouldn’t the machine answer the question for us (or even alert us to what questions we ought to be asking)?

[Salesforce CEO] Benioff even told analysts on a quarterly earnings call that he uses Einstein at weekly executive meetings to forecast results and settle arguments: “I will literally turn to Einstein in the meeting and say, ‘OK, Einstein, you’ve heard all of this, now what do you think?’ And Einstein will give me the over and under on the quarter and show me where we’re strong and where we’re weak, and sometimes it will point out a specific executive, which it has done in the last three quarters, and say that this executive is somebody who needs specific attention.”

 – Wired

I am not saying that the clerical workers of today are overpaid data jockeys not worth their weight in avocado toast. Nor am I criticizing their managers for hiring them and needing their assistance. I recall a particularly large project from my old consulting firm. It required an army of fresh-out-of-college consultants to comb through loan files and flag missing documents and other discrepancies. The work was tedious, but it required attention, occasional analytics, and downright intelligence. The young consultants did not find it particularly rewarding, only repetitive, and the bank certainly did not want to be paying the millions of dollars for error identification. Nevertheless, we still live at the dawn of the Digital Revolution, and this work was a necessary evil for everyone involved in the project. With AI far more popularized now than it was only ten years ago, nearly everyone can see the promise of AI in automating audit work like that. Nevertheless, it is still not a reality.

When the Digital Revolution does usher in true machine-powered cognition, I foresee banks, investment houses, insurance companies, and trading businesses, just to start, operating drastically differently from what we are used to today. Midtown will be cleared out – both the food halls and the corner suites. A few managers will rely on AI for most decision making, and the remaining workers will be more creative in nature, delving into new and emerging business models, or possibly still toiling in the age-old task of sales (with Einstein’s help, of course).

I hardly see this as apocalyptic for our knowledge economy. Yes, Midtown will be desolate, but Brooklyn will be bustling. Suit peddlers will be out of business, but hipster boutiques will be teeming. The advent of AI will be the advent of what I call the Creative Economy. Today, the creative economy is the corner of our economy focused on arts and leisure, design, media and entertainment, performing arts, fashion, and a smattering of other cottage industries.

Although people will lose jobs (or fewer new jobs will be created in the knowledge sector), our economy will be operating more efficiently. This relieves pressure on prices and leaves employed people with more disposable income. As an economy we can then deploy this disposable income into new interests, hobbies, passions, and arts. With more of the world’s most intelligent people free to devote themselves to their passions and leisure, there will be an explosion in creativity and creativity-as-commerce. Rather than focusing on high paying jobs devoid of meaning (if anyone who spends their whole day collating data says they “love their job,” they are lying), far more of our collective intellect can be dedicated to creative pursuits. We can create more content of an intellectual nature and consume more downright leisure.

I see four creative sectors staking claims for themselves and growing rapidly alongside AI:

• Pure Leisure & Arts
• Digital Arts
• Creative Enablement
• Physical-Digital Interaction

Pure Leisure & Arts

We already consider leisure & arts as very virtuous pursuits, although one that relegates all but the luckiest artists among us to being the perennial starving artist. These arts include writing, painting and drawing, film-making and acting, music, dance, fashion, gastronomy, architecture, other forms of literature, performing arts, and visual arts. With more time left to pursue the consumption of leisure or the practice of these arts, the traditional arts will proliferate.

Digital Arts

Digital arts will be one of the fastest growing new creative pursuits. With more immersion in the form of augmented reality and virtual reality (AR/VR), there will be immense demand for graphic design, 3D design, animation, and VR environment design.

Creative Enablement

 

All of this art begs for software in which it can be designed, rendered, mixed, shared, and experienced. Today we have a knowledge economy, and Microsoft, along with the likes of SAP and Oracle, dominate knowledge software, so they are some of the largest companies in the world. In the future, the creative economy software manufacturers will be among the largest companies in the world. Design software by companies like Autodesk and Adobe will dominate our daily lives, and those companies will be vaulted into the Dow 30. There are even companies that are merging many technologies, from teleconferencing and virtual reality to design and architecture. They are creating software that will allow remote teams to interact in virtual reality environment and collaborate on design and creation real-time. Imagine a team of architects, all over the world, being able to virtually fly around the buildings they are designing and make changes together based on each other’s comments.

Physical-Digital Interaction

We will continue to live in a physical world (I am not predicting The Matrix), and manufacturing, engineering, medicine, and other physical sciences and fields will continue to be of the utmost importance. While less creative in nature, companies that bridge the physical-digital divide and allow AI and automation to assist in these fields will be extremely valuable. Importantly, they will continue to fuel the creative economy by freeing workers from tasks that can be performed by computer and machine, allowing them to more freely contribute to the creative economy.

3D Printer in action

The shift from a knowledge economy to a creative economy will have to be supported by the educational system. Training for trade and business will diminish. Instead, there will be more learning how to learn. Liberal arts will flourish, alongside an emphasis on mathematics and statistics, engineering, biology and medicine, and hard sciences. Coding, which is already moving to the mainstream of education, will gain even more importance, and humanities and the arts will once again be respected and valued fields of study. Education will also be prolongated and emphasized throughout one’s life, not just at its beginning, and there will be more economic emphasis on education. The creative economy will also self-reinforce the education sector by more effectively immersing learners in their education and create new and innovative ways to learn. If we can align our education system with the promises of the future and coordinate our data protocols for our collective well-being, the future will be bright, colorful, and fun and filled with enjoyable work and pleasurable leisurely pursuits.

 

… Servicization

This article is part of a series, The Seven Innovations That Will Change the World.

Servicization is a recent and growing trend. So recent, in fact, that Microsoft Word is trying to convince me that it does not exist by underlining the word in red. Typically, companies make products with a particular function. After selling the product, the company wipes its hands and moves on to its next customer. For instance, Whirlpool sells washing machines, and the function of these machines is getting clothes clean. Save warranties and guaranties, Whirlpool has no other contractual obligations to the buyer once the company sells the machine.

Servicization focuses on what consumers really want: the function. Instead of selling washing machines, Whirlpool could sell clothes washing as a service. The company could install washing machines in consumers’ homes and charge consumers based on how much they use the machine. The machine could have an attached smart meter that transmits the number of washes back to Whirlpool. The company would send you a monthly bill, just like your electric utility. Whirlpool, remaining the owner of the machine and simply the purveyor of a service, would be responsible for maintenance and would have an incentive for the machines to be reliable. After all, they would not get paid if the machines break down and the consumers can not wash their clothes.

Whirlpool could actually increase its revenue if it changes to a servicization model. Currently, washing machines cost around $2,000 and can last around 11 years (Consumer Reports). In contrast, a wash at the laundromat costs around $2. If a family of four does 500 washes a year, that’s 5,500 washes over the life of the washing machine, or the equivalent of $11,000 at the laundromat. If Whirlpool were to price its new service at par with the laundromats and Whirlpool’s weighted average cost of capital is 10%, then the value to Whirlpool is $7,145.

The consumer benefits in a servicization relationship because they do not have to invest in a depreciating asset and recoup the value of their purchase over time by washing their clothes. Besides the increase in revenue, Whirlpool would receive a large tax benefit as well. Since they do not sell the washing machines to the consumer (it just sits at the consumer’s house), they can keep the washing machines on their books as a depreciating asset and offset their income with the depreciation. Over the last four years Whirlpool’s average cost of revenue exceeded $17 billion. Even if only 5% of that is taken as depreciation on an annual basis it would have a sizable, if not transformational effect on Whirlpool’s tax bill.

In addition, new people would be able to afford washing machines in their homes, since they only pay by the wash, not for the whole washing unit up front. In this scenario, production would increase, and the company would be more resilient in downturns because people are more likely to continue washing their clothes and paying a small amount each wash rather than making large ticket purchases. Whirlpool benefits, its employees benefit, and its customers benefit as well.

For me, washing machines are a simplified example of servicization, but there are countless more applications of servicization beyond home appliances:

PRODUCT – SERVICE or FUNCTION

Computers – Digital computation

Automobiles – Mobility

Clothing – Warmth, style, status

Food – Nourishment, enjoyment

Copy machine – Printing

Aircraft engine – Thrust

Lightbulb – Illumination

Furnace – Heating

Voice-enabled digital assistant – Connectivity, convenience

Books – Knowledge, information, stimulation, storytelling, graphics, enjoyment, posterity of knowledge

Health care – Health, wellness

Hard drive – Storage, posterity, retrieval

Circuit breaker – Current, safety

Some of these products have already been servicized, but I can conceive of some sort of servicization scheme for all of them. Xerox has servicization contracts with offices. Instead of simply selling copiers it sells contracts that allow offices to use copy machines and pay by the copy (or more likely, for tiers or block-quantities of copies). Xerox is responsible for maintenance of the machines. Defense contracts are also servicized to an extent. Fighter jet engines have to guarantee a certain number of flyable hours to the military. The manufacturer is on the hook if downtime for maintenance puts the jets out of service for too long. One of the first commercial examples of servicization was for office flooring and carpeting. The concept was not very successful because facilities managers had a hard time shifting capital budget expenditure to operating costs. Consumers may be more willing to make the shift.

I could imagine Amazon switching to a subscription model for Alexa. Right now, the product is actually called an Echo, but many people refer to it as their Alexa anyway. Why not just make Alexa that actual product? Amazon could simply give people the device (or enable it over other devices that we use, such as our phones, televisions, and computers) and have us subscribe to various levels of service from Alexa (number of requests per week, music access, advanced information access, additional “Skills”, etc.). Undoubtedly one of Amazon’s objectives with their Echos is to increase engagement between consumers and all of the company’s products and services. Certainly, this new service-based model would incite more interaction and engagement with Amazon. One of the reasons I personally do not use Alexa is because I do not want to pay for one of the devices, especially when I already have a Bluetooth speaker that I am fairly fond of. If Amazon lowered the monetary barrier to access, I may be more inclined to become a user of the service.

Health care, for the most part, is already a service, but we pay for its delivery rather than its function. I could imagine a very different health care system in which we compensate providers for what we actually value: mainly wellness, as well as restoration in certain situations. We could actually wind up paying more the healthier we are rather than the sicker we are! Many people would appreciate that change, especially the government, which is the largest buyer of health care in the world.

The largest market in the world is the market for labor. Most labor, especially in developed economies, is full-time. Our time is bought as a product by our employers, but what the employers actually want is the function: applied strategic thinking, computation, applied engineering, customer consultations, treating patients, servicing vehicles, etc. In the current economy, in which there is extremely low unemployment, there is less risk to having salaried employees. Labor is in tight supply and companies are able to meet payroll by generating regular revenue. However, during downturns, when consumption decreases, full-time employment becomes a critical problem and companies wind up laying off employees, only exacerbating the downturns. Without guaranteed salaries no one would ever be laid off and the economy would have a natural hedge against severe downturns.

Freelancers have already servicized their labor, but nearly all professions could conceivably be servicized. Sales roles are partially servicized through the paying of commissions. The problem is matching service providers to customers. It’s typically easier to work with garages than for every person who needs an automobile repair to find a mechanic with some free time. The same goes for companies. It is difficult to find and contract on a continuous basis doctors, pilots, drivers, construction workers, bank tellers, data scientists, store managers, and nearly all other jobs, so they are usually just hired on at least a semi-permanent basis.

Aggregators have begun to servicized labor by ushering in the “gig” economy. An aggregator usually just replaces the old employer, but with none of the risk of having to pay salaries. For instance, Uber facilitates an income for all of its drivers, but Uber does not directly pay any of them, so in a downturn Uber would not be responsible for making sure any of them earn an income. For a truly equitable system to emerge the intermediaries would have to disappear and workers and companies (or the buyers of any service) would have to be able to directly contract with each other. Blockchain is one innovation that offers a solution to this problem. Blockchain allows for direct peer-to-peer transactions, so no aggregator is necessary to facilitate matching. In a way, all participants would own the matching algorithm and they would all benefit from it.

Maven car sharing, by GM

Another market that we may soon find to be servicized is automobiles. Automobiles are vastly under-utilized assets, spending most of their time taking up space in our garages or sitting outside of our workplaces. Instead of buying automobiles for personal use, imagine receiving an automobile on-demand from an auto marker or a fleet operator. Rental companies do this on a small scale now, but as autonomous driving technology develops, the scope of on-demand car service can expand. People will no longer have to own their own vehicle to guarantee themselves mobility. No matter where you need to go, a car will be available to you and you will pay based on how far you go and how long it takes you.

In the example of the Whirlpool washing machine, the consumer may very well pay for the electricity to operate the machine on her own. However, it is possible that for items like air conditioners, home furnaces, and automobiles, the manufacturer would pay for the utilities. In this arrangement, the manufacturer would save money the more efficiently the machine operates. Servicization has an opportunity to create a more sustainable economy. Carbon efficiency would be aligned with the financial incentives of manufacturers. No matter what the miles per gallon requirements legislated by the government it would always be beneficial for an auto manufacturer to exceed the standards. They could still charge the customer based on mileage, but the manufacturer would save money on gasoline.

Many developing countries do not have access to technology and machinery because purchasing them are too expensive and there is less access to credit in emerging markets. This leaves most low-income countries dependent on exporting raw materials to earn dollars and then spending those dollars on imported foreign goods. Servicization could change this. Farm machinery, manufacturing equipment, and construction machinery can all be servicized by the original equipment manufacturer. The amount of capital needed for new projects would be much lower, allowing for more development.

Around the world, many people buy big ticket items, like washing machines and automobiles, on credit. Servicization presents a large threat to consumer lenders around the world. Consumers would receive access to machines like washers for free and then just pay a little bit for each use. There would be far less need for consumer loans and credit cards. Consumers would be able to escape downward debt spirals, and finance ministers would be glad to see capital being deployed to more productive sectors of the economy.

Think of all of the items you use and rely on every day to go about your life. Sitting here at my desk (1) now there is a window (2) in front of me. I’m writing on a laptop (3). I’m listening to music from Spotify (4) of a Bluetooth speaker (5). The lights (6) are on, the air conditioning (7) is running, and my laptop is plugged into a socket to receive electricity (8). My food comes from a stove (9), I use a stationary bicycle (10) at the gym, and my cell phone (11) is never far from me. I enjoy cold water from a dispenser (12), and my clothes are washed in a washing machine (13). I take medicine (14) every morning to prevent malaria. We have an economic relationship with all of these items or services, but the terms of the relationship can easily be changed, both for the benefit of the consumer as well as the purveyor. The computing power of this laptop and my smartphone sit idle for most of the day. I have no way of knowing how efficient the air conditioning or the stove are, and I’m going to be quite upset if I get malaria despite having paid for the prophylaxis and adhering to the regimen.