The New Job Market

Posted on May 5, 2016 by Chris Niccolls

Robot DataRobots and artificial intelligence are about to change… everything! Cars that drive themselves have arrived. Drones are scary killbots in the sky, but U.S. citizens rarely see them in person. A new generation of robots are arriving in the workplace, and will become teachers, clerks, analysts and even doctors. They may have a few glitches on day one, but within a year of arriving, robots will outperform their human counterparts.

Top US firms may wait and see, but corporations that are struggling for profit, startups seeking breakthrough costs, underfunded school districts, bankrupt municipalities, the military, poor areas without doctors, China’s emerging  touches and medical services for the elderly are likely to lead the way. They will demand quality services at a price that only a robot can provide. Transition to a robotic economy will take a decade, but we can expect the labor market to be “disrupted” for another 20 to 50 years, or more. How will you survive until the disruption is over?

The best predictions point to a loss of as much as half of all U.S. jobs, in as little as 10-20 years. This js a bigger labor disruption than anything before in U.S. history. Farming once employed 70% of U.S. workers, but now employs just 2%. That change took 150 years. Manufacturing was once 70% of the workforce but fell to 8% in just 50 years. In 1980 70% of Chinese workers were farmers. After 35 years, it’s just 29%. This “disruption”  represents the elimination of 300 million farm jobs… just using “old” 20th-century mechanization. That’s equal to the entire workforce of the US AND Europe combined. How has China survived this shift in their labor market?

Essentially, China implemented the most impressive social engineering project that the world has ever known. China planned the transition for decades, invested trillions of dollars, built over 500 new cities, installed nearly 2,000 conventional and nuclear power plants, relocated (not always voluntary) hundreds of millions of workers from China’s  interior to coastal and river cities, and raised college enrollment from 1.5 million to 20 million.

Yet, even with this staggering amount of effort, the transition has not been without problems. Newly built cities have been left deserted due to changes in planning, there are huge inequities between city and rural pay, in 2015 there were nearly 3,000 strikes and riots, and new workers no longer want to work in factories creating labor shortages. Of course, the US and Europe labor markets were hit by their own disruption as offshoring eliminated millions of jobs. Robots will be even more disruptive. Especially when China itself (the world’s largest buyer of robots) installs millions of robots in its own factories.

In the West, we have been allowed to view some of the disruption in China, especially in factories where iPhones and similar electronics were assembled. China improved working conditions and raised pay. Strikes slowed down, but the increase in pay raised the cost of work in China, compared to competing Asian nations. In order to compete, China needs to improve worker productivity. Which led to China’s “robot initiative”.

To match the cost of countries with lower pay (Vietnam and Cambodia) or higher productivity (South Korea) China plans to install millions of robots. However, by installing these robots would not only costs, it would also more than double productive capacity. Therefore, China needs to either terminate more than half of their 100 million industrial workers, OR they need to encourage the West to outsource more jobs.

If China can cut labor costs in half (or lower), that will attract more work from the West. China also plans to move millions of displaced factory workers into knowledge work jobs into banking, insurance, media and service firms. With these new resources, China can execute on plans to take over these industries around the world. At the same time, the NEW generation of Artificial Intelligence systems will be rolled out to improve productivity in these sectors.

With new worker, and new technology China just may dominate global financial and service markets by the early 2020’s. Did you know that 4 of the top 5 banks in the world are Chinese? Or that China Life is the 3rd largest insurance company in the world? China is already well positioned to dominate the global markets. Recently China even created an alternative to the World Bank, the Asian Infrastructure Investment Bank. China will use this institution to lessen US and European political influence in developing countries, and support their own commercial expansion. This plan requires China to be on the leading edge of robotic automation. As China automate knowledge services, the U.S. and Europe will be pressured to do likewise.

Internationally and domestically, market pressures will accelerate the adoption of robotics and A.I. Similar pressures created the last wave of outsourcing. Decades of corporate outsourcing (2000 – today) created the infrastructure to quickly analyze a position, and then automate it or move it to a new (lower cost) worker. When it’s time to replace a job with a robot, all of the procedures and protocols are already in place. The top consulting firms (IBM, Accenture, Capgemini, TATA, etc.) already have contracts with the Fortune 500 firms. Networks and other technology that will control A.I.s and robots are already up and running. HR and legal long ago agreed on the processes and legalities to eliminate staff. The Robot Revolution will just take over the infrastructure, the “Outsourcing Engine”, speeding job losses until they outpace new job creation. Where does that leave you?

More Training: The most positive articles about the Robot Revolution say  that another degree might save you from losing your job. But this just doesn’t ring true. If your job as a financial analyst has been taken over by a robot, getting another degree to be promoted to a higher position in the same line won’t work because – 1) Can you afford to pay-off another school loan?, 2) It will take a couple of years to get a new job, but by then this job might be targeted for a robot takeover, 3) Having a lot of new PhD graduates doesn’t mean that there will be a lot of new Ph.D. jobs.

More education is often a good thing, but not always. The numbers behind a new degree may work for a few jobs, but it requires case by case analysis. More education may not buy more security. Moving into a completely different field might be a more successful strategy for finding a job, but moving from a highly paid financial position to a mid-level healthcare job might provide the same level of compensation. Education may be the answer for some, but not the majority of displaced workers.

Be A Teacher: A growing number of articles tell us to, “Consider a job in training or education.” There are excellent opportunities in education, and we certainly need  more good teachers, but the Robot Revolution will create fewer new training positions than these writers expect. In the old model, trainers work with experts to document the how to perform work processes, and then use this documentation to train new workers. For robots, few trainers will be needed. Robots work directly with experts to learn work processes, and then robots will “teach” other robots. Robots are continuous learners, who correct and expand their database of knowledge. Robots then expand into adjacent jobs, with minimal help from trainers. Teach one robot to fill out mortgage applications, and all robots learn the process. Eventually, they learn how to fill out car loan applications, and replicate the information. Finally, they will learn how to approve the applications…. with better results than the humans they replaced.

Perhaps you want to be a trainer or teacher in some other field? Unfortunately, the Robot Revolution is targeting teaching. Robots can individually monitor students, tracking biometric information (eye focus, body language, blink rates) to know if they are paying attention to the materials, adjusting the pace of education for each student, customizing homework and projects to match individual abilities and issues. Humans cannot cost-effectively match the capabilities of a robot teacher. Don’t believe me? Then take it up with Elmo! That’s right, IBM’s WATSON learning system is teaming up with Sesame street to deliver education to young, poor students. In a few years… will the Cookie Monster teach financial modeling?

Climate Change: Two wrongs are not supposed to make a right… but, there are exceptions. Like climate change. Temperatures are rising around the world, water sources are dwindling, coastal cities may  drown, finned fish are disappearing, farm productivity is falling behind population growth, fossil fuels are anathema to millennials, and even optimistic environmentalists say we don’t have enough people to fix all of these problems. But when robots take hold and global unemployment rises by hundreds of millions, could this new army of workers be our solution?

Hurricane Sandy caused $70 billion in damage. Katrina cost $125 billion. Obviously, it’s worth paying a lot of money to avoid this magnitude of damage. New York City has a proposal to storm proof sections of the city, at a price of $20 billion. A plan for Jakarta is estimated at $40 billion. Then there is Venice, Mumbai, Naples, Bangkok,  Mexico City, and Miami. Think about all of the smaller towns along coastlines. Add to that damage to farms, dwindling water sources, highways and infrastructure. One estimate puts the cost of economic damage for modest global warming (2.5% degrees C) at 0.5% of global GDP, or around $500 billion annually.  Fixing the environment is a huge job. Not fixing the environment isn’t an option, unless we are ready to lose city after city as storms and disasters destroy our urban infrastructure.

Crowd Funding: In the past, if you had an idea for an important project (a business, a new product, a service) you would ask a bank or your friends and family for funding. Now, you can raise just about any amount of money, directly from the public. There are about 200 Crowd Funding sites, with more sites being built. Crowdfunding allows you to campaign for funding through social media tools. The biggest crowdfunded project to date is a game, Star Citizen, which has raised $110 million. In 2014 over $30 billion was raised through crowdfunding, and by as soon as 2018 it will surpass $90 billion. Today, Venture Capital (VC) is the money behind emerging technologies and emerging service companies, but crowdfunding is about to surpass VC funding.

Many of the college graduates who are about to be displaced by robots follow a very specific pattern. They graduate from an Ivy League business school, get a corporate job, gain experience, and then try to launch their own business. If they could, most would jump to creating their startup, either earlier in their career or while they are still in college. This is beginning to  happen with crowdfunding. Music, games, books, new technology, even projects that can save the world… are all getting funded through crowdfunding. The combination of a global environmental crisis, and the ability for new talent to circumvent traditional employment paths is the best alternative to waiting for a robot to take your corporate job.

Conclusion: The coming Robot Revolution will revolutionize the workplace. The lucky owners of key corporations will see massive new profits as inexpensive robots replace high-cost human labor. This is a frightening scenario, but it is one that America has lived through before. Agricultural jobs went away.  Manufacturing jobs went away. Recently, millions of basic corporate jobs (clerks, phone operators, secretaries, typist pools) went away… and will never return.

In another year or two smart robots will move out of the factory and into the corporation, wiping out jobs from chauffer to financial analyst, and then learn how to do the same for lawyers, doctors, and teachers. The infrastructure for replacing large numbers of workers was put in place during recent rounds of outsourcing. The Robot Revolution will re-use the “outsourcing Engine”, vastly accelerating the replacement process, reducing jobs faster than they can be created.

With high paying jobs going away forever, we face the possibility of double-digit unemployment for decades to come. The solution to the labor market may collapse couldn’t be more ironic. The millions who lose their jobs in finance and in knowledge work may find new careers fighting environmental collapse or using crowdfunding to build new technologies and new products that the old sources of funding (banking, venture capitalists, etc.) would be too conservative to back. Will global warming and crowdfunding make up for the gap created by the Robot Revolution? It’s too soon to say if it can offset all of the job losses, but it will certainly soften the impact.

The labor market today is  significantly different than it was just a decade ago. Now, we MUST use social media as a part of our job search process. Music stars, software developers, actors, writers and many others live on social media and can’t work without it. The most intelligent and talented young workers will bypass traditional corporate employment and become part of the creative economy… creating new products and services, funding them directly through crowdfunding. Signs of the environmental crisis are mounting, and project to fix the world will be crowdfunded. The Robot Revolution and Global Warming, two of the greatest disruptions to human civilization EVER, may be the only cure for each other. That’s my Niccolls worth for today, and I’m sticking with it!

Do you agree, or do you think there are better options for the coming disruptions to employment? Comment on this blog and let everyone know what you think!

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Flatline… Subscriptions Will Be The Death Of Tech Jobs

Posted on April 27, 2016 by Chris Niccolls

FlatlineWhen does a buzzword become a technology? It’s hard exactly, but “Subscription” is a great example. It started as a simple idea. Software sales groups spend a lot of time and money chasing after customers. They want customers to buy the latest version of software, but the cost and effort to buy and install software are often too much for too little benefit. Even worse, the customers who invest the least, end up with the most obsolete hardware and software and require the most support. Maybe they need to squeeze another year out of obsolete equipment. Either way, these customers consume the most resources and pay developers the fewest fees. A subscription/cloud solution could solve this problem! Software would be up to date, and the sales revenue from updates would be more predictable! That’s not a bad goal, but it could create changes that will disrupt the Tech sector. Let’s dive in and go through the details!

The concept of a subscription is that consumers pay a fee every year (or monthly) for every user of a software product (or suite). Customers with an ebb and flow of projects only need to pay for the licenses needed to work today. In the old days, a big project at the beginning of the year might require 100 licenses; when the project ended, and only 10 licenses are needed for the rest of the year, you can’t get your money back. You might be able to rent licenses from a subcontractor, but that is usually expensive and messy. A subscription is just turned off when you’re done. That’s a big advantage, that can lead to big cost savings.

Of course, once you use a subscription, if you ever fail to pay the fee, the product stops working. Just like failing to pay the phone bill means that it is disconnected. That’s quite different than the old model where if you fail to pay for a new version of software or fail to pay for a support contract, you run an outdated version of the software or you cannot call for support.

Subscription models also automatically renew. As a customer, you may not see this as a wonderful feature, but your software provider loves this feature. It makes their revenue predictable. Before subscriptions, customers chose if they wanted to upgrade. Customers tended to skip some upgrades and install others. A new release might have great features, or it might just contain incremental fixes. Even when you want to upgrade, an enterprise rollout of a new version of software involves much more that licensing fees. You may need to upgrade your O/S, hardware and networks, other software, test drivers, change printers and other equipment. That involves a lot of IT hours and a lot of potential conflicts with other corporate projects. When customers fail to embrace a new version of your software,  the loss of revenue can hurt your bottom line.

The bigger the corporation, the greater the impact of a weak release. Lost revenue is just the beginning. Public firms MUST announce their predictions for an upcoming release, long before it is ready. To must report when the release will happen, how many customers will adopt it, expected revenues and anticipated problems. When expectations are not met, Wall Street analysts are not forgiving. Ger ready to be hammered a second time! The weak release cost your revenue, and now the analysts downgrade the value of your stock or recommend that your shareholders sell. Even if it is a very small miss, or even if you met your prediction, analysts may be unhappy that you did not exceed it. An unsuccessful release can force out a CEO or crush a publically traded firm. Regular releases that need to be purchased create an “event”, that analysts want to comment on. Subscription models spread out revenues and make them more predictable. That’s very alluring to the C-Suite, which explains the growing popularity of the model.

Another incentive is… cost! Older computers (and printers, servers, etc.) cost much more to support. As drivers slowly go out of date, old software is no longer supported or doesn’t run reliably. Automatic upgrades, a standard in subscription software, constantly test your configuration, and “ping” you as individual pieces of older equipment become obsolete. Rather than big messy upgrades, subscriptions create small incremental hardware upgrades. “Cloud” component are standard with many subscriptions, moving large parts of the storage and processing offsite, allowing the use of simpler/cheaper workstations. That further reduces the need for expensive upgrade projects. So far, so good? Here’s the downside.

The first item is the cost. What is the right price for a subscription? Under the old model an expensive software product, such as Autodesk’s Autocad, costs thousands of dollars for a minimal installation. A full suite of tools easily costs $10,000 or more per user, not including PCs, printers, and networks. How do you count licenses? Are they concurrent (i.e. maximum number of users at any one time), or is each license owned by a specific user (total number of users employed this month)? That’s a BIG difference if the product is used infrequently. If you can change your license count, these “flexible licenses” are usually expensive and more difficult to

If your license count changes frequently, “flexible licenses” are often expensive, difficult to count, and harder to use (can you only change count once per month?). Will your annual license be based on a “natural” purchase cycle of once every 5 years ($10,000 / 5 = $2,000 annually), every $3 years ($10,000 / 3 = $3,333), or something else? Whatever calculations are used, some new subscription users, especially those who upgrade infrequently, are going to pay more under the new model.

While a particular business might be greatly impacted by new pricing model, the operational model creates the real impact. Because subscriptions and Cloud services often go hand in hand, they will trigger massive consolidation. Imagine you run a business that relies on computer software. You switch to a subscription plan that moves your software and data files to the Cloud. You now have little (or no) need for servers, and for desktop storage. Your firm’s IT infrastructure shrinks, dramatically reducing the need for servers, customized PC’s, software installation and support for remote computers. Your IT department moves to the Cloud, but the jobs don’t.

For you, supporting technology is a cost center. For your vendor, providing software is how they generate profit. Automatic updates dramatically reduce the support cost for vendors. Taking the hardware platforms of a thousand corporations and consolidating it into just one big environment is massively more efficient. BUT… vendors are not building servers and hiring new techs. Instead, they too are moving their services to Cloud providers, such as Amazon Web Services (AWS).

Software developers no longer want to create their own storage centers, because they cannot create storage servers centers as cost effectively as AWS and its rivals. In 2014, AWS had over 1,000,000 customers, and their annual revenue grew by 40%. Microsoft, Iron Mountain, Rackspace and many other providers are seeing similar growth. New technology startups often begin building their model with cloud storage and processing baked into their model.

Meanwhile, back at your office, your reduced IT staff is struggling to provide 24 x 7 support. Time to move the rest of your servers to the Cloud? Most corporations will save quite a bit of money, get higher security and more reliable backups. New software rollouts will be faster and take less planning. When everything was on-site, you would wait weeks or months for an opening in IT’s maintenance schedule. Cloud providers have reduced this to the flip of a virtual switch. Let’s break this down:

  • Server Scale: A cloud-based service allows you to grow or shrink your operations, without any previous notice. That means fewer barriers to running the latest versions of software and drivers. The more up to date your environment, the lower the number of problem calls per user, and the lower the cost of operations.
  • Budgets: Installing new software in the corporation means buying new software licenses, new equipment, and budgeting time for the techs to do the work and fix the inevitable follow-on problems. For a big upgrade, you may need a big budget increase over last year. These periodic surges in the budget are difficult for IT managers to explain to financial managers. That leads to delayed upgrades, which leads to higher complaints. Subscription based services may not lower the budget, but they make budgeting more predictable.
  • Support Cost: Data centers at AWS and its competitors are far larger than most corporations. They buy more servers and storage and get lower prices. Cloud providers can sell each GB of storage or CPU Cycle for less than you can pay. And you get built-in services such as…
  • Anti-Virus: Virus attacks, malware, spyware and targeted attacks on corporate data centers have shown how vulnerable most centers are. The cost of security rises every year, and so too do the number of large-scale “hacks”. Cloud storage bakes in anti-virus/malware software plus other security services into the data center design, making another layer of corporate techs unnecessary.  Few corporations can compete with AWS’ in-house virus lab. Corporate reputations have been permanently damaged by cyber-attacks. IT heads are increasingly turning to dedicated storage providers for an answer.
  • Automation: In order to be the most efficient providers of storage and CPU’s, services like AWS make use of far more automation than corporate IT groups. Gone are the endless meetings with IT to authorize and schedule work. Gone too are the IT techs and managers. Instead of submitting requests, you move a slider on a control panel and… you have more space. Installations and uninstallations are automated. AWS, for example, also provides automated software testing facilities, eliminating the need for (human) techs to perform this function. This may not spell the end of software testing as a career, but it dramatically reduces employment.
  • Efficiency: CThe scale of Cloud-based services allows them to manage and update your environment more quickly and at a lower cost. Even if Cloud services were no more efficient than corporate IT department, merely consolidating a thousand IT groups would yield massive redundancies. Let’s assume a 50% improvement, which we’ll call “A”. However, AWS storage is much more efficient than a typical corporation. By buying an order of magnitude more equipment than the typical customer, hardware prices are much lower. Add automation that is years ahead of their customers, and you have at least another 50% in efficiencies that we will call “B”. Finally, future growth from customers joining the Cloud will drive the next generation of efficiency, for another 50% that we call “C”. “A” x “B” x “C” is 12.5%. Optimistically, less than 20% of IT support will survive the next 5 to 10 years.

The Bureau of Labor Statistics (BLS) lists 4 million support techs and software developers in the U.S., plus half a million IT managers. Add to that another 2 million sales and sales support positions in the technology industry. What happens to the sales staff under a subscription model?

Sales staff either finds new customers or helps customers move from their first interest in a product to negotiating a price, training staff and installing the product. Sales groups are usually divided into “hunters” to find new customers, and “farmers” to grow existing accounts. Farmers focus on renewing licenses on-time and growing the number of licenses for an existing account. Subscriptions automatically renew, eliminating that part of farming. Adding, deleting and reassigning licenses is better handled by Artificial Intelligence (A.I.) than human sales staff. An A.I. just needs to monitor accounts and message customers when they need adjustments in their license count. Users can make other changes themselves.

That just leaves the “Hunters”. Small software firms still sell their products directly, while larger firms outsourced most of their hunters in the 1990’s. They use “channel distributors” or “Value Added Resellers” (VAR). Developers create products, pay for ads, provide technical support and training (to VARs and customers), pay for conferences and events, and send sales leads to distributors.

Distributors are independent firms that sell products from one or more software developers, plus add-on products, training, installation services, and support. Simple products (ex.: MS Office), are sold on-line or in big box stores. Expensive products that require professional installation and training (ex.: a full suite of CAD products from AutoDesk, for $10,000 or more per license) usually involve a VAR. Developers surrender a large part of their revenue stream to distributors because distributors usually cost less (30% less) than internal sales groups. With subscription based sales, sales groups and VARs will need to cut over 50% of staff.

With a Cloud/subscription model, the few remaining sales staff will each be paid less. Today, in a market like New York City, top sales people get compensation of $250,000… $500,000… even $1,000,000 or more. A very, very few of these will remain but most are destined to become support, and might be moved into customer service. The subscription product is so much simpler, commissions will be few and far between. I have seen communications from developers to distributors explaining how the new subscription world will work. They all talk about change, some mention staff reductions, none discusses the need for more sales staff.

The subscription model is picking up steam and will probably be the standard in less than 5 years. Six million tech and sales jobs are at risk, and most will be lost in 5 years with the rest gone in no more than 10 years. Customers never asked for this model, the benefits to the customer (simplicity, consistency, better support, maybe cost savings) make it very compelling. Developers drove the model, and their benefits (lower support costs, higher revenue, elimination of distributors, reduction of sales staff, stable revenue) make the model inescapable.

A few firms or customers will refuse to go along, but they can only delay, not derail subscriptions. If you’re in technical support or sales, the future is a bit grim. On the other hand, by reading this you have become one of the privileged few who knows exactly what’s coming. And still has time to prepare for it. At least, that’s my Niccolls worth for today! Follow the next blog where we discuss alternatives to the coming Robotic revolution… such as the creative economy!

What do you think? Do you agree that the subscription model is about to disrupt sales and support positions? What are the alternatives? Write in and tell us what you think!

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The Robot Revolution On Speed: Too Late To Turn Back!

Posted on April 4, 2016 by Chris Niccolls

Speed

Tongues have been wagging about the rise of the robots. Robots taking over manufacturing, robots acting as home companions and robots taking our jobs. A lot of digital ink has been spilled on the subject.  You know what? Just about every one of these fear mongering articles… is right! Many of the most common types of robots won’t look anything like people. Don’t expect tall silvery humanoids with antennae sticking out of their heads. Many robots will be hard to recognize or they might just be one more app running on a computer, or even a cell phone.

What makes robots and artificial intelligences game changers is not how they look, but what they do. And what they do… or are about to do… is your job!  When robots aren’t actually performing your job, they will spend their leisure hours learning how to do your job better. Or, maybe they will learn how to do your boss’s job. Before the end of 2016 you’re going to see some of the first skirmishes in the robot revolution. By 2017 you’ll see human employees lose a few critical battles. Read along and learn what you need to know about the coming Robot Revolution!

Machines replacing people? This isn’t exactly new.  Machines, starting with simple machines like the wheel, have made people more productive. For most of human history, humanity was so poor that we would immediately consume any additional productivity. Especially when that productivity was in the form of food or fuel. This additional productivity led to more wealth and more security. This Virtuous Cycle has been working since the earliest days of civilization.

Virtuous Cycle: The age of Sail started thousands of years ago and ended just a few centuries ago. Sail driven ships moved more goods, for a lower cost.  It also showed us that there were far more goods in the world than our own village, and some foreign goods (high-quality wool, silk cloth, spices, delicate jewelry, superior metals) were vastly superior. We had choices in what we could buy. This drove wealth, then luxury, and finally leisure time. Better machines improved productivity, which improves our standard of living. There were gaps and exceptions, but the story of mankind has been one of growing wealth.

Political collapse, war and natural disaster could disrupt this Virtuous Cycle, but eventually it would move forward and increase our wealth. In the 21st century we live with constant technological change and constant material improvement. There may be a good argument that wages have stalled for the last 20 years, but even with no more dollars in our pockets, our lifespan increases every year, diseases are cured, nutrition around the world gets better, and even our entertainment choices improve. But, what if something broke this cycle? Something like… robots?

Consider how this “Virtuous Cycle” works. It takes a very long time to implement new technology, and even longer to fully optimize and integrate into society. Social and technological limitations (cost of travel, cost of fuel, short live spans) made it difficult for the technology to spread very far from where it was discovered. It took centuries for the locals to figure out all of the details of a new technology, and even after a thousand years, it might not spread very far. Over this long period of time, wars and disasters could disrupt or even wipe out the technology. But all of these problems provided people and society

But all of these problems provided a benefit. People and society had time to absorb the technology. 3,000 years ago iron was the “secret weapon” the Hittite people used to conquer the middle east. In less than a thousand years, the iron age had spread across most of the world and even small villages had blacksmiths.

American Example: If an iron age example a bit too “dawn of time” for you, let’s move it up to… the early United States. America started out as a largely agricultural country, but also had fishing, mining,  a textile industry, fur trapping, shipbuilding and other industries. By 1850, agriculture was America’s the primary source of employment. The industrial revolution arrived and replaced animal and human labor with machines. EXCEPT the American South. The South’s choice of slavery over technology sparked a war between the North and South. Over 150 years, farm workers declined from 70% of the workforce to just 2%.

Need another example? Here’s one! The factories that built the machines that replaced the farmers, needed workers. Early factories were often difficult places to work, but they were better than farms. They paid more, they brought people together, increased education, and (eventually) improved life expectancy. Were the farmers happy about the transition? Absolutely not. There were all political and physical fights over land and jobs, culminating in the Civil War. Nonetheless, the industrial age turned farmers into factory workers, and by 1960 most Americans worked in factories. Yet just 50 years later, manufacturing is just 8% of the job market, and shrinking.

There is, however, an important difference between the revolution in farming and the revolution in farming and the revolution in manufacturing. Time! You may not be surprised that the Iron Age took 1,000 years to take hold. America’s farming revolution took just 150 years. Manufacturing took just 50. Every new technology revolution builds on the last revolution and takes less time to be adopted across a larger area. Still not enough? Let’s look at one more example.

Chinese Comparison: In 1980, 70% of China’s workforce was agricultural… just like the U.S. in 1850. China needed to move agricultural workers into manufacturing jobs. This became the #1 priority of their long-term economic plan. By 2012, agricultural workers were just 35% of the workforce. In percentages, that’s at least twice as fast as the US conversion. Pretty good. But in total population, this is absolutely stunning!

America eliminated most of the agricultural positions when our nation was young, with a small population. Agricultural employment peaked in the US in the 1920’s, at just under 11 farmers; now there are just 2-3 million farmers. This transformation occurred over 7-8 generations, so it impacted the lives of more than 11 million farmers. However, China peaked at 700-800 million farmers towards the end of the 20th century. China eliminated 350-400 million jobs in just 35 years, or 10 million annually. Rather than eliminating jobs twice as fast as America, it might be more accurate to say that China eliminated jobs 100 to 200 times faster. How long would it take China to eliminate the 40 to 60 million corporate “knowledge worker” jobs in America?

New Cycle: The Data Economy arrived in the last quarter of the 21st Century. Instead of wealth arising from the land or a factory, it came from information. Farms were largely self-supporting through human history. In the 20th century, farms grew large enough to need  significant capital to buy fertilizers, pesticides, seeds and equipment. Manufacturing firms were still larger, and needed banks for funding, banking services, and services to exchange payments with workers, vendors and customers. Financial firms (and financial departments in corporations) needed Knowledge Workers.

Knowledge workers turn collections of information into knowledge (insights). These insights guide investments: which markets are growing, where are profits the highest, etc. Knowledge workers initially used data, a calculator and paper & pen. Soon, computers replaced calculators, and data feeds replaced paper. Demand continued to rise, and publishing firms sold on-line data: McGraw Hill Financial, Standard and Poors, Moody’s, Gartner and others. On-line services began replacing jobs. Computer and consulting firms (IBM, Oracle, Accenture,  McKinsey, Bain, and scores of others) sold services to automate (or outsource) functions, reducing onshore knowledge worker jobs.

Outsourcing created the Virtuous Cycle 2.0, and a quandary. Jobs with better salaries were being created but in lower pay geographies. A $60,000 position in the US, had a total cost of $100,000-$120,000 (adding bonus, benefits, space, IT support, etc.). In India, that position is paid just $20,000 (the employee is paid just $6,000). Even if it takes 2-3 workers to replace one onshore, it still saves money. That $6,000 offshore salary isn’t much in the US, but it an be a great salary offshore. Virtuous Cycle 2.0 still works, if you look at global results and ignore domestic losses. Did VC 2.0 continue to create wealth? This is still being hotly debated.

The Vicious Cycle: The Data revolution differed from the two prior revolutions in many ways, but the key difference was… speed! It’s very simple. Employment is determined by new jobs created and old jobs lost. Whether the economy is hot or cold, jobs are always created and lost.

In the Data economy, computer intensive work was targeted for offshoring.  In 1970, computer jobs were a rarity. By 2000, you couldn’t get hired in a corporation without computer skills. One by one, isolated parts of the corporation that were not “outsourcing ready”, disappeared. Decades of effort, effort that was not always specifically aimed at outsourcing, slowly prepared millions of jobs for outsourcing and automation. “Flexible” robots and A.I.s will now trigger the elimination of these jobs.

The Domino Theory: Have you ever watched a domino display fall down? A good display can take days to set up, and seconds to fall. It just takes one little push on that first domino, and the rest all fall. We’ve been setting up “dominos” for the robot revolution for 30 years. Take a good look at this delicate stack of dominos:

  • Infrastructure: Think of mobile robots (self-driving cars, flying drones) as blue collar workers, and A.I.s running on computers as white collar workers. Corporations own BILLIONS of PC’s. Each computer could run an A.I. that writes stories (reporter, financial analyst, lawyer), analyzes data (accountant, financial analyst, lawyer) or performs other tasks. Tasks too complex for any single PC would be shared across a network, in your office or around the world. Any work performed on a PC is ready for an A.I. to take it on.
  • Capabilities: A.I. systems are disembodied intelligences that read electronic data and documents. However, not all information is in computer format. To capture information from the “real world”, you need cheap and reliable robot “senses”. A generation ago, these didn’t exist. Now they are everywhere, including your cell phone. Robots can read and speak (Siri and Cortina), hear (voice recognition) and see (phone cameras, object recognition software). Touch screens let you physically interact with technology. Smell and taste? Not yet, but specialty sensors can identify: smoke, explosives, drugs, etc. Arms and legs are difficult to mimic, but automated kiosks (such as ATM’s) can take your cash and checks and hand you money without lifting a robotic finger.
  • Capacity: Corporate computers are already used by employees, but only for 40 hours a week. “Robot workers” could exploit the remaining 128 hours. As computers wear out or become obsolete, less expensive yet more powerful replacements will lower the cost of robot workers every year. Consider, Deep Blue. In 1985, IBM wanted to build a computer capable of defeating a chess grand master. IBM built the world most powerful supercomputer (Deep Blue), and won! Today, a chess app running on a cell phone can defeat grandmasters. Once a robot takes over a job, they ALWAYS perform it better than a human. A job lost to a robot, is lost forever.
  • Self Learning: Robots differ from earlier technology. First, they are flexible, allowing them to work in less structured environments. Older tech would stall when faced with unexpected issues; robots can apply general rules to find a specific solution. That allows robots to work in more environments. Second, robots learn on their own. Humans still write the general software, but robots then work with experts to learn how to do work. The expert gives the robot an assignment. The robot then analyzes documents (spreadsheets, images, forms,etc), to produce its assignment. Just like a student, the robot’s work is graded and the robot eventually “graduates” and can then independently perform work. This process is applied to task after task, eliminating jobs. After “worker” tasks are all replaced, robots can apply the same process to management positions.
  • Knowledge Transfer: When outsourcing hit the US, the speed of moving jobs offshore was limited by the speed of training. Training an offshore financial analyst took years. Then, after a few years, the local pool of candidates was smaller or completely used up. More workers could be found by hiring less skilled candidates, and providing more training. This took longer, and cost more, but outsourcing could continue. Once a robot is fully trained, you just download the software to another robot. Or a million. The cost of outsourcing rises every year as the best candidates are consumed. The cost of robots falls, as technology becomes cheaper every year. And each new robot is always more capable than the one before it.
  • Scale: In the last revolution, jobs were lost to automation or were moved offshore. In the robot revolution, you will still compete with outsourcers to keep your job. But you will also compete with the robots your company buys, on-line robots from data providers, consulting firms bundling robots and A.I.s with their services, and a new generation of robot vendors. Offshore firms will also build or buy their own robots, and offer hybrid human/robot services. The forces competing for your job will be far more numerous and more capable than ever before.

Robots Everywhere: We’re faced with a pretty simple question, “How many jobs will robots take over and how fast?” The answer is equally simple, “More jobs than ever before, and faster than anything you’ve ever seen!” There are more sources for robots, more corporations and universities working on robots and… thanks to the infrastructure already in place… vastly more jobs for robots to immediately take over.

Robots will create some new jobs, but less than people expect as robots eventually take over their own training, management and possibly sales. In the Virtuous Cycle 3.0, new wealth is still being developed, but fewer are domestic jobs with the same pay. Corporations will have higher profits, retirees will benefit from affordable personal services, and those with wealth will see a new age of luxury.

However, the 30% to 50% of the workforce that will be eliminated, especially the younger workers,  will have a more difficult life. Especially in the next 10 years, when we lose 10 – 20 million jobs, or more. In as little as 20 or 30 years, this disruption may end and employment may rise again.

It will be years before we have any idea of what VC 4.0 will look like. Most likely, it will rely heavily on the Creative Economy (more in a soon to be released blog). For now, if you have an entry or mid-level corporate job, spend your time writing or reading reports, work on a computer, drive a car, train, ship or plane, assume that a robot can do your job. If not today, then in a very few years.  And that’s my Niccolls worth for today.

Do you agree or disagree? Comment on this blog and tell us what you think!

Posted in Decision Making, Delivering Services, Employment, Improvement, Robots | Tagged , , , | Leave a comment

Uber Orders 100,000 Robot Cars!

Posted on March 24, 2016 by Chris Niccolls

VOLVO SWEDEN FORD

AP Photo/Jonny Mattsson

Did they or didn’t they? Everyone wants to know if the rumor is true. Did UBER order 100,000 Mercedes-Benz type-S cars? If they did, UBER could provide a “driverless” taxi  service AND  set a new low price for car and transportation services. Some report that UBER is on the verge of buying the cars while others report that the deal is already consummated. The truth is… it doesn’t really matter!

Whether UBER placed an order or not, the rumors are an indicator that UBER is far enough along in negotiations with Mercedes-Benz (and other manufacturers?) to start leaving an evidence trail that they need to buy cars soon. I said it before and I’m saying it again… the robot revolution is on its way, and UBER is the tip of the spear. The future is as early as tomorrow morning, and tomorrow morning is when YOU WILL GO TO WORK WITH AN UBER DRIVERLESS TAXI. Today, we look at the UBER phenomena and the inevitability of driverless cars.

First, and perhaps most importantly, I told you so. Now that that’s out of the way, here’s what you can expect. The first step, the pilot of driverless cars in a taxi service, will start in New York City. Second, by the end of that year, 15,000 to 20,000 taxi driver jobs will be eliminated in New York City. Third, within 5 years, 1 million driver jobs in the Metro New York area (not just taxis) are eliminated, and a total of 5 million driver and transportation positions are eliminated or targeted for elimination across the US. Fourth, after 5 years of “disruption”, we pick up speed as consumers embrace driverless cars. The auto insurance industry crumbles, healthcare and legal industries lose billions of dollars of revenue from car accidents, garages and gas stations close due to a lack of fender benders, and the car industry has strong growth for a few years before changing direction and losing 90% of its workers. This is what the robot revolution will bring about in transportation.

If you haven’t read my earlier articles, here’s some key background. UBER never made a profit. In 2015, UBER lost $250 million. A primary cost, fuel, has recently had a dramatic decline. This makes driver’s compensation even more prominent. Mix in stories about UBER drivers killing and raping passengers. If UBER goes driverless in just Manhattan, a mere 23 sq. miles of territory, UBER would go from a $250 million loss to at least a billion dollars of profit. Even if they fully loaded the cost of new robot cars into the first year. The numbers are really stunning. This will motivate UBER and its competitors to do anything in their power to get driverless taxies on the road. Here’s another number. If UBER can ride this wave and stay in business, their fleet of 100,000 cars will grow to 10,000,000 in 10 years.

That’s a lot of big numbers and big projections. You must be thinking, “Surely it’s not possible for me to predict how this part of the robot revolution is going to play out over the next decade.” Or is it? Here’s your playbook for the coming revolution!

Ground Zero: Autonomous cars today drive as well or better than human drivers. However, while it is legal for a driver to turn on an autonomous driving  feature, it is not clear if an autonomous car is allowed on the road without a driver. The more counties and states you drive through, the more laws and regulations you may break. However, New York City is UBER’s biggest market, with both the highest revenue and the greatest driver costs.

In just a few years, UBER created a fleet of 15,000 cars in NYC, more than all of the Yellow Taxis (which held a near monopoly since the 1930’s). Manhattan, just 23 square miles of land, is responsible for 90% of UBER’s NYC revenue. UBER could not hope for a better location for a driverless taxi pilot than Manhattan.  UBER’s aggressive “just try to stop me” operating model in NYC is how they out-grew the Yellow Taxi industry, and it is how they will beat the City government once again if the City blocks autonomous cars.

Lost Jobs: It took UBER just a few years to build its fleet of 15,000 taxis in NYC, and they could replace it in just one year. The fact that they haven’t purchased the garages and gas stations they would need for this transition is a sign that they are not yet ready for the rumored order of 100,00 cars.  But the infrastructure need for a pilot could be in place in a few weeks. By offering a Mercedes Benz type-S (or any similar car), New Yorkers would have a choice between riding in an UBER luxury car or a low to mid-end Yellow Taxi. If the UBER ride is cheaper, Yellow Taxis could disappear in just 2-3 years.

Lawsuits, regulations, and government foot-dragging give Yellow Taxi’s another year or two, but that’s it. After all, can City government be against autonomous cars that virtually never have an accident or kill a citizen? Taxis that electronically cooperate could also reduce traffic jams. An autonomous car is really the auto industry’s greatest safety feature, and will save more lives than seat belts, airbags, and anti-lock brakes put together. As driverless taxis take over NYC, commercial trucks, buses, trains, and airplanes will also adopt full autonomy.

The US: Driverless cars will succeed in New York, and they will spread throughout the US. Some states or counties may form pockets of resistance, and former drivers will protest and vandalize robot cars (that’s the pattern for employment disruption). But it won’t change the outcome. Consumers will adopt “autonomy” in the next 3-5 years. They will let their car drive them to work. This will drive changes in car design: removal of the steering wheel, drivers seat, perhaps having all seats face each other, add a work table, WiFI, and maybe a bed for the owner with a long commute to work?

Now, we enter UBER’s next stage, making American’s give up car ownership. Why have a second (or third?) car that spends most of the time in your garage? Instead of giving your teen a car, give them an UBER account. Shouldn’t you get your parents to stop driving at night with their limited vision? Will super-moms welcome some help picking up and dropping off the kids at school and other activities? Dump the extra cars, the cost of gas, the lease, and insurance costs, and get everyone pre-paid UBER accounts! UBER’s fleet rises to 10 million cars or more. Then UBER can turn its full attention to taking over truck transport, bus services, messenger delivery, etc.

Disruption: Just as moving from vinyl to CD’s made us all buy many of the same albums in a new format, as the “office on wheels” becomes the car of the 21st century, old cars with a driver’s seat will be ditched and new cars purchased. A couple of years later, when the average American family has 1 car or less, the auto industry crashes and half of all transportation (5 million) and auto manufacturing jobs (also about 5 million) go away.

By year 10 this rises to about 18 million lost jobs. All of those driverless cars start to eliminate car accidents and deaths. The auto insurance industry collapses. A million lawsuits a year don’t happen, and 4 million accident victims don’t need medical care. Without a steady stream of fender benders, thousands of garages close. Without many repairs and with highly reliable cars, a third to a half of car dealerships are wiped out. Patrol cars around the US become autonomous, reducing the nation’s fleet of police and patrol vehicles. Round that up to about 20 million lost jobs, in about 10 years.

End Game: If you continue to drive your own car, the cost of insurance soars. If you have an “accident”, it will have only occurred because you made a conscious decision to disable a safety feature of your car (autonomous driving). You have just surrendered your defense against damages, especially if anyone has been injured or killed. If it is even possible to get insurance to drive your own car, the price will be so high that only a handful of Americans will  be able to afford it!

In 10 years we’ll all gather in the living room and say, “That’s how it happened.” You think this isn’t possible, or that something will come along to change it? Look at the history of America. The 1800s was the century of the horse and the wooden ship. The 1900s was the age of steam, with an iron horse (trains) and iron ships. The 20th century was the age of oil, which gave us the car and airplane.

Today we are in the age of data and computers. Cars, trains, ships and planes will continue on, we will simply add artificial intelligence to drive them. Cars are already highly automated, with airbags, anti-lock brakes, and other safety features. Automatic braking will be a standard feature in a few years. Autonomy just ties together all of the features that are already in your car, and adds a few new features, and turns the whole set into a more effective service.

Driverless cars will be a chaotic disruptor simply because of the speed and thoroughness of the transition. During previous disruptions, new jobs (often better jobs) replaced jobs that were lost. This transition will destroy jobs faster than they can be created. We’re going to lose 10-20 million jobs in the next 10+ years, but replacement jobs may not arrive until after the “disruption” ends… perhaps 15 to 30 years later.

One or more generations of relatively unskilled labor will have very few choices, but higher paid workers and the wealthy retired will see many benefits. Think of changes to society as being pretty much like taking a car ride on an unfamiliar road… but the future will feel like you are driving at twice the speed while wearing a blindfold. A big wave of changes will hit our labor market in just a little while. If you want to stay ahead of that wave, keep reading this blog! At least, that’s my Niccolls worth for today!

Posted in cars, Decision Making, Robots, Uncategorized, Unique Ideas | Tagged , , , , | Leave a comment

Clean Up Your Books Before Selling Your Business!

Posted on March 20, 2016 by Chris Niccolls

Family-business

You’ve been thinking about what to do with your business. Is everything running the way that you like, or does something need to change? Did you stop growing years ago, or has a major customer started working with one of your competitors? Or is profitability down and back office costs out of control? Then you might be asking yourself if it is time to revamp your firm or sell. Before you look into a valuation for your firm, you need to be sure that the data you will use for that valuation is reliable. Surprisingly, many family run small businesses have a lot of problems in producing a clean set of numbers for an accurate valuation. Why is it so difficult for small businesses to produce reliable financial documents? Well, you could say that the problem is… ahhh… relative.

Every firm has different challenges, but family run businesses have special issues. When a buyer is interested in buying a small business that is completely run by the owner, the valuation is likely to be low. This type of “one man show” has a lot of risks. All the functions of the firm… sales, business development, operations, customer satisfaction… are managed by  just one person. If anything happens to that one person, not much is left to the purchase. That’s why a one person operation is penalized in the valuation. A larger firm with multiple family members can spread the risk around by having key positions occupied by multiple individuals, even when they are all related.

However, this benefit also has a downside. How are you paying those relatives? Often the answer is, “Through hidden payments.”  You pay some family members through salaries, and other payments may be hidden throughout the firm’s operations. When workers are family members, the family is willing to approve unorthodox payments.

As long as you keep within the bounds of common sense, this is rarely illegal. However, this practice almost always lowers the profitability of your business, which always results in a lower offer for your firm. It can also create issues of trust (and credibility) between you and the buyer. Usually, you can explain why there is a difference between your tax filings and your “real” profitability. You can credibly explain to a buyer that the numbers on your tax returns are not a complete view of your firm’s finances. But, if you repeatedly change the numbers, you will undermine that trust.

If you never sold a firm before, you will find that it takes longer than you expected to get realistic numbers to your buyer. It is not unusual for your tax filings to show a profit that is under-reported by as much as 10% to 50%. That means that if your valuation is based on profitability (or EBITDA or anything similar), your offer will be proportionately reduced. Let’s take a look at the most common expenses that get “stuffed” in the wrong expense categories and what you can do about it.

  • Payouts: A family business often pays members of the family from firm profits. In an attempt to reduce taxes, families often redefine payouts as expenses or other forms of payments. This can be done legitimately when legitimate work is performed, but it is often abused or at least poorly tracked. If you carefully track these payouts, it makes it a simple task to present a real net profit number to a buyer. Documentation is important! While some family businesses issue shares to the family, other businesses have a freewheeling process of directing payments to the family. Documenting how the family is paid goes a long way to explain last year’s tax return, and a map of payments (and voting shares?) reduces last minute debates about the conditions of the sale.
  • Compensation: Members of the family may be full-time, fully documented and taxed employees. That shouldn’t be a problem. As long as you pay market rates. It’s perfectly legal to overpay your brother-in-law. If a position could be filled for $40,000, you might pay a more generous salary to a relative and provide extra benefits that have a total cost of $100,000. That hurts your valuation. After you sell, will your brother-in-law still have this job? If he does, will he be brought down to market pay? You need to identify intentionally overpaid positions and know if you would advise a buyer to adjust, eliminate or re-staff these positions.
  • Cars: One of the most significant “family” charges are for car leases. It is reasonable to charge one or two leases to the business (the president and sales manager?), but a family owned business might have many more cars billed to the firm. You need to keep track of all car leases, mileage expenses, repairs and any other car related expenses. How many cars will the buyer pay for after the buyout? That may be a critical issue for your valuation.
  • Memberships, Travel & Expenses: All sorts of expenses are hidden in your Profit and Loss statement, like club memberships. Golf course memberships can be very expensive. A retired family member might charge one or more of these memberships to the firm. You may even make occasional use of a membership to entertain your customers. Is the benefit worth the cost? If it isn’t, track this and make it clear to the buyer that this expense will not continue after the buyout. Also, check carefully for lunch, dinner, taxi’s, and  travel/hotel expenses that are not company related.
  • Office Expenses: Not surprisingly, this can be a dumping ground for unclassified expenses. Very carefully examine the expenses and identify all family related expenses.

As you prepare your firm for sale, you need to make a shift from avoiding taxes to maximizing valuation. Small businessmen normally look for every possible tax deduction. Many of these expenses will be perfectly legitimate. But when you choose a relative to do the work… are you paying more than you would pay to a non-family member? Let’s be honest, this generosity is mutually beneficial. The head of a family business is often also the head of a family. The family head is expected to help young family members find an entry level job and provide financial aid to retired family members. But these tasks that fall to the head of the family are fulfilled by the head of the business. Also, these (over?) payments minimizing taxes, so it is easy to think that you’re more or less breaking even. Until you find out how it impacts your valuation.

The best solution is to start tracking and documenting these expenses BEFORE you try to sell your firm. Take a look at the amount of unnecessary or non-recurring expenses in last year’s profit and loss. It may be an eye-opening experience. You might even decide that you need to start being more critical of family expenses. At least, that’s my Niccolls worth for today! If you don’t agree or if you have your own ideas on this subject, comment and let’s talk about it! If you are planning to sell your firm and you need help in addressing valuation (or finding a buyer), just connect with me and we can have a confidential discussion!

Posted in Small Business, Valuation | Tagged , , | Leave a comment

UBER Drivers Are Out Of Time

Posted on March 7, 2016 by Chris Niccolls

UBER-facebookThe beginning of the end. Remember all of the old “B” sci-fi movies from the 50s that either started or ended that way? Usually with a shot of an atomic bomb exploding in the background. Sir Winston Churchill had a much better way of saying it. “Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning.” UBER has reached the end of the beginning. The world’s most heavily valued taxi service is about to become the world’s most heavily valued robotics firm.

UBER has been clear for years that Robots are part of their future. The question was WHEN will UBER will join the robot revolution. The answer is, “Real soon!” UBER’s wants to immediately replace drivers, and if they can they will start in New York. For UBER, soon is just not soon enough! Unexpected events have dramatically changed their schedule, and they’ve got to get robot cars on the street in 1-2 years.

UBER is faced with some very unique problems. We all know what UBER is, and almost everyone is aware that UBER is valued at over $60 billion. Far fewer are aware that UBER loses money hand over fist. UBER expects losses of $250 million in 2015. Any company that has never made a profit and is losing nearly a million dollars a day has to start identifying the issues and developing projects to cut these losses. You can be sure that this is exactly what has been happening at UBER. So, before we get to the end of the beginning, let’s go back to…

THE VERY BEGINNING: UBER launched in 2009, and the UBER App arrived in July of 2010. UBER was just a taxi service (named UBER taxi) and dealt with the same issues as every other taxi service. All taxis have three basic costs: cars, fuel and employees. There are always other costs, such as a garage and fines, perhaps even some advertising, but these are usually far less significant. When the UBER App launched, gasoline was $2.78 a gallon. In less than a year, it rose to over $4 a gallon and then bounced between $3 and $4 over the next few years.

In order to break even, UBER needed to control fuel costs. UBER started to buy new cars for their new drivers, partially to ensure a supply of drivers and partially to ensure they were driving fuel efficient cars. UBER even experimented with electric cars in China. But then, in late November of 2014, the price of gasoline unexpectedly fell below $3 for the first time in more than six and a half years. The price fell then fell, then rose (to $2.92), and then fell again and hit a lot of $1.84. Now, Iran is re-entering the international oil market and will add a million barrels a day of oil production. This could be a very rough ride, but overall gas prices are likely to continue their fall throughout 2016.

With the price of oil cut in half, three things happen immediately. First, the cost structure for UBER and every other transportation service falls. UBER and their competition (including the famous Yellow Taxis) will start lower their rates, perhaps starting a price war. That’s good for UBER, which can easily change fares, and bad for Yellow Taxis that require a very complex process that requires government approval. Second, if UBER can retain some portion of fuel savings, they can close the profitability gap. Third, and most importantly, cutting one of the primary operating costs in half makes the remaining two appear even larger. Especially, the cost of employees.

DRIVERS DRIVE COST: Drivers are now the primary cost of an UBER ride, and UBER rides are the most expensive in New York City. By one estimate, New York City UBER drivers make $27 per hour. For our calculations, we’ll round down to $25 per hour. Humans can only drive for so long before they need to eat or sleep or get to a bathroom. In New York City, a taxi is often owned by a garage, rather than the driver. When one driver completes their shift, they return to the garage and another driver starts their shift. Currently, most UBER cars are run by a single human. They might choose a longer shift, or even a 7 day work week, but they are not on the road 24×7. A robot car would only stop for fuel or for maintenance.  A robot car could stay on the road indefinitely, replacing not one, but several drivers.

While UBER has 14,000 cars vs. slightly less than 14,000 Yellow Taxis, many of the Yellow Taxi’s are run for 3 or 4 shifts a week. UBER plus Yellow Taxi’s are probably equal to 20,000 “24×7” cars. For the purpose of calculation, allow 2 hours a day for refueling and maintenance, so 23 hours a day and 365 days a year yields 8,030 working hours for an autonomous car. At $25 per hour, if that had a driver, it would need to pay an additional $200,750 annually. We don’t need to go much further to understand how UBER can become profitable, and take over the world’s car service market.

In order to replace these drivers, you need robot cars. If UBER buys 14,000 new cars, that allows them to replace all of their current drivers and take over the work of many Yellow Cab drivers. Let’s be very conservative and assume that the first autonomous cars that UBER buys will be $75,000… the price of a Tesla or premium gasoline car. UBER would pay about a billion dollars for new cars, but save $2.8 billion EVERY YEAR. Let’s toss in UBER’s $250 million annual operational loss, and a year after adopting a 100% robot fleet, UBER’s profit rockets to $1.5 billion. Since we loaded up all of the car costs into year 1, in year 2 profit would rise again to $2.5 billion. That’s the result of merely converting the 14,000 NYC UBER cars, and not touching the 145,000 other UBER cars around the world.

Other locations won’t have the same combination of taxi density and high wages of NYC, but many urban environments will have similar numbers. Assume that the next 14,000 cars UBER automates only yield half of the year 1 benefit ($880 million), and the next 14,000 contribute half of that ($440 million). UBER’s 1st-year  profits rise to $2.8 billion. That’s without touching two-thirds of drivers. Still, once UBER converts these cars they will be swimming in cash and be able to dramatically reduce the cost of a ride.

This is where UBER can take over private shuttle buses (Uber-X ride sharing), city express buses and perhaps realize their dream to replace consumer cars. With robot cars, it will make economic sense to pre-park cars nearby customers who need cars on-time, every day. Second generation robot cars could be designed without a steering wheel or driver’s seat, allowing a “living room” configuration that is more comfortable for a working commute to work when it is more than an hour away.  For the even longer commutes of “remote” workers, who take a 2 hour or more drive to work once or twice a week, a configuration with a bed or sleeper seat might be very popular!

DRIVERLESS CARS MOVE FORWARD: Driers are more than a cost issue for UBER. Drivers have been a problem since their early days. UBER has tried to classify  drivers as software users, and not employees, to avoid paying benefits. UBER wants to experiment with pricing methods and special offers, to improve ride volume or profitability. Drivers dislike these unannounced changes and have found that most experiments cut their pay. In early 2016, the fee for UBER-X (shared Taxi’s) was cut 15%, causing another round of driver protests. It also launched a new competing app, Swyft, launched by disgruntled drivers.  That’s the sort of news that will make UBER’s investors… unhappy. Partially because it is bad publicity, and partially because it could one day be real competition. And mostly because 2016 is looking a lot like the year that UBER will launch its IPO.

At this very moment, robot cars drive as well or better than humans. In a short time, robot cars will improve beyond the ability of humans to compete. Which is exactly what has happened with every other task robots have performed. As soon as 2017, Tesla expects to introduce a fully autonomous car. Audi has similar expectations for 2017. Google is targeting 2018 for their autonomous car. Other manufacturers may hold off until 2020. Those who are scheduling a later release date are not waiting for technology. Instead, they are waiting for new regulations and opinion statements from insurance companies.

Early pilot programs show that autonomous cars are ready to take to the road today, and can drive at least as well as human drivers. But does that mean that they are legal to drive? Nevada, California, Michigan, Florida and other states approved laws that allow at least limited use of autonomous cars. When a consumer has an autonomous car, it will almost always have a human driver in the car, they will just allow the car to drive (and be ready to take over if necessary). A taxi will always drive itself, and the passenger may not know how to drive, even if an emergency occurs. The insurance industry is just beginning to say that they will accept the car as the driver, but that doesn’t tell us who gets sued when an accident occurs (UBER, the car manufacturer, the software developer, etc.). Do you need to wait until the laws are in place before you start using a robot car? America didn’t wait for new regulations when drones, electric bikes,

Alternatively, a taxi will always drive itself and passengers may not know how to drive, even if an emergency occurs. The insurance industry is just beginning to say that they will accept the car as the driver, but that doesn’t tell us who gets sued when an accident occurs (UBER, the car manufacturer, the software developer, etc.). Do you need to wait until the laws are in place before you start using a robot car? America didn’t wait for new regulations when drones, electric bikes, smartphones, and the Internet arrived. Will driverless cars be any different?

Today, UBER has gone as far as buying cars for its drivers to ensure that it can expand its program in New York City. It seems likely that that are negotiating an insurance policy that will allow them to start a driverless car pilot program in New York. As to regulations, UBER has proven to be quite ingenious in navigating regulatory issues. New York City’s Taxi Commission controls all car services, and strictly limits the number of Taxis, yet UBER has adroitly sidestepped these regulations. That’s how UBER grew larger than the entire NYC Yellow cab fleet.

TIME FOR A PILOT: The next step is to run a pilot program. Google, Tesla, and other autonomous cars have been doing road tests for years. However, none of these tests have been for Taxis, nor have they allows members of the public into autonomous cars. We already know that it makes great financial sense for UBER to launch driverless cars in NYC. NYC also makes a great location for a pilot. Unlike a driverless truck, that might have a route that goes through multiple states and many cities, UBER’s driverless Taxi pilot only needs to go through one City, and perhaps far less than that. Remember, with UBER, you call the car and tell it where you are going. UBER could start with just one high-volume route, say between Wall Street and the Upper East Side (where many Wall Street employees live). Run it for free. Let UBER become a true expert on this one route. If no accidents occur for 30 days, expand beyond this route. When all of Manhattan is running to satisfaction, expand to the boroughs (Brooklyn, Queens, Staten Island and the Bronx).

There will be resistance from City Hall and from drivers. Perhaps even the Insurance Industry. Resistance is less likely from Taxi riders, who have always had complaints about the quality of Taxi’s in NYC. NYC has a massive tourist industry. Some tourists take home great stories about NYC and it’s Yellow Taxis. Others tell the world that NYC’s Taxis are like something out of an underdeveloped country. Robot cars might improve NYC’s image in the tourist industry. What about traffic violations and accidents?  On May 20, 1899, America issued the first speeding ticket, to Jacob German.

Do I even need to tell you that Jacob was a NYC Taxi driver?  Just a few months later, on September 13th of 1899, another NYC Taxi driver gets the credit for the first Taxi fatality. The NYC Taxi accident report was released in 1999, showing 13,134 accidents with 3,041 causing personal injury and 10 resulting in deaths. Robot cars would virtually eliminate accidents. Also, 14,000 or more robot drivers could significantly reduce NYC’s legendary traffic jams. Given the social benefits, could City Hall credibly argue against driverless Taxi’s?

Well, they might… if they looked at the employment numbers. There are 50,000 drivers behind the 14,000 Yellow Taxis and another 50,000 drivers in other forms of cars for hire. A successful driverless Taxi will accelerate driverless trucks and other vehicles. There are at least 300,000 full-time truck drivers in New York state and a similar number in New Jersey. So, across the NY Metro area, the relentless drive towards driverless vehicles will result in a million lost jobs. Not even including  a massive reduction in fender benders (and work for garages), more serious accidents (reducing medical work) and, of course, the beginning of a very rapid decline in the auto insurance industry (robot cars that don’t crash will have very, very low premiums). The stakes are very high, so there will be resistance. But resistance can only slow, not stop, the inevitable.

SUMMARY: The numbers don’t lie. UBER has such an overwhelming financial interest in replacing its drivers with driverless cars, that it is indeed inevitable. Perhaps UBER will do their IPO in 2016, perhaps in 2017, but once they go driverless they will go from losing money to multi-billion dollar profitability. And it will only take a pilot in Manhattan to achieve more than a billion dollars in first-year profits. Resistance to robot cars is FUTILE… but there will be resistance…. from drivers, politicians and insurance companies. But in the end, UBER, or something that looks just like them, is going to launch driverless cars, and then  it’s just a matter of time (and not much time) before all drivers of all road vehicles are replaced. That’s my Niccolls worth for today, and I’m sticking with it! Don’t agree? Then comment and tell me why!

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Will Cheap Fuel Undermine Sustainability?

Posted on February 25, 2016 by Chris Niccolls

SustainableWhen technology changes, early adopters become risk takers. If you sit and wait, you will eventually know which technologies win or are even worth pursuing. Early adopters either see something that the rest of us have missed, or they truly believe in a new technology. But what do the early adopters see that we’ve missed? And when do early adopters throw in the towel when they’ve clearly backed the wrong horse?

As hard as it is to remember, there was a time when everyone said that no one can outsource complex jobs. It just won’t work. Then early adopters saw the value of freeing up managers and resources that could focus on creativity and the creation of new products. Today, it’s hard to find a lot of evidence that outsourcing unleashed creativity or was responsible for new innovations. However, there is a lot of evidence that a good outsourcing program can create a pile of savings. Yet, while savings are the primary artifact of a well-run outsourcing program, survey after survey still talks about creativity and new products.

A few years ago, “sustainability” became an important term / trend in business. Especially, but not exclusively, when applied to food products. Early adopters talked about the benefits… the environment, health, family values. Sustainable fuel programs spoke to all of these benefits and… were a lot cheaper than gasoline. But now petroleum prices at record lows. If the cost savings go away, will sustainability still matter?

Everyone wants to be the hero. We want to do the right thing. At least, we want others to think we want to do the right thing. A business leader today has been hammered throughout their career to take responsibility for the environment. Other generations have been pressured to do social good, but never before has the pressure been so high, nor have business leaders (and consumers) had as many business options to make a positive environmental impact. Sustainability touches on many issues but is especially strong when we talk about the generation and use of energy.

Petroleum generates pollution. Much of our petroleum comes from the middle east, a region roiled by political conflicts and violence. In order to keep the oil flowing, the US has committed its military, its reputation and huge quantities of dollars to the ever more complicated alliances and politics that make up the middle east. Before the start of the last Iraq war, oil was cheap. In March of 1999, the national average for gasoline was $0.95. Throughout the 1990s prices remained close to a dollar. As yet another middle eastern war arrived, the price of gasoline rose to $4.12, and then began dropped to a low of $1.67 at the end of 2008, when the world economy collapsed. Between 2012 and 2014 the price of oil appeared to stabilize at just above $3.40.

Many pundits said that oil had hit it’s “new normal”. They were very wrong!  Instead this was that strangely quiet place at the top of just about every roller coaster, where new riders say, “What? That’s it? I thought this was a scary….. riiiddddeeeee!” And down we go as a new generation of energy companies came online. Down we go!  What’s that up ahead? “Drill baby Drill” became big petroleum’s new motto. Drill we did and…. down we go again! New fracking technology delivered massive new quantities of oil and natural gas from North Dakota, New York and Pennsylvania while tar sands start to ramp up in Canada. Wait! Is the ride over? Can we get off? Not yet! The Chinese economy starts to sputter and the stock market rises and falls by 1,000 points every other week. By December 2015, gasoline prices fell to $2.28, and fell again to a low of $1.83 in February of 2016.

OK, OK that MUST be the end of the ride! Nope, not yet. Get ready for the real thrill ride! None of the OPEC countries want to slow down production and give up market share. And But now  Afraid not! As of February, Iran is back on the international oil market and will ramp up to another million barrels of oil a day. As quickly as it can. But at least, that’s the end of this ride! Unless… the price of oil rises and the frackers who shut down open up those wells again. Or if they come up with a new drilling technology and lower prices again. Or maybe they start replacing workers with robots, or… ?

If oil has been on a wild ride, natural gas has been tumbling even faster. Fracking has delivered a vast amount of oil, but it delivered even more natural gas. Oilfields used to burn off gas instead of trying to sell it. When oil (and gasoline) prices peaked, natural gas became a phenomenal buy. It been at least 25% to 50% less expensive than gasoline, and it is a lower pollution fuel. Farms and factories started converting to natural gas several years ago. Some started conversion due to pollution issues, some for cost, and many as part of a sustainability program.

Natural gas comes in many forms, at different price points, but generally its use has some additional costs. Such as converting existing trucks to natural gas, or buying new trucks (approx. $35,000-$70,000 per truck), and adding storage tanks or infrastructure for pumps. Natural gas continues to be cheaper (and more environmentally friendly) than gasoline, but lower fuel costs translate into a longer pay-back time for converted infrastructure. It still makes sense to convert to natural gas, but the numbers are no longer as compelling.

Looking at other sustainable fuel programs, we have Storms Farm in North Carolina. They developed a large-scale biogas converter that turns waste from pig farming into fuel. A still larger operation is planned at Loyd Ray Farms, also in North Carolina. The exact cost of bio fuel is difficult to price. Since all sorts of organic matter can be fed into the process, and the mixture changes with the seasons, the price (and quantity) of the fuel produced will change. There are also bonus benefits of bio-fuel. Pig waste and other organic waste has to be stored and hauled away. By turning waste into fuel, these other costs are minimized or eliminated. But with petroleum-based fuel at half the price it was a year ago, fewer farms are finding that bio-fuel still makes economic sense.

 

Elsewhere in North Carolina, solar power has made huge strides. According to cleanpower.org, North Carolina is the 2nd fastest adopter of solar power in the nation.  In fact, one of the world’s largest producers of solar panels, the Chinese firm GCL New Energy, has been buying the rights to build new solar projects around the state. With direct access to China’s solar panel producers and ultra-low interest rates from Chinese banks, we can expect these projects to set a new low price per kilowatt. But, just like bio-fuel, there will be some point… perhaps at a dollar a gallon? … where solar will no longer make sense. Unless. More than just the money makes sense.

I’m not talking about going full tree-hugger here. The economics of sustainability is shifting. On the one hand, the lower return on investment may cause it to fade away. On the other hand, the definition of sustainability may become a bit blurrier and merge with other “green” initiatives, and have a higher indirect value. “Green” has become a powerful consumer and corporate marketing force. If a farm or food operation is sustainable, it is assumed to be: natural, environmentally responsible, better thought out and even more humane. The combined movement towards Green and sustainable yields intangible social benefits, and increasingly very real monetary benefits. In the US, 42% of consumers say that they would choose to buy products from Green. Let’s look at it a different way. A research report from McKinsey (the world’s largest consulting firm) surveyed consumers to quantify how much consumers preferred Green products.  In an article by Mehdi Miremadi, at McKinsey Consulting, we learn that a survey showed that only around 10% of consumers would pay of premium of over 25% for Green products, between 60% and 70% of consumers would pay 10% more. Depending on the market you want, the numbers will vary, but there is a very definite financial premium for being Green

Green Marketing

 

Consider the humble egg. Years before “sustainable” was a marketing term, “Cage Free” was well-established.  Cage free was a litmus test for the humane treatment of farm animals. Cage-free eggs started to sell in health food stores, and then in high-end specialty grocers and eventually went mainstream when they showed up at Whole Foods. McDonalds is one of the largest consumers of eggs in the world. In order to distance themselves from the “fast food” label, McDonalds has gone from liquid eggs to pre-cooked eggs to “fresh cracked” eggs. In 2015 McDonalds announced that they will shift to 100% cage-free eggs. In rapid succession, every fast food chain signed on to cage-free eggs. Target and Costco have joined the cage free movement, and Taco Bell has given its suppliers just one year to switch to cage-free operations. One year?  Conservatively, the industry needs 10-20 years to rebuild its hen houses, identify different breeds that work better in a cage-free environment, and solve a lot of other issues that drove the industry to cage chickens.

Cage-free eggs are chemically identical to caged eggs. The “greener” solution is not the more nutritious solution. But it is highly likely that cage-free eggs will also be higher cost eggs. Depending on how big poultry farms must change over, and how many old hen houses will be destroyed before their time, the cage-free movement may significantly raise the cost of eggs. All to make an egg that is no more nutritious or tasty. It doesn’t even guarantee a morally superior egg.

You see, one of the main reasons for caging is that when chickens are left on their own, they tend to want to peck other chickens to death. Especially, the young and the weak. And then, they eat each other. I’m not saying that the cramped cages are better than occasional cannibalism. I’m just saying that taking away the cage does not necessarily lead to a more humane environment. Of course, pastured hens that wander around a spacious and sparsely populated farm would be morally superior, but their eggs would be astronomically expensive. Cage-free eggs don’t do all that much to move “Green” forward, bit it sure does show the power of “Green Marketing”!

Will the same hold true for sustainable fuels? Even if sustainable fuels lose most or all of their direct cost advantages, will a “sustainable” label make up for cost in marketing advantages? As we’ve seen, Green related labeling definitely allows you to charge more for your product. What have we learned? Consumers are interested, very interested, in the social benefits of Green products. But you need to know where your customers stand on this issue. The more Green your customers, the more they are likely to reward you financially for going Green. Which means that you not only need to know how Green your customers are, you need for your customers to know how Green you are! Conventional (non-organic) farms differ dramatically in their green commitments. Some conventional farms are arguably more Green than many of their organic peers. If your customers are green, and you are green or going green… SAY SO! And that’s my Niccolls worth for today!

Don’t agree? Do you see other trends dominating in sustainability? Let us know!

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What Are Small Businesses Selling For In 2016?

Posted on February 20, 2016 by Chris Niccolls

money

Over the last year, I’ve written a number of articles about small businesses. One of the most frequently asked questions is, “What exactly is my firm worth?” Fortunately, I can tell you with absolute precision that the answer is… “it depends!” It depends on the profitability of your business, and it depends on how long you have been in operation. It depends on how your business fits into the buyer’s organization. And it depends on who you ask. I’ve seen multiple buyers offer under 1 times profit for a business while the seller is sure that they are worth at least 6 times.

I’ve seen both the buyer and seller on a deal paying to get a professional valuation, by valuation experts, and coming up with completely different numbers. Formulas are rarely the same, so we shouldn’t be surprised that sellers tend to choose a formula or a source of information that is usually high, and buyers usually choose a process that leads to a low number. That’s just human nature. So, today, we’re going to go past formulas and opinions and look at real data on businesses that are being sold today. Let’s jump right in and see what your small business is worth!

We need to understand a little bit about methodology and focus on service companies. Why? First, they are simpler to deal with. A supermarket,  farm or factory has equipment, inventory, and land to deal with. Each of those assets has a different process for valuation. Land can be very difficult to value if there have been no land sales in the area for a long time. Inventory is usually sold at a significant discount (30% or more), rather than at the purchase price. Cars and trucks use blue book prices, but very specialized mechanical equipment in a factory is harder to price. The second reason for focusing on service companies is that 80% of all employees in the US work in the service sector.

Many 1st time sellers make a big mistake by trying to use data from publically traded firms as their basis for valuation. Publically traded firms are sold for much higher valuations than small businesses. Publically traded firms pay millions of dollars in fees to investment banks and consulting firms in order to be on stock exchanges. They follow very stringent standards, and must meet standards for revenue and other business performance standards.  This ensures that they are considerably greater value than the average small business, and it is reflected in their higher value. Publically traded firms also provide much more information on their operations. The Security and Exchange Commission (SEC), the primary government regulator for publically traded firms in the U.S., requires annual and quarterly reports on all aspects of operations.  Privately owned small businesses rarely reveal any information at all. Private businesses must put in a lot of effort to find firms to buy and sell, to say nothing of what to pay.

While small businesses cannot turn to public data for valuation, there are private websites that sell firms. The biggest and the best of these is BisBuySell.com. With over 45,000 active listings, BizBuySell.com is the largest website in America for small businesses “classifieds”. BBS is partially owned by the Wall Street Journal, which used to be the dominant print publication for small business classifieds. BBS and their competitors are the evolution of classified ads. They not only allow you to buy and sell businesses, they also provide statistics on listings and sales.

Every quarter BBS produces “Insight” reports, aggregated information on market trends. Additionally, at any time, you can use their search engine to look for other firms that are similar to yours. You can see location, income, profit, asking price and other information. While income and profit are real (on the word of the seller), the asking price is what the seller wants to receive. In reality, the selling price runs about 15% lower than the asking price. In Q4 of 2015, the average asking price (for all businesses) was $230,000 and the selling price was $200,000. It’s difficult to say how many firms are sold, since a listing can be stopped and restarted under a new ID, an unsold firm may move to a different listing service, the owner may remove their listing and try again next year, etc. Over a full year period, only 16% of BBS listings are eventually sold.

Why are so few businesses sold? Classic economics tells us that if you aren’t selling enough of a product,  the price is too high. However, I think it is more likely that the issue is visibility. It really is that hard for the right buyer and seller to find each other. Buyers don’t want it known that they are buying because it could raise prices. Sellers are even more cautious. The rumor or a sale can cause both customers and employees alike to flee. Once everyone knows that a sale will take place, it’s a matter of sell or die! At least, that’s what many small business owners believe.

There is some truth to this. The smaller the business, the more likely that the owner is a “one man show”. They run the business, take care of customer support and manage the clients. If they leave, there isn’t much left to sell. Larger firms generally have to delegate responsibilities, which provides some protection during a sale. The secrecy behind the sale of a small business makes it difficult for buyers and sellers to find each other. Unfortunately, that means that for all too many small businesses, the exit plan is to close their doors.

Now, let’s tighten our focus ever more, and just look at a specific type of service company, staffing firms. If you’re not a staffing firm, you probably think… “That’s pretty specific!” If you are a staffing firm, you’re thinking… “That’s not enough information! Where are they located? What is their discipline? Tech should be more valuable than admin! And if they are SAP or other sub-disciplines, that should be worth even more!”  The more we know about our own business, the more data we want!

Unfortunately, it is unlikely that you will be able to find that level of information. While the BBS database has 45,000 active listings, when I filtered for staffing firms and filtered for listings that showed Gross Income, Cash Flow (or EBITDA), Asking Price and, at least, $750,000 in income (below this it is a “home based business”), I only get 20 businesses. The same is true in any other detailed search. Yet, even from this small and unscientific sample, there are trends.

The key numbers for most businesses are the “multiples”. This is the multiplier that you apply to either your gross income or to EBITDA. Generally, the EBITDA multiplier is more reliable and of greater importance. A buyer is better able to understand what they will pay for a firm that generates a profit of $500,000 (without knowing the revenues) than it is to make a bid for a $10 million firm (when you don’t if there is any profit). Cash Flow is not the same as EBITDA and may not be the same as profit, but they are close enough to be used interchangeably. The multiple for Cash Flow, for our sample, is either 2.2 or 2.3 depending if you use a weighted or unweighted average. Let’s split the difference and call it 2.25. That means that a business with a profit of $100,000 has a value of $225,000. This just creates a baseline, and then all sorts of adjustments will be made to come up with a final number. However, please remember that this is based on the Asking Price, not the price that was paid. We know from earlier numb that the price paid is 15% less than asking, yielding a multiple of 1.9.

Some of you may be thinking, “Is that it?” or “ I can’t retire on that!” If you are a small business that earns a million dollars or so with a profit of around $100,000… it may not make sense to sell your business. Not as a retirement strategy. You might get  a one-time payout that adds retirement portfolio, but it can’t serve as your entire retirement strategy. Non-service businesses might have property, inventory, and equipment that is worth far more than the annual revenue. However, these numbers are only averages. As your business gets older and grows larger, these mature firms tend to have a higher value. While not all older firms become bigger firms, few big firms are new. Being “established” does impact the value of your firm. Our sample size is too small be definitive, but let’s see what our sample looks like when we plot it.

Non-service businesses might have property, inventory, and equipment that is worth far more than the annual revenue. However, these numbers are only averages. As your business gets older and grows larger, these mature firms tend to have a higher value. While not all older firms become bigger firms, few big firms are new. Being “established” does impact the value of your firm. Our sample size is too small be definitive, but let’s see what our sample looks like when we plot it.

This chart plots all of the small businesses, showing the cash flow multiple against the age of the firm. I then put three bubbles on the chart, with each bubble being 1 multiple wide. As you can see, the first bubble takes in all but one outlier. However, the outlier is so close to the first bubble that we should include it in the same grouping (multiples of 1 or less). All firms under 5 years old are in this bubble, even though one firm is 17 years old.

Ask Price 3

Next we have an empty area between the 1st  and 2nd bubble.  No firms are selling for 1.5, 2, or 2.5 times cash flow. The 2nd bubble clearly contains older firms. The youngest are 10 years old, and the oldest is 29 years old. These firms have asking multiples of 2.5 to 3.5. The third bubble, which only has 3 firms, should contain the oldest firms, but this is not the case. Instead, firms in the 3rd bubble are younger than the previous bubble, but still older than the 1st bubble. If we join the 2nd and 3rd bubbles, we have the 1st group with low valuations, followed by a “dead zone” at 2-3 x cash flow and then the 2nd bubble with firms asking for a multiple of 2.7 and 4.4.

Ask Price 2

Let’s sum it all up! A lot of business are put up for sale every year, but not every business gets sold. Today, most businesses that are for sale are service businesses. Because service firms usually have no property, equipment or assets, the valuation process is simpler but valuation is never that simple. With many methods to calculate a valuation, no two individuals will produce the same numbers. To get a better idea of what is real, we extracted come live examples from the BizBuySell database. Overall, small businesses sell for around 3 times cash flow.

For our specific sample (staffing firms) we found that firms sold for multiples between 0.2 and 6.3.  Obviously, anyone who wants to sell their business would far prefer to get a valuation of more than 6 rather than less than one. How do we get the higher valuation? There’s no single answer, but the most important factor seems to be the age of the firm, probably because older firms are on average larger and more profitable.

Overall, small businesses sell for around 3 times cash flow. For our specific sample (staffing firms) we found that firms sold for multiples between 0.2 and 6.3.  Obviously, anyone who wants to sell their business would far prefer to get a valuation of more than 6 rather than less than one. How do we get the higher valuation? There’s no single answer, but the most important factor seems to be the age of the firm, probably because older firms are on average larger and more profitable.

That’s about it! You now have another tool and some guidance on how to value your firm. But in the end, the only thing that really matters is the deal that the buyer and the seller agree to. A valuation tells you something about what your firm is worth, but how you present your firm and how you negotiate will also impact the sale price. Whether you are the buyer or the seller, you need to listen to the other side, understand how they arrived at their price and always ask about the details. At least, that’s my Niccolls worth for today! If you’re buying or selling your business, give me a call and I might be able to help you put together a deal that fits your needs!

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First View: Legal Tech NYC 2016

Posted on February 4, 2016 by Chris Niccolls

Legal Tech PortI hope everyone at LegalTech had a great time, there certainly was a lot to see! While I like to do an overview of new technology every year, this year I wanted to focus on answering a big question about legal technology. Legal pretty much developed all of the modern document management systems we use today. Then it was legal that came up with the automated ediscovery and continuous learning software. For years, legal led the world in developing technology that could take over the work performed by entry-level associates, moving basic knowledge work from humans to machines. Now, the rest of the world is catching up quickly.

In just a year or two, we can expect fully autonomous cars and taxis to hit the road in large numbers. A Deep Learning system, Watson, was created to beat human contestants on the game show Jeopardy, and has now “graduated” to consult with oncologists at Sloan Kettering; Watson is acknowledged to be better at designing a course of treatment for lung and breast cancer than any human doctor. What was my question for LegalTech? Just this… has the Legal world squandered those years in the lead, and will it now need to do a mass transition to TAR (Technology Assisted Reviews),  CAL (Continuous Active Learning), and Deep Learning Systems.  I don’t have an answer in this blog, but LegalTech ain’t over… till it’s over!

For this first installment, I’m just going to mention a few of the highlights from the Key Note speech. A bit later, I want to get back to the Big Question! So, here’s a few early tidbits to think about!

 

Rate Of Progress: It was pointed out early in the Key Note speech that sharpened turkey quills were once the key document technology in law firms. Just a reminder that while legal firms always move slowly to adopt new technology, they do eventually adopt.

Unmet Expectations: Jurors expect to see technology in the courtroom that looks like the court technology on TV. Rarely will those expectations be met in a real courtroom. Most courtrooms don’t have WiFI, computer projectors or other consumer level technology. And they don’t have IT techs waiting around to support your problematic PowerPoint presentation. Too often, legal teams don’t think of think of bringing along a paper backup for their electronic presentation… in case they run into an insurmountable technical problem.

Paper, Really?: While we are all talking about e-discovery, automation and Continuous Active Learning, just about all of that technology is targeted at pre-trial. Not the courtroom. Judges on the Key Note panel are still seeing lawyers who show up with paper documents for the jury’s review. Too often they show up with just one copy and expect the court proceedings to stop while a single copy of a document is passed from Juror to Juror.

Missing Evidence: It’s difficult to say what the true level of technology is in court cases since less than 1% of cases actually make it to court. Is the technology used for the 99% better than the technology of the 1% that makes it to court? Possibly! If the discovery process is productive, in a positive or negative way, it may force an out of court settlement.

Onshore, Offshore… Not Shore?: The technology that’s most important to us is not necessarily the technology that we want to discuss. Take the Cloud, for example. When documents are in the cloud, it’s time for your legal staff to play that fun game, “Whose jurisdiction is it anyway?” Data in the cloud physically reside anywhere. Someone’s email) could reside on several servers in different countries. It’s common for company management to discover the real location of their data, only after litigation has started. Microsoft is experimenting with offshore, underwater, data centers. They say it’s because pf the low cost of land and the availability of water for cooling. Really? That’s going to offset the cost of building and servicing these centers? Is Microsoft building a more robust data center, or an offshore legal defense against government oversight? In either case, if undersea data-centers catch on, I hope the FBI has a budget for wetsuits!

Old Rules Are Good Rules: No matter what, the twin rules of proportionality and reasonableness apply! If it takes $1,000,000 of effort to settle a case worth $10,000, you probably don’t need to do it. Go to the judge and talk about the dollars. Judges understand that discovery can be very expensive, and when it is too expensive they will listen to you. Especially if you propose a reasonable alternative. Don’t throw money way, have that discussion with the judge!

Changing Terms: It used to be that the most used term in legal technology was TAR, and now CAL (Continuous Active Learning) is becoming the term, and the technology, of preference. While they are not identical, CAL has many of the same attributes Natural Language Programming and Deep Learning. The combination of these tools and techniques move well beyond the focus of today’s e-tools, which is generally to support associate tasks. These tools, in other professions, are replacing doctors and financial analysts. There was only a whisper of these tools at this year’s LegalTech, but this is an area where we can expect enormous advances in the next year or two.

Did He Really Say That!: The best new phrase of LegalTech… Judge Lorenzo Garcia urged technologists from opposing parties to work together more collaboratively. How closely? The judge said that in the dance of technology, your IT departments should be, “Dancing Geek to Geek”!

And that is my Niccolls worth for today!

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How The Outsourcing Engine Will Change The World

Posted on January 25, 2016 by Chris Niccolls

EngineIn our last blog, we looked into how robots and learning machines are positioned to take over professional jobs: doctors, lawyers, and corporate MBA jobs. Advances in learning systems allow robots to learn the same way humans do, by reading and by doing. Advanced robots are paired with experts. The expert gives the robot work assignments and gives feedback on the work. All in plain English (or French, or German, depending on the expert). Just like an apprentice,  the robot’s work improves with every assignment and soon looks like the work of the expert. But is it not just learning to copy a specific document, it is truly learning about what makes an expert, an expert.

An example of these learning systems is IBM’s Watson. Watson first rose to the public’s attention when it won the game show, Jeopardy. If you’re not familiar with it, Jeopardy is a knowledge contest, often involving obscure knowledge that also requires interpretation of a pun, an inside joke or an intentionally twisted turn of phrase to choose the correct answer. Since computers can’t understand a joke, a computer can never beat a human. Or so the logic went, until February 14th, 2011 when Watson trounced the world’s best Jeopardy players.

IBM_WatsonThat’s impressive! But winning a good game isn’t the same as working in a real job. That’s why IBM took it’s learning system and let it teach itself to be an expert in… Oncology. In a year, Watson had completed medical school and was ready to advise experienced cancer specialists at the world best-known cancer treatment center, Sloan Kettering. Watson does what mere humans cannot do, Watson reads (and understands) every single document written on cancer treatment. Watson can (and does) read millions of individual cancer treatment records, tracking and analyzing every patient and learning when a treatment works or doesn’t (do specific genes, medical histories, previous illnesses, family histories, and other factors correlate with better recovery?).

NEW ROBOT CAPABILITIES: Watson can identify, better that any doctor on earth, the specific relationship between every patient and factors for cancer remission. Even more importantly, Watson has moved beyond just mimicking the best practices of its teachers, it is creating new methodologies of its own. This robo-doctor never forgets a scrap of patient history, always remembers treatments that will improve recovery, is completely up to date on every article ever written, and every new drug and treatment that is available… and now it is developing new treatments that other doctors have not discovered. In a few years, if you have a terminal disease would you want a robo-doctor or a human doctor that can be impatient, forgetful, and have other interests than just the patient?

You’ve heard it all before. Robots are coming for your job! Luminaries such as Elon Musk and Steven Hawkins are convinced that the robots want more than your job. They think robots are going to replace the human race. Maybe they will, but not today, and not this week. It’s going to be quite a while before robots will benefit from ambition, or desire or anything else that leads to THAT kind of revolution. The issue that we are facing is that robots are about to do the work of knowledge workers.

Old robots could follow rules and instructions, but they couldn’t innovate. They couldn’t make a decision or come up with any answers, they could just repeat what they had been previously programmed with. New robots are designed to go beyond their programming. They make decisions, they innovate, they take actions (and come to conclusions) that they were not programmed for. If the last generation of robots and thinking machines took over simple jobs, the new robots are being designed to take over for managers, college educated professionals, doctors, lawyers, and especially MBA’s. The last wave of outsourcing and automation is still going on, and new technology is pushing job replacements to a new and much, much higher level.

In a moment, we’ll go into the details of how capable these robots are. First, however, we need to dispel a misunderstanding. All of this talk about robots taking over sounds far-fetched and clichéd. Probably because Science Fiction writers have been saying this for almost a century.   And it hasn’t happened yet. Well, sci-fi writers come up with some pretty good ideas; after all, their job is to think and write. But if you read the best of these sci-fi stories, they placed the revolution into the future. Usually, the 21st century. Still, rather than relying on science fiction, or “futurists” or even the scientists and economists that have joined the chorus, you might want to look at history. History? Yes, history. Surely, you did think this was the first time almost every job in America was taken over by machines? Here’s a quick history lesson.

(UN)EMPLOYMENT IN AMERICA: There have been several revolutions in how we work, mostly driven by new technology. Back in the 1980’s a futurist by the name of Alvin Toffler wrote a series of books about how this cycle of technological change works and how it would affect the future (well, that future is now our past). The key phrase from those books, which has become a part of our culture, is “The Third Wave.” The type of robots and learning machines we are faced with today are the beginning of “The Fourth Wave”. Here is a short version of the theory, as it applies to America.

  • Farms: For thousands of years, wealth came from the land and from farming. This was the first wave, that allowed humanity to rule the world. America was founded by farmers, and the rich land attracted new settlers. Agricultural employment reached a peak of 70% of the workforce in the 1850’s and declined to today’s low of just 1% to 2% of the labor force. Agriculture is still economically important, it just doesn’t provide jobs. Technology replaced human and animal muscles with machines. Later computers continued the process, further reducing the need for human labor. The reduction was mostly completed by the middle of the 20th From being the biggest source of employment to being insignificant (and with employment still falling) took 150+ years. Old farming is gone, and will never return as the major source of employment it once was.
  • Factories: This was the second wave. As machines slowly eliminated farm jobs, new factories had time to be built. Eventually, millions of former farm workers and laborers, especially from the south, moved into northern factories, making factory jobs over 40% of the work force. We have long since forgotten the staggering social, economic and political changes (including a civil war) that accompanied this change. Only a few fairy tales are left, like the tale of John Henry, “A steel driving man”. The tale both praises humanity, even though it tells us that the machine will always defeat even the best of us. To coin a phrase, “Resistance is useless.” Local economies rose and fell. In the end, America became the world’s premier manufacturer, with the manufacturing sector peaking in 1965, at 40% of the workforce. Eventually, automation and outsourcing arrived, and employment was slowly eroded. Today, just 8% of the US labor force works in factories. And the number continues to fall.  It took just 50 years from peak employment to today’s low. There are still a few manufacturing jobs left, but they too will go away over the coming years. More importantly, the second wave was much faster than the first wave… because it built on previous innovation and knowledge.
  • Data: Computers arrived the post-industrial economy began. Workers were soon fleeing the rust belt. Factory jobs were in decline, now data was what mattered. Factories were replaced by banks, financial firms, information services, news and media firms, the film industry and the rising world of “information”. Data services and software firms became worth more than on industry. Soon, the new economy provided automation and robots to disassemble the old economy. Robots replaced autoworkers, and jobs moved offshore. Cheap telecommunications and plentiful computers meant more workers used a computer, and office jobs could now be outsourced… at first t lower cost states and then offshore. Computers spread to factories, farms, schools, and our homes, and then evolved into tablets, smartphones, and soon…  smart clothes. The first generation of the “Outsourcing Engine” was tied together consulting firms, corporations, offshore services, the Internet, and computer companies. Services are still the core of American employment, but millions of jobs have been offshored or automated) over the last 20 years. Traditional corporate positions, such as secretary, have gone away… and will never return. Now, the 4th wave is just becoming visible on the horizon!
  • Knowledge: The last wave decimated clerical and support positions in America. The fourth wave is coming for professional positions. The clerk was outsourced, now the manager will be replaced. “Do-ers” are gone (or going) and “deciders” are about to go. If you manage or have expert knowledge, you are targeted for outsourcing. Learning systems will, and must, replace decisions makers. Managers, leaders, department heads, VPs, directors, analysts, researchers and similar titles that identify 40% of the jobs in America… will eventually go away. Technology (robots, learning systems, the internet, etc.) is linking up with a more advanced version of the Outsourcing Engine that was created in the last wave. There is, however, an important difference in the fourth wave. The first wave took 150 years, the second just  50 years, the third wave (still ongoing) just 20. Each wave becomes more efficient and faster than the wave before it. The Outsourcing Engine… can learn!  The fourth wave will hit our shores in just 2-5 years. That means that 50% of knowledge jobs will be replaced by robots in just 5 years, and the rest will be replaced in another 5-10 years. By 2030, the core of American jobs, 64 million knowledge jobs, will be gone forever.

The evidence of acceleration in job replacement is there, yet very few consider the increasing speed of job conversion when they discuss the robot revolution. Just like a wave on the ocean, the faster the wave the greater the impact! Knowledge jobs are no less important to our economy than factories or agriculture was to previous economies.  Yet, previously dominant sectors are gone. Forever. The same pattern has played out in England, France, Germany, and other developed nations. And each wave has had political consequences. The second wave created the Russian revolution and Nazi Germany (and Nazi Italy, Spain, etc.); post-war industrialization caused a need for oil and the rise of the Middle East. The third wave initiated the collapse of the Soviet Union and the fall of communism. If that’s not enough to think about, consider China.

THE FOURTH WAVE: Until the 1980’s, China was a communist nation with an agricultural economy. China then initiated a series of changes to, join the West. Technically, China is still communist, but they act like the world’s largest Capitalist economy. Since the 1980’s they have been racing to modernize their economy. As a result, China is the largest component of the “Outsourcing Engine” providing, food, manufactured goods and (more recently) financial and information services. China needs to move its internal job market upscale, AND it needs to continue taking over western jobs. How effective is China at modernizing its labor market? In 1980, 70% of Chinese workers had farm jobs. Today, 35 years later, it is only 30%. In another 10-15 years, only 5% of the employment will be farm jobs.

Love it or hate it, that is a seriously impressive employment accomplishment! It is also further proof that each wave moves faster and hits harder than the previous wave. What is not so obvious is the true scale of the change in China. In the US, agriculture gave way to manufacturing when America had a much smaller population. US agricultural employment never exceeded 40 million jobs in total. In China, where the population is over a billion, there were over 600 million farm workers. China has eliminated 320 million agricultural jobs in just 35 years.

That’s more than the entire population of the United States. In fact, if you eliminated the entire labor market for the US, it would just be 161 million positions. We would need to eliminate every job in Germany, France, Italy and the UK, and we should still only reach 290 million. If we then eliminated the labor markets of Norway, Sweden, Ireland, Belgium, Denmark, Finland, Slovenia and Slovakia, we match China’s accomplishment. Or half of it.  China then created another 320 million jobs to replace the ones that went away. When the fourth wave hits America, we don’t have a plan for replacement jobs.

China is just now entering the US and European Banking, Insurance and Financial markets. The three largest banks in the world are Chinese. We can expect China to be as aggressive in pursuing growth in Western financial markets as they have been in their other fields in their home markets. The US or Europe might hesitate in using the latest robotics and learning systems technology to improve the efficiency of financial services. China, however, will not. China needs an advantage to break into our financial markets. Even if Chinese banks are slow to adopt robots, Chinese outsourcers must be aggressive. Our banks, Chinese banks, Chinese outsources… and many other parties… are all components in the global “Outsourcing Engine”.

Any one component of the outsourcing engine may fail to fully utilize an opportunity. But it is rare indeed that every component ignores a significant opportunity to do what the engine does, convert domestic employment into something more efficient or less expensive. The engine creates a “pull” or a “push” on one side, resulting in the opposite effect on the other side, driving the engine forward. If a new outsourcing or automation technique arrives, it automates away the position or pulls it offshore. If there are cost pressure domestically, if the economy drops or if a position is hard to fill (and the salary rises), there is a push that moves the position offshore or adds technology to make it more efficient/lower cost.

Knowing all this, is it still so incredible that an army of intelligent robots (quite possibly made in China), will hook up with the Outsourcing Engine to eliminate a mere 62 million US jobs over the next 10-20 years? Is the number too high? Is the time too short? Or is this a logical, straight-line projection, based on similar sequences of events in several previous economic periods? Will the fourth wave be just another hollowing out of the “core” of our economy?

Wave2The fourth wave is almost here, and it will indeed sweep away these jobs. It will happen so swiftly that we don’t be able to replace these jobs as quickly as they disappear.  Some pundits say that at least some can soften the impact by getting yet another degree, and maybe yet another a few years later. That might work for a small number of workers, but for a generation that has been crushed by the cost of student loans, that might just buy a little time without improving their lives, or they financial situations. There is, however, one possible way to escape the robot revolution.

This revolution is one of the two great economic disruptions of the 21st century. The other great disruption is the collapse of the environment. What if, just possibly, these two great disruptors… just one of which could cause the collapse of our economy, could work together to save our jobs and the world? That’s right, in our next blog I’ll show you how two wrongs are going to make a right! At least, that’s my Niccolls worth for today!

What do you think? Do you see a different trend, or are yo aware of a technology that could lead us into a different future? Let and the readers know… or just contact me with your ideas!

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