Consulting is episodic. That works better for consultants than consulting companies.

Consulting is an episodic line of work. That’s great for the individual consultant because of the sheer variety it provides: different industries, companies, people, jobs and problems to solve. You get to see not only how many unique ways companies do similar things, but (if you’re paying attention) you’ll understand why. Plus, every problem calls for different sets of knowledge and skills, meaning you’ll get the chance to learn as much as you already know.

The episodic model has been good for me. It’s given me the opportunity to work in a wide range of industries: commercial insurance; investment and retail banking; wealth management and proprietary trading; commercial leasing; heavy equipment manufacturing; philanthropy. I’ve been able to work on a wide range of problems, from order to cash, shop floor to trading floor, new machines to service parts to used machines and more service parts, raw materials purchasing to finished goods shipping, underwriting to claim filing, credit formation to fraud prevention. Plus, I’ve been able to solve for multiple layers of a single stage of a solution (the experience, the technical architecture, the accounting transactions, the code) as well as the problems unique to different stages (the business case, the development of the solution, the rescue, the rescue of the rescuers, the acceptance testing and certification, the migration and cutover, the cleanup, the crisis room after a bumpy rollout, the celebration for pulling off the implementation everybody said couldn’t be done.)

(Yes, that’s a lot of run-on sentences. It’s been a run-on career.)

At some point in time, with a company you’ve never worked with in an industry you’ve never been in before, you recognize classes of need when nobody else does. You see things differently. I’d like to believe this is the benefit of working with an experienced consultant.

The episodic nature of the work provides meaningful variety for the consultant provided the consultant does not get staffed in the same role to perform the same task over and over again. Variability of experience evolves the individual; specialization stunts career development. While a person can become valuable as the go-to person for a specific need at a particular moment in time, needs, like fashions, come and go. To wit: ISO 9001 compliance consultants, SAP implementation project managers and Agile coaches don’t command the hourly rates they once did. To the extent that those things have value today, the knowledge of the nouns is more valuable than knowledge of the verbs.

There are career risks and downsides to being in an episodic profession. Consultant labor is part of some other company’s secondary workforce: when economic times get tough, it’s easier for the buyers to cull the consultants than badged employees. Even when the economy is humming along, the contract will eventually run its course and it’s time to move on. The consultant can’t talk publicly about the companies they’ve done business with and must be judicious in anything they do share. And for every company with the toxic culture and petty employees you tolerate because you know you won’t have to work with them for long, there’s a company with a healthy culture and great people you wish you could work with for the rest of your career. But that’s not an option, because you’re valuable to any one client for what you learn by working with many, many others.

Still, on the whole, it’s a great line of work.

* * *

The episodic nature of consulting is great for consulting companies when there is more demand for services than there is supply. This is especially true when that demand is ambitious in nature: the proliferation of affordable personal computing in the 1980s, the commercialization of the internet in the 1990s, the mass adoption of mobile computing in the 2000s, the expansion of cloud computing in the 2010s. Big technology changes meant big spending, and wave after wave didn’t just fuel growth of services, it smoothed out what would otherwise have been more volatile boom and bust cycles for technology services firms.

When the spending pullback comes, consulting companies don’t much like the episodic nature of their business model. The income statement lurches and contracts. A surprise pullback by two or three clients leaves consultant labor idle. A surge in proposals expected to close but not yet closed creates uncertainty as to if, when, and how many new people to hire. Uncertainty frustrates investors and stresses out managers.

Recurring revenue, on the other hand, means predictability in cash flows, which means fewer ulcers and less frequent boardroom rants. In consulting, recurring revenue comes from the more mundane aspects of technology, things like long term care and feeding of digital infrastructure and legacy software assets. (Software development consultancies have tried to use the “products not projects” mantra to change buying patterns to no avail: it remains episodic.)

Repeatable revenue comes from repeating tasks. While there are episodic events in the delivery of those repeating tasks - incremental improvements to deployment scripts and production monitoring - there is considerably less variability in the nature of the work itself. Repeatability industrializes the work, and with it the workforce. Where labor and employer share the risks of the episodic nature of project-based consulting, labor carries the bulk of the risk of the repeating revenue: the incremental improvements that reduce labor intensity; the codification of tasks that enable the work to shift from a low cost labor market to a lower cost labor market, and to a still lower cost labor market after that.

* * *

The software services industry hopes that AI is the next incarnation of demand for consultant labor. As I wrote last month, AI has not yet proven to be a boon to consulting firms in the same way that cloud, mobile computing, internet and personal computing were.

At the same time, there is a lot of hand wringing over the damage AI has the potential to do to employee development. If AI is ever truly capable of replacing swaths of software developers, the skills development pipeline will thin out considerably. If it is “I do and I learn” as the old saw goes, then there is reason to be concerned with “AI does it for me and I’m none the wiser”. (Again, please do not misunderstand this as a statement of imminent doom for fundamental skill acquisition among junior consultants. Maybe someday, certainly not today.)

But I can’t help but think a preference for recurring over episodic work will have a bigger impact on the development - more accurately, the impairment of the development - of future knowledge workers in consulting firms. Business models built around specialization of knowledge curtail the variety of the variety to which people are exposed. As I wrote above, it isn’t just exposure to a variety of different projects, it’s a variety of different solutions in different stages of their evolution to different problems in different businesses. (Jimmy Collins missed the mark quite badly in that regard. As, of course, did so many among the pantheon of his anointed “greats”.)

* * *

This preference for recurring over episodic income streams is playing out in a lot of industries, including automotive and high tech manufacturing. This portends a curtailment of product innovation and with it, the advancement of capabilities: while a manufacturer may squeeze more functionality out of deployed hardware through services, it will be functionality of convenience more than capability. Simply put, the OEM uses the incumbency it has (through e.g., high switching costs) with captive customers to extract more rent for the solutions it engineered in the past. Loyalty isn’t earned as much as it is exploited.

In consulting, a preference for the recurring over the episodic portends smoother cash flows but a depletion - potentially quite rapid - of competency in its workforce. A consulting firm is a services firm, but a services firm is not necessarily a consulting firm. A services firm may be a supplier of specialized labor, but a specialist and an expert are very different things. A services firm incubates specialists with training and certifications; a consulting firm incubates experts with exposure to diverse clients, projects and responsibilities. The two can cohabitate, but a services firm will have a dominant ethos: provider of utility services or provider of value generative services. The prior supplies specialists to fill roles; the latter supplies experts to frame opportunities and develop solutions.

Companies in growth industries grow by gambling on their smarts. Companies in ex-growth industries grow by extracting more rents from captive customers. A consultancy pursuing rents no longer believes in, and perhaps no longer has, its smarts.

Bosses at troubled companies say they want growth through innovation. They prefer growth through girth.

The headlines are heavy with iconic companies that have hit the skids recently, from Starbucks, to Boeing, to Intel. All have relatively new CEOs, each of whom has said that their respective company's path to salvation lies in returning to their roots, to once again be a coffee shop or an engineering firm. The assertion is that by going back to basics, they can regain the crown of leadership in their respective markets.

The problem is, there is no going back. The combination of circumstances that created the conditions for rapid growth and market dominance are long gone. The socio-economic factors have changed. The regulatory environment has changed. The key technologies have changed. The supply chain has changed. The competitive landscape has changed. Don't bother with the flux capacitor.

What those bosses are really saying, of course, is “we need a do-over.” But there is no do-over. Not only are years of financial engineering not easily un-done, they created a financial burden that operations have to carry by generating copious free cash flow. Sorry, but no matter how desperate the situation, the new CEO is not the William Cage figure in the Edge of Tomorrow.

While the Starbucks and Boeings grab the headlines at the moment, there are many once iconic companies that backed themselves into a corner by starving operations to feed the balance sheet. Time ran out, old management was shown the door, new management is ushered in.

This is the playbook that new management follows.

The first step is to alleviate immediate financial pressures, starting with the balance sheet. This means any or all of: maxing out credit facilities, selling assets, raising cash through equity sales, taking the company private, breaking up the company, and in the extreme filing for bankruptcy protection. Investors may win (a company breakup can work out well), investors may lose (dilution of equity), and investors may get wiped out (common equity is valueless in bankruptcy).

Fixing the balance sheet buys time, but not a lot of time, so the income statement needs to be shored up as well. Quality problems? Losing customers? Margins too thin? The playbook here is well established: simplify operations, promote quality above all other metrics, reduce contractors and staff, cut discretionary costs, slash prices, over-reward customer loyalty, etc. The good news is, customers mostly win. The bad news is, employees mostly lose, as they will face perpetual cost cutting efforts ranging from the structural (opportunistic terminations) to the petty (workplace surveillance).

Which brings us back to the CEO statement that “we need to become who we once were”.

Unless there are high-value trophy assets, or a large portion of the debt can be saddled onto divisions spun off, the financial stabilization effort will leave a balance sheet that is out of proportion with the income statement. That is, the balance sheet is structured for a company with higher sales growth and stronger cash earnings. Before it does anything else, the company must face the fact that financial stabilization isn’t going to return the capital structure to what it was before all of the financial engineering happened.

That has serious repercussions for operations. Recapturing lost competence only solves the growth problem if the core business can once again be a growth business. This is the fundamental hypocricy of CEOs trafficking in "back to the future" statements: the core business has to be a growth business or returning to core competencies solves nothing. Absolutely nothing. And they know it. Being good at what you used to do well will staunch decline, but it offers no guarantee of a return to growth if there’s not much growth to go round. It is also worth pointing out that the “we need to become what we once were” statement also masks the actual objective: it isn’t to be the pre-eminent firm in the market the company made its name at, as much as it is to resucitate the income statement to a point that the balance sheet makes sense again.

If the core market is slow- or ex-growth, the go-to strategy is to capture a greater share of total customer spend, a.k.a. “revenue grab”. The opportunities here include extending the brand by selling adjacnet products and services (e.g., as GE famously expanded from selling nuclear power plant technology to servicing nuclear power plants in the 1980s). Another is selling against type: where a company once differentiated from its competition by refusing to sell what it derided as low-value offerings, it will cheerfully sell anything and everything because every dollar of revenue is now the same. Yet another opportunity is predatory monetization: charging for things that were previously given away for free (e.g., whereas once upon a time, all economy seats were priced the same, those near bulkheads or the aisle cost more than seats in the middle).

None of these alternatives are revolutionary. All have the advantage of not being “bet the business” pursuits. Which is helpful, because the balance sheet limits the investment the company can make in pursuing any growth opportunities. All of these can be pursued through partnership and small acquisition, as opposed to organic development; this jumpstarts capability, minimizes cash outlay, and promises faster time to return.

Yet none of these are truly growth strategies in that they open new markets or compel buyers to spend where they had not spent before. They are only growth strategies because they assume that customer income statements are growing: growth in wallet share creates exposure to growth in customer income statements. Restated, as aggregate customer topline grows, aggregate customer expenses grow, and a greater capture of customer spend leads to growth. It’s growth by girth, not by invention, innovation or creativity.

The intermediate-term success of this strategy is a return to modest growth and sufficient cash flows to make its interest payments and modest - if only occasional - dividends. These are not steps that will help the company regain its lost edge. Just the opposite: they signal capitulation that it will never regain that edge. Its long-term success is either to be acquired (a possibility because inflation increases revenue and simultaneously reduces the debt burden), or to be in a position to grab more revenue should a competitor stumble or outright fail.

To become what the company once was - a company that made its own growth by obsoleting its own products and services (why keep that 286 PC when you can have a Pentium? Why fly a fleet of 707s when you could fly a fleet of 747s? Why keep using that old phone since it can’t keep a charge anyway?) - requires both a capacity for innovation and a growth market. Yes, I just wrote “the company makes its own growth” because it does so by proxy. Technological advances bring new consumers into the market: personal computer manufacturers expected to sell more PCs once they had more powerful CPUs that could solve more complex problems; airlines expected to fly more passengers once they had more planes and larger planes to drive down costs in the post-deregulation airline industry.

A cash strapped, debt laden business is an unlikely candidate to invent the market-creating technologies of the kind that brought it to prominence in the first place, if for no other reason than a company pursuing revenue grab is focused on yesterday’s growth markets and is therefore poorly attuned to tomorrow’s. That is not to say it is utterly hopeless, but that the path back to growth markets isn’t going to be self-directed. Finding the way back into a growth market requires that the company be quick to recognize, implement and operationalize something new. But that, too, requires balance sheet leeway that, depending how dire the straits it finds itself at the start of the retrenching cycle, will limit its ability to spend on the R&D necessary to innovate. This is the cundrum of retrenchment: while fortifying the balance sheet is unavoidable, the resulting fortress imprisons cash flows and, ultimately, the income statement. Add - well, subtract - labor severed during retrenchment and the disengaged labor that remains, and it is highly unlikly that a fallen icon will return to the vim and vigor of its go-go days.

The retrenching company needs balance sheet and operational restructuring. It will then look for easy money anywhere that it can extend its brand and monetize its offerings. It will hope to buy time for inflation to work its magic on the debt burden and the topline, and to be ready for a competitor to stumble. That is the definition of success.

While a nice sentiment, nothing in this playbook benefits from the business returning to what it used to be. Because success of the grafted-on rescue management isn't "to win", it is "not to lose."

US automakers are struggling with electrification. They won’t have that luxury bringing autonomy to market.

Four years ago today, I blogged about the difficulty automakers faced in transitioning to electric vehicles, specifically that there were consequences to transitioning too soon or too late. Here we are, four years later, and US automakers are in a tight place. Manufacturers invested heavily in the factories, only for sales to stall right when OEMs need them to soar. EV product discounts are eroding margins. Legacy US automaker losses on EVs have been papered over by strong sales of products in the combustion portfolio. They are deferring investments in PPE and new models.

It’s not just the legacy automakers that are finding the electric vehicle business difficult. Lordstown filed. Fisker filed (different legal entity, same outcome). Rivian - losing money making vehicles - needs VW’s cash lifeline as much as VW needs software to sort out their struggles creating an EV platform.

Regulation requires automakers to make more EVs but do not obligate consumers to buy EVs. Range limitations, inadequate charging infrastructure, power loss in cold weather, higher repair costs, higher insurance costs and the occasional fire are turning out to be disincentives that are overwhelming Treasury’s tax credit incentive.

Four years on, the electric future is still the future.

My point then was that making a one-way, all-in bet is a risky strategy. While the future may be a legislated certainty, the path to that future is not. The best way to deal with transitional uncertainty is to “muddle through” with policies that enable adaptability, attentiveness, and awareness. Toyota’s preference for hybrids over pure electric, and Porsche’s and Mercedes-Benz investment in flexline manufacturing, are examples of maintaining optionality by transitioning product and operations.

With the all-in strategy stalling, automakers are pulling back on electric vehicle production and lobbying congress to ease the timing of electrification mandates. If they are successful, it will buy automakers more time but create more market confusion. How committed are regulators to transition? Will consumers be forced to buy EVs? Will suppliers extend production for parts to keep older model combustion vehicles roadworthy for longer?

Transitory states do not pander to human impatience because they create the appearance of extended transition. But transitory states give OEMs and their suppliers, dealers, lenders, insurance companies, consumers and regulators the opportunity to learn and adjust. And in this case, the counterfactual - that bringing EVs to market in large numbers will result in a rapid transition of the fleet - is known to be untrue.

Smart strategy is transitory and adaptive, not all-in. That is just as true today as it was four years ago, as it has been for all of human existence.

* * *

Automobiles are in a multistage transition. Along with electrification, automakers must transition from building human operated to autonomous vehicles.

The theory supporting aggressive investment in electrification by incumbents and new entrants is that the new regulatory regime will create conditions for a financial windfall through share capture during transition. As pointed out above, disjointed public policy has not created those conditions in the US, and an investment frenzy has yielded an abundance of EVs which, in turn, has depressed returns for OEMs. As mentioned previously, any changes (i.e., relaxation) in public policy will only create more uncertainty that further threaten returns.

Autonomy is a much different opportunity. A bet on autonomy is a bet on the belief that autonomy brings entirely new and different use cases into the transportation sector. E.g., airlines stand to lose passenger volume on short haul flights to autonomous vehicles available through transportation-as-a-service. The payoff for autonomy is much, much larger than electrification.

The prize is bigger, the price is bigger. Electrification has gobbled up billions of dollars; autonomy will gobble up even more. The technology is more complex, the liability (for passenger, pedestrian and property) is greater, and the business models that exploit it are as yet unknown and unproven. Not to mention, autonomy will become even more complex once there is critical mass of autonomous vehicles on the road as the fleet can be made to behave collectively, not just individually.

Automakers hope the regulatory clock slows down to give them time to sort out electrification. Meanwhile, the race - a higher stakes race - is on for autonomy. There turned out to be first mover advantage in electrification in the US, as Tesla is still the sales leader by a wide margin. There will be first mover advantage in autonomy if only because being first to offer “free time for all the humans” will capture a lot of unit sales.

But the financial windfall from autonomy won’t come from vehicle sales: it will come from the services built around autonomous transportation. Figuring out those transformative services, everything from design to offer to pricing to availability - will emerge through discovery and thoughtful experimentation, organizational learning, adaptability and attentiveness. They will emerge by muddling through, not grand design.

The race to provide autonomy-based services starts once comprehensive autonomy is in-market. Coming in a distant runner-up in the race for autonomy will be very costly indeed.

The era of ultra low interest rates is over. Tech has painful adjustments to make.

Interest rates have been climbing for two years now. The Wall Street Journal ran an article yesterday with the headline the days of ultra low interest rates are over. Tech will have to adjust. It’s going to be painful.

When capital is expensive, we measure investments against the hurdle rate: the rate of return an investment must satisfy to exceed to be a demonstrably good use of capital. When capital is ridiculously cheap, we no longer measure investment success against the hurdle rate. In practice, cheap capital makes financial returns no more and no less valuable than other forms of gain.

There are ramifications to this. As fiduciary measures lapse, so does investment performance. We go in pursuit of non-financial goals like "customer engagement rate". We get negligent in expenditure: payrolls bloated with tech employees, vendors stuffing contracts with junior staff. We get lax in our standard of excellence as employees are aggressively promoted without requisite experience. We get sloppy in execution: delivery as a function of time is simply not a thing, because the business is going to get whatever software we get done when we get it done.

Capital may not be 22% Jimmy Carter era expensive, but it ain’t cheap right now. Tech has to earn its keep. That means a return to once familiar practices, as well as change that orchestrates purge of tech largesse. Business cases with financial returns first, non-financial returns second. Contraction of labor spend: restructuring to offload the overpromoted, and consolidation of roles or lower compensation for specialization. Transparency of what we will deliver when for what cost, and what the mitigation is should we not. An end to vanity tech investments, because the income statement, much less the balance sheet, can no longer support them.

Some areas of the tech economy will be immune to this for as long as they are thematically relevant. AI and GenAI are TINA (there is no alternative) investments: a lot of firms have no choice but to spend on exploratory investments in AI, because Wall Street rewards imagination and will reward the remotest indication of successful conversion of that imagination that much more. Yet despite revolutionary implications, AI enthusiasm is tempered compared to frothy valuations for tech pursuits of previous generations, a function of investor preference for, as James Mackintosh put it, profits over moonshots.. Similarly, businesses where there is a tech arms race on because innovation offers competitive advantage, such as in-car software, it will be business as usual. But these arms races will end, so it will be tech business as usual until it isn’t. (In fact, in North America, this specific arms race may not materialize for a long, long time as EV demand has plateaued, but that’s another blog for another day.)

Tech has had the luxury of not being economically anchored for a long time now. If interest rates settle around 400 bps as the WSJ speculated yesterday, those days are over. The adjustment to a new reality will be long and painful because there’s a generation of people in tech who have not been exposed to economic constraints.

This is the Agile Manager blog, as it has been since I started it in 2006. Good news, this change doesn’t mean a return to the failed policies of waterfall. Agile had figured out how to cope with these economic conditions. Tech may not remember how to use those Agile tools, but it has them in the toolkit. Somewhere.

That said, I also blog about economics and tech. If the Fed funds rate lands in the 400 bps range, tech is in for still more difficult adjustments. More specifically, the longer tech clings to hopes for a return to ultralow interest rates, the longer the adjustment will last, and the more painful it will be.

The ultralow rate party is over. It’s long past time for tech to sober up.

Relics

I recently came across a box of very old technology tucked away in my basement: PDAs, mobile phones, digital cameras and even a couple of old laptops, all over two decades old. It was an interesting find, if slightly disturbing to think this stuff has moved house a couple of times. Before disposing of something, I try to repurpose it if I can. That's hard to do with electronics once they're orphaned by their manufacturers. Still, electronics recycling wasn't as easy to do twenty years ago, so perhaps just as well that I held onto them until long after it was.

In addition to bringing back fond memories, finding this trove got me thinking about about how rapidly mobile computing evolved. In the box from the basement were a couple of PDAs, one each by HP and Compaq; phones by Motorola, Nokia (including a 9210 Communicator) and Ericcson; and a digital video recorder by Canon. The Compaq brand has all but disappeared; the makers of two of the three phones exited the mobile phone business years ago; the Mini-DV technology of the camcorder was obsolete within a few years of its manufacture.

There were also a couple of laptops in the box, one each made by Compaq and Sony. The interesting thing about the laptops is how little the form factor has changed. My first laptop was a Zenith SuperSport 286. The basic design of the laptop computer hasn't changed much since the late 1980s (although mercifully they weigh less than 17 lbs). The Compaq and Sony laptops in that box from the basement are not physically different from the laptops of today: the Sony had a square screen and lots of different ports, where a modern laptop has a rectangular screen and a few USB ports.

The laptop, of course, replaced the luggable computer of the 1970s and early 1980s made by the likes of Osborne and Kaypro and Compaq. The luggable was a statement for the era: what compels a person to haul around disk drives, CPU, keyboard and a small CRT? Maybe it was the free upper-body workout. The laptop was a quantum improvement in mobile computing.

But once that quantum improvement happened, the laptop became decidedly less exciting. As the rate of change of capabilities in the laptop slowed, getting a new laptop became less of an event and more of a pain in the ass. Not to mention that, just like the PDA and phone manufacturers mentioned above, the pioneers and early innovators didn’t survive long enough to reap the full benefits of the space maturing.

And the same phenomenon happened in the PDA/Phone/camera space. The quantum leap was when these converged with the original iPhone. Since then, a new phone has become less and less of an event. Yes, just like laptops, they get incrementally better. Fortunately, migration via cloud makes upgrading less of a pain in the ass.

The transition from exciting to ordinary correlates to the utility value of technology in our lives: in personal productivity, entertainment, and increasingly as the primary (if not only) channel for doing things. There are, of course, several transformative technologies in their nascent stages. Somehow, I don’t think any are spawning the Zenith Data Systems and Compaqs making a future relic that somebody someday will be slightly amused to find in a box in their basement.

Shortage

Silicon chips are in short supply, ports are congested, and as a result new cars are expensive. The shortage of new cars has more people buying used, and as a result, used cars are fetching ridiculously high prices as well. The same phenomenon of supply shortages and logistics bottlenecks have been playing out across lots of basics, manufacturing and agricultural industries for months now.

At the same time, we have M2 money supply like we’ve never seen. All that cash is pursuing few investment opportunities, which bids them up. Excess liquidity seeking returns has inflated assets from designer watches to corporate equity.

Supply shortages twined with excess capital have created inflation like we’ve not seen in nearly 40 years.

Included among the supply shortages is labor. The headline numbers in the labor market have been the number of people leaving the workforce and the labor participation rate: fewer people of eligible age are working than before the pandemic, and many have simply checked out of the labor market forever, electing to live off savings rather than income. This means those who are working can command higher wages. In the absence of productivity gains, higher wages contribute to the inflationary cycle, because producers have to pass the costs onto consumers. Inflationary cycles can be difficult to stop once they start.

But labor market tightness can do something else: it can be the genesis of innovation. When a business cannot source the labor it needs to operate, it innovates in operations to reduce labor intensity. By way of example, businesses contracted their labor forces (including the ranks of their core knowledge workers) in the wake of the 2008 financial crisis. While this reduced corporate labor spend, it put remaining workers under strain. Soon after the reductions-in-force, companies invested in technology to lock in productivity gains of that reduced force. Capitalizing those tech assets reduced their impact on the income statement while those investments were being made. Once recovery began and revenues rose, that tech kept costs contained, resulting in better cash flow from operations after the financial crisis than before.

We are potentially in an inverse of the same labor dynamics. Whereas in 2008 the corporate innovation cycle was driven by corporate downsizing of the labor force, today it is driven by the labor market downsizing itself. And just as in 2008, when it was a secular problem (finance had an abundance of labor, while tech did not), it is secular again today.

Among the labor markets suffering a supply shortage is K-12 education. Education has become a less attractive occupation since the pandemic. A highly educated cohort disgruntled with work is an attractive recruiting pool for all kinds of employers.

The exodus of people from the teaching profession has created a shortage of teachers. The K-12 operating model is based on physical classroom attendance of teacher and student at increasingly high leverage ratios - 20, 30, 35 students to one teacher. This model becomes vulnerable with a scarcity of teachers. Classroom dynamics - not to mention physical facilities - don’t scale beyond 35 or 40 K-12 students in a single classroom. If there are fewer people willing to teach in the traditional paradigm, then the teaching profession will be under pressure to change its paradigm in one way or another.

I’ve written before that technology is generally not a disruptive agent. Technology that is present when socioeconomic change is happening is simply in the right place at the right time. Where there are acute labor shortages today - public safety, education, restaurant dining - the socioeconomic change is certainly afoot. What isn’t obvious is whether the right tech is present to capitalize on it.

Distribution

In the past year, I’ve written several times about changes taking place and likely to be long lasting in the wake of the COVID-19 pandemic. One area I’ve touched on, but haven’t delved into much, is distribution.

Producers of all kinds have had to create new ways of connecting with their customers. Restaurants lost and had to contend with severely curtailed access to their primary distribution channel, the dining room. Countless manufacturers lost and had to contend with complete loss of their primary distribution channel, small retail and department stores. Digital products like movies lost and had to contend with severely curtailed access to their primary distribution channel, theatres.

Producers have responded by finding, creating and reprioritizing alternative means of distribution. All restaurants (well, those still in business) increased their distribution through take-away meals. eCommerce retail sales from groceries to gadgets skyrocketed last year. In December, Warner Communications announced they will distribute movies simultaneously to theaters and on their HBO streaming service in 2021.

And the opportunities to innovate in distribution are far from over. To wit: in December, the FAA relaxed guidelines for drone delivery, expanding the potential for commercial drone use in the US (and of course, drone delivery has expanded in the rest of the world).

Distribution will be among the primary ways the chattering classes characterize the post-pandemic world. To what extent do people return to old - largely physical - forms of distribution? Are the new forms of distribution long-lasting, or are they just phenomenon of their times, like Sherry served at Christmastime in Britain?

Change is like a regenerative fire. Fire needs three things: fuel, spark, and accelerant.

The fuel is policies in response to COVID-19 and, in turn, the individual and business reaction to them. Companies need to sell and people want to buy. The longer those policies remain in place, the more significant these new forms of distribution become to producers. Those policies also depress the prices for assets tightly coupled to past forms of distribution, things like commercial airplanes and shopping malls.

The spark is the realization that businesses don’t need to operate in the ways that they have for decades, e.g., expenses don't need to be so high, a company doesn’t need as much square footage or needs to be able to use its physical space differently. The evidence exiting 2020 supports this: even though revenues declined in 2020, earnings per share for the S&P 500 for the year look great.

The accelerant is cheap capital: Fed policy will maintain cheap capital for the foreseeable future. This creates liquidity that has to go somewhere, and will find every nook and cranny.

Distribution has changed. Policy is entrenching those changes. The businesses that didn’t collapse survived in large part because they found new distribution channels. Innovation in distribution is accelerating. Cheap capital will finance more innovation in distribution. It’s a reinforcing cycle. And it’s just beginning.

All In

Immediately after World War II, Coca-Cola had 60% of the soft drinks market in the United States. By the early 1980s, it had about 25%. Not only had Coca-Cola been outmanouvered in product marketing (primarily by Pepsi), consumer concerns over sugar and calories drove consumers to diet soft drinks and to refreshment options outside of the soft drink category. The fear in the executive ranks was evidently so great that Coca-Cola felt it necessary to change its formula. Coca-Cola did the market research and found a formula that fizzy-drink consumers preferred over both old Coke and Pepsi. Coca-Cola launched the new product as an in-place replacement for their flagship product in 1985.

New Coke flopped.

Within weeks, consumer blowback was comprehensive and fierce (no small achievement when media was still analogue). Turns out there is such a thing as bad publicity if it causes people to stop buying your product. Sales stalled. Before three months were out, the old Coca-Cola formula was back on the shelves.

Why did Coca-Cola bet the franchise? The data pointed to an impending crisis of being an American institution that would soon be playing second fiddle to a perceived upstart (ironically an upstart founded in the 19th century). Modern marketing was coming into its own, and Pepsi sought to create a stigma among young people who would choose Coke by using a slogan and imagery depicting their cohort as "the Pepsi generation." Coca-Cola engineered a replacement product that consumers rated superior to both the classic Coke product and Pepsi. The data didn't just indicate New Coke was a better Coke than Coke, the data indicated New Coke was a better Pepsi than Pepsi. New Coke appeared to be The Best Soft Drink Ever.

Still, it flopped.

There are plenty of analyses laying blame for what happened. One school of thought is that the testing parameters were flawed: the sweeter taste of New Coke didn't pair as well with food as classic Coke, nor was a full can of the sweeter product as satisfying as one sip. Another is sociological: people had a greater emotional attachment to the product that ran deeper than anybody realized. Most of it is probably right, or at least contains elements of truth. There's no need to rehash any of that here.

New Coke isn't the only New thing that flopped in spectacular fashion. IBM had launched the trademarked Personal Computer in 1981 using an open architecture of widely available components from 3rd party sources such as Intel and the fledgling Disk Operating System from an unknown firm in Seattle called Microsoft. Through sheer brand strength, IBM established dominance almost immediately in the then-fragmented market for microcomputers. The open hardware architecture and open-ended software licensing opened the door for inexpensive IBM PC "clones” that created less expensive, equally (and sometimes more) advanced, and equally (if not superior) quality versions of the same product. IBM created the standard but others executed it just as well and evolved it more aggressively. In 1987, IBM introduced a new product, the Personal System/2. It used a proprietary hardware architecture incompatible with its predecessor PC products, and a new operating system (OS/2) that was only partially compatible with DOS, a product strategy not too dissimilar to what IBM did in the 1960s with the System/360 mainframe. IBM rolled the dice that it could achieve not just market primacy, but market dominance. They engineered a superior product. However, OS/2 simply never caught on. And, while the hardware proved initially popular with corporate buyers, the competitive backlash was fierce. In a few short years, IBM lost its status as the industry leader in personal computers, had hundreds of millions of dollars of unsold PS/2 inventory, laid off thousands of employees, and was forced to compete in the personal computer market on the standards now set by competitors.

These are all-in bets taken and lost, two examples of big bets that resulted in big routs. There are also the all-in bets not taken and lost. Kodak invented digital camera technology but was slow to commercialize it. The threat of lost cash flows from their captive film distribution and processing operations in major drug store chains (pursuit of digital photography by a film company meant loss of lucrative revenue to film distributors and processors) was sufficient to cow Kodak executives into not betting the business. Polaroid was similarly an leader in digital cameras, but failed to capitalize on their early lead. Again, there have been plenty of hand-wringing analyses as to why. Polaroid had a bias for chemistry over physics. Both firms were beholden to cash flows tied to film sales to distributors with a lot of power. While each firm recognized the future was digital, neither could fathom how rapidly consumers would abandon printed pictures for digital.

We see similar bet-the-business strategies today. In the early 2000s, Navistar bet on a diesel engine emission technology - EGR, or exhaust-gas-recirculation - that was contrary to what the rest of the industry was adopting - SGR, or selective catalytic reduction. It didn't pan out, resulting in market share erosion that was both substantial and rapid, while also resulting in payouts of hundreds of millions of dollars in warranty fees. Today, GM is betting its future on electronic vehicles: the WSJ recently reported that quite a few internal-combustion based products were cut from the R&D budget, while no EV products were.

All in.

The question isn't "was it worth betting the business." The question is, "how do you know when you need to bet the business."

There are no easy answers.

First, while it is easy to understand what happened after the fact, it is difficult to know what alternative would have succeeded. It isn't clear that either Kodak or Polaroid had the balance sheet strength to withstand a massive erosion in cash flows while flopping about trying to find a new digital revenue model. The digital photography hardware market was fiercely competitive and services weren't much of a thing initially. Remember when client/server software companies like Adobe and SAP transitioned to cloud? Revenues tanked and it took a few years for subscription volume to level up. It was, arguably, easier for digital incumbents to make a digital transition in the early 2010s than it was for an analogue incumbent to make the same move in the late 1990s. Both firms would have been forced to sacrifice cash flows from film (and Kodak in film processing) in pursuit of an uncertain future. As the 1990s business strategy sage M. Tyson observed, "Everyone has a plan until they're punched in the face."

To succeed in the photography space, you would have needed to anticipate that the future of photography was as an adjunct to a mobile computing device, twined with as-of-yet unimagined social media services. Nobody had that foresight. Hypothetically, Kodak or Polaroid execs could (and perhaps even did) anticipate sweeping changes in a digital future, but not one that anticipated the meteoric rise in bandwidth, edge computing capabilities, AI and related technologies. A "digital first" strategy in 1997 would have been short-term right, only to have been proven intermediate- and long-term wrong without a pivot to services such as image management and a pivot a few short years after that to AI. It's difficult to believe that a chemistry company could have successfully muddled through a physics, mathematics and software problem space. It's even more difficult to imagine the CEO of that company could successfully mollify investors again and again and again when asking for more capital because the firm is abandoning the market it just created because it's doomed and now needs to go after the next - and doing that three times over the span of a decade. In theory, they could have found a CEO who was equal parts Marie Curie, Erwin Schrödinger, Issac Newton, Thomas Watson, Jr., Kenneth Chenault, and Ralph Harrison. In practice, that's a real easy short position to take.

Second, it's all well and good when the threat is staring you in the face or when you have the wisdom of hindsight, but it's difficult to assess a threat let alone know what the threats and consequences really are, and are not. A few years ago, a company I was working with started to experience revenue erosion at the boundaries of their business, with small start-up firms snatching away business with faster performance and lower costs. It was a decades-old resource-intensive data processing function, supplemented with labor-intensive administration and even more labor-intensive exception handling. Despite becoming error-prone and slow, they had a dominant market position that was, to a certain extent, protected by exclusive client contracts. While both the software architecture and speed prevented them from entering adjacent markets with their core product, the business was a cash cow and financed both dividends and periodic M&A. They suffered from an operational bias that impaired their ability to imagine the business any differently that it was today, a lack of ambition to organically pursue adjacent markets, and a lack of belief that they faced an existential threat from competitors they saw as little more than garage-band operators. Yet both the opportunities and the threats looked very plausible to one C-level exec, to a point that he believed failure to act quickly would mean significant and rapid revenue erosion, perhaps resulting in there not being a business at all in a few years. Unfortunately, all unproveable, and by the time it would be known whether he was prophet or crazy street preacher, it would be too late to do anything about it: remaining (depleted) cash flows would be pledged to debt service, inhibiting any re-invention of the business.

Third, even the things you think you can take for granted that portend future change aren't necessarily bankable on your timeline. Some governments have already created legislation that all new cars sold must be electric (or perhaps more accurately, not powered by petroleum) by a certain date. A lot of things have to be true for that to be viable. What if electricity generation capacity doesn't keep up, or sufficient lithium isn't mined to make enough batteries? Or what if hydrocarbon prices remain depressed and emissions controls improve for internal combustion engines? Or what if foreign manufacturers make more desirable and more affordable electronic vehicles than domestic ones can? If they were to happen, it would increase the pressure that legislatures would feel to postpone the date for full electrification. For a business, going all-in too late will result in market banishment, but too early could result in competitive disadvantage (especially if a company creates the "New Coke" of automobiles... or worse still, The Homer). These threats create uncertainty in allocating R&D spend, risk of sales cannibalization of new products by old, and sustained costs for carrying both future and legacy lines for an extended period of time.

Is it possible to be balance sheet flexible, brand adaptable, and operationally lean and agile, so that no bet need be a bet of the business itself, but near-infinite optionality? A leader can be ready for as many possibilities as that person can imagine. Unfortunately, that readiness goes only as far as creditors and investors will extend the confidence, customers will give credibility to stretch the brand, and employees and suppliers can adapt (and re-adapt). To the stars and beyond, but if we're honest with ourselves we'll be lucky if we reach the Troposphere.

Luck plays a bigger role than anybody wants to acknowledge. The bigger the bet, the more likely the outcome will be a function of being lucky than being smart. The curious thing about New Coke is that it might have been the Hail Mary pass that arrested the decline of Coca-Cola. Taking away the old product - that is, completely denying anybody access to it - created a sense of catastrophic loss among consumers. Coca-Cola sales rebounded after its reintroduction. In the end, it proved clever to hold the flagship product hostage. Analyst and media reaction was cynical at the time, suggesting it was all just a ploy. Then-CEO Roberto Goizueta responded aptly, saying "we're not that smart, and we're not that dumb."

And that right there is applied business strategy, summed up in 9 words.

The Innovator's Cunundrum

Even as the pandemic hits sales, [automakers] need to pour vast sums into developing electrical vehicles - with absolutely no guarantee of success.

-- FT Lex, July 29, 2020

Socioeconomic systems in transition are impossible to navigate. At the same time that the old order is in collapse, the keys to the new order are very difficult to forge. Plenty of people bet and lost - in many cases quite tragically - during the 1918 Russian revolution, the great depression that began in 1929, the 2008 financial crisis, as well as in many other transitions in between. It is easy to realize that things are changing, but what they are changing from is easier to identify than what it is they are changing into.

Let's consider a specific case. For about a decade now, governments round the world have mandated that automobiles change from hydrocarbon-powered internal combustion engines to battery-powered electric motors. Yet the availability of electronic vehicles for sale has far outstripped the demand for the vehicles themselves. Plenty of automakers are building EVs at volume. Unfortunately, that volume is staying on manufacturer and dealer balance sheets, because legacy automakers are designing and building EVs that few want to buy.

The future is right in front of every legacy automaker, yet the legacy automakers don't really know how to crack the nut. Build EVs, check. And build EVs they have. Unfortunately, it's hard to make money on a product when the unit volume is measured in 4 or low-5 digit range. In 2019, Chevrolet sold 4,915 Volts and 16,313 Bolts in the United States, for a combined sales volume of 21,228 EVs. That same year, Chevy sold:

• 575,600 Silverado pickup trucks in the US.
• more Blazers in the fourth quarter of 2019 alone - 23,008 - than they sold EVs for all of 2019.
• more than twice as many Cruize vehicles in the model's final and discontinued year of production - 47,975 - than it did their maturing new product line of EVs that same year. The Cruze sold more in decline than Chevy sold EVs at their peak: 43,669, in 2017.
• Tesla sold more Model 3s in two years (2018 and 2019) than Chevy sold in a decade's worth of Volts, Bolts and Sparks.

If at first you don't succeed, try, try again. That sounds easy enough for the legacy automakers to do: keep funneling cash flows from the lucrative legacy business of internal combustion trucks to finance more R&D of EV products (including, of course, an electronic pickup truck) until they get it right. Regulators have spelled out the future, and neither the debt burden nor equity holder's appetite for dividends preclude a healthy R&D spend. Tesla figured it out from scratch. How hard can it be?

Tesla the automotive company (ignoring the solar panel company) has (in that context) one mission - to make money manufacturing, marketing, selling and servicing a line of electronic transportation products. Sole mission and sole purpose make a clear investing proposition: this is an all-in wager. By way of comparison, legacy automakers have multiple missions: satiate bondholders and shareholders of a multi-line mass-transportation company. All-in wagers are not so appealing to investors in legacy firms.

This is especially true since it isn't clear just how quickly the clock is ticking on this transformation. EVs are the future, but when is that future? Proven hydrocarbon reserves are vast, demand for refined hydrocarbons (lubricants, jet fuel, automotive fuel) is down across all sectors and will remain depressed for years. Excess and untapped supply portend cheap refined petroleum product prices for a decade. Regulation could change that, but regulation is just as subordinate to economic needs as it is to environmental ones. The electronic vehicle manufacturer doesn't employ as many people (a.k.a. "voters") as the internal combustion engine vehicle business does. Cheap energy - be it electric or hydrocarbon - drives household productivity and therefore household leisure. Lots of entities stand to lose if the migration to EVs is too quick, EVs are the future, but that future might very well now be anywhere from one year to ten years out.

The standard playbook to navigate this dynamic is to do continuous market testing. The modern tech playbook would have that be done in the form of user need surveys, MVPs that gauge user interaction via instrumentation, and user satisfaction surveys, all in lock step. But if the market is in a volatile state, historical data is useless and real-time data only has value if the right filters are applied. Good luck with that.

During times of transition, there is no right policy, but there is wrong policy. The macro strategy risks are almost too obvious to point out. Bet too heavily on the future and you will lose. Cling too tightly to the past and you will lose. That's great policy, but utterly useless, unless you're JC Penny a decade ago standing at the city on the edge of forever with the ability to step back in time to alter the present. The micro strategy is where the transition is won or lost. The successful strategy will be one of muddling through.

In 1959, Dr. Charles Lundblom published a paper entitled "The Science of Muddling Through". The point was that policy change was most effective when incremental and not wholesale: evolutionary, not revolutionary. Dr. Lundblom was mocked by the intelligentsia of the day, who subscribed to the grand strategy theory that was fashionable at the time: that all outcomes could be forecast as in a chess match, that all plays could be anticipated and their outcome maximized toward a grand plan. The theory sounded great, but it assumed a static future path, muted all feedback loops, and was willingly ignorant of statistically improbable but highly significant events. Experience teaches us that the world is not so much chess as it is Calvinball. The grand strategy proved intellectually bankrupt as exhibited through its applications ranging from companies such as ICI Chemicals to the United States military prosecution of the Vietnam conflict. The prior resulted in monumental destruction of employee and shareholder value; the latter resulted in societal implosion.

Grand strategies will be all the rage in response to great challenge because they appear to have all of the answers. History teaches us that most, and likely all, will fail. In times of great transitions, Dr. Lundblom was right. Grand schemes and grand hypotheses will not win the day. Micro-level attentiveness, situational awareness, and adaptability will.

Lost Productivity or Found Hyperefficiency?

Labor productivity creates economic prosperity. Increasingly productive labor results in lower cost products (greater output from the same number of employees == lower labor input costs), higher salaries (productive workers are valuable workers), greater purchasing power (labor productivity allows households to keep monetary inflation in check), increasing sophistication (skill maturity to take on greater challenges), and higher returns on capital. The more productive a nation's workforce, the higher the average standard of living of its population.

In recent years, economists have drawn attention to low productivity growth in western economies as a key factor restraining economic growth and perpetuating low inflation and low interest rates. In particular, they cite the lack of breakthrough technologies - e.g, the emergence of the personal computer in the 1980s - to spur labor productivity and with it, more rapid economic growth. By traditional economic measures, things do not appear to be getting much better.

There is an alternative perspective that is far more optimistic: digital companies drive down costs through hyper-efficiency (speed, automation and machine scale) and price transparency. Algorithms are cheaper than humans and can be networked to perform complex collections of tasks at a speed, and subsequently a scale, that humans cannot achieve. Twined with the radical reduction of information asymmetry (particularly with regard to product price data), it stands to reason that there has been significant productivity growth in western economies: supply chains have never been so optimized, retail and wholesale transactions so price-fair and friction-free. This stands to reason: it is considerably less time- and energy-intensive to ask an Echo to order more Charmin toilet paper than it is to drive to a grocery store or pharmacy, walk in, price compare to justify those few extra pennies for softness, queue, pay, and drive home. The argument for this invisible efficiency is that economic models have simply failed to change in ways that reflect this phenomenon. The productivity is there, and will intensify with technologies such as AI and ML; the instrumentation simply doesn't exist to measure it.

In this definition, productivity through technology is a deflationary force that makes products more affordable. Even if real wages remain stagnant, the standard of living increases because people can afford more goods and services as they cost less today than they did yesterday. In theory, the increasing standard of living will occur regardless the cost of capital: because retail prices are going down, interest rates could move higher with no ill effects to the economy, juicing returns on capital. The bigger the tech economy, the better off everybody is.

There is truth to this. Consider healthcare: although medical costs are much higher today in nominal terms than they were in 1970, they are much lower in real terms when adjusted both for monetary inflation and medical-technological innovation. If medicine were still practiced today as it was 50 years ago, the cost of delivery would be lower in real terms, but the standard of care would be much, much lower than what it is today. Would you want to receive cardiac treatment at a 1970 standard, pulmonology treatment at a 1980 standard, or HIV treatment at a 1990 standard? Or would you rather be treated for all of these to a standard of care available in 2020? Technology is clearly a deflationary force that increases individual prosperity.

Still, there are three factors that should temper enthusiasm for an unmeasurable tech-led labor productivity bonanza.

The first has to do with the real price of and the real payers for tech-generated benefits. Ride sharing services have added driver/fleet capacity and accelerated speed-of-access for local transportation service. However, the individual consumer isn't fully picking up the tab; the ride is heavily subsidized by private capital. That makes the price affordable to the user. The question is, how sustainable is the price without the private-capital subsidy?

Economic subsidies are a common practice, typically sponsored by governments to protect or advance economic development. Sometimes a subsidy is direct, as is often the case with agricultural commodity price supports: if depressed crop prices drive farmers out of business, a nation loses its ability to feed itself, so in years of commodity gluts governments will offer direct assistance to make farmers whole. And, sometimes a subsidy is indirect. The United States was dependent on oil from foreign countries for much of the past 60 years. The price of petroleum products in the US did not reflect the cost of US military bases as well as having the Fifth Fleet patrol the Persian Gulf. The federal government prioritized energy security to guarantee supply and reduce the risk to energy prices of supply shocks. The immediate cost of that security and stabilization was borne by the US taxpayer; the policy was founded on the expectation that the federal government would be made whole over the long term through increasing tax receipts from economic growth that resulted from cheap energy.

There are subsidies that are sustainable and subsidies that are not sustainable. In theory the US projecting military power to secure Middle Eastern oil was a sustainable economic subsidy: containing energy prices while your nation gives birth to the likes of Microsoft and Apple and many other companies seems a good economic bargain (exclusive of carbon emissions, which did not historically factor into economic policy). By comparison, productivity in the Soviet Union grew in lock-step with direct government investment in industry (primarily steel production) through the 1950s and 60s, Trouble was, when the Soviet government pulled back investment, labor productivity growth flatlined. Labor productivity was entirely dependent on outside (e.g., government) financial injection. The lack of organic productivity growth translated into stagnation of economic prosperity of the masses. A standard of living that was competitive with the United States and Western Europe in the 1950s was hopelessly trailing by the 1980s. Turns out Maggie was right: eventually you really do run out of other people's money.

The investment case for the ride sharing companies is that there will eventually be one dominant player with monopolistic pricing power. A market for on-demand transportation is now established, so a single surviving ridesharing firm will reap the winner-take-all benefit of that market, giving it scale. Being the only game in town, the surviving firm will have pricing power. In theory, the surviving firm should have access to a larger labor pool spanning Subaru drivers to software developers, thus depressing wages, and thus the cost of service. Lower input costs twined with scale should mean a lower price increase is needed for the firm to become profitable.

But there are a lot of variables in play here. Ridesharing firms are carrying billions of dollars of losses they accreted over many years that they need to make up for their investors to be made whole; that will create pressure to raise prices. There are other industries competing for the labor of these firms (especially those software developers), so input costs will not necessarily decline. Because drivers work for multiple ridesharing services, their utilization is already high, meaning economies of scale that will temper price increases passed on to consumers.

If or when a monopolistic competitor triumphs, prices are going to rise and individual consumer's "productivity" will be impaired by the withdrawal of the price subsidy. Consolidation and scale will not perpetuate the subsidy, so the price of service is going to rise. The subsidy is only sustained if a new entrant with deep-pocketed backers emerges to challenge what will by then be a "legacy" incumbent; in essence, the cycle of subsidy regenerates itself. Don't rule it out: it isn't out of the question as long as capital is cheap. While it's reasonable to assume the industry will run out of greater fools, there has always been a high degree of correlation between "minutes" and "suckers born". The WSJ reported today that Softbank is pumping cash into multiple meal delivery services operating in the same markets and therefore competing directly with one another, each firm engaged in an arms of subsidies with one another to sign restaurants, delivery labor and customers. It is difficult to fathom the logic of this.

The second factor is the implicit assumption that the tech cycle has triumphed over the credit cycle. There is a popular theory that technological innovation has become more important than capital in setting prevailing economic conditions. The evidence of this is the shift in economic activity steered by emerging technologies in areas such as ecommerce and fintech. A technology-centric business benefits from lower costs for facilities, lower inventory carry costs, and lower network (transaction) costs, and therefore has an intractable competitive advantage over incumbents. As I've written previously, unfortunately the evidence doesn't entirely support this yet. Plus, deep-pocketed incumbents can raise capital to acquire, compromise or corrupt the business models of would-be disruptors, not to mention that would-be disruptors are finding themselves engaged in technological arms races not with incumbents, but other would-be disruptors. This distorts the playing field, making it much more about capital than tech.

It's curious that contemporary strategy among big tech firms is to burrow into the existing economy as un-metered, un-regulated, subscription-based utilities, as opposed to betting on ever-accelerating revenue from their intrinsic value-generative nature. Consider entertainment streaming services: by selling subscriptions, they are willfully exchanging the potential for sky-high equity-like returns from the value of the content they produce (which is how movie studios used to operate) for more modest debt-like returns from the utility that subscribers will pay for access to a library where they can find something they can tolerate just enough to pass the time (which is how cable companies operate). While streaming services are engaged in a long-running competition for content and tech, they have concluded they are not going to win by out-tech-ing or out-content-ing one another. Streaming entertainment is not a value proposition, it is a utility proposition. A utility business model is one that is explicitly (a) not leading with tech innovation and (b) seeking immunity from the credit cycle.

What this tells us is that the tech cycle is not the dominating economic force. As it stands today, more people suffer economically when the credit cycle turns than when the tech cycle turns (e.g., a dearth of innovative new technologies). A turn in the credit cycle contracts business buying which creates layoffs. A turn in the tech cycle makes means there will not be a still more convenient way to get a ride from The Loop to O'Hare or food delivered from a Hell's Kitchen restaurant to an apartment in Midtown. While it may happen some day, we are still not yet at a point where the tech cycle is triumphant.

The third factor goes to the question of labor capacity versus labor productivity. Labor productivity and labor-saving efficiency are really measures on the same axis: less time, effort and energy necessary to complete a task and ultimately achieve an outcome. A different but equally important dimension is labor capacity: the more people engaged in gainful employment, the greater the level of household income, the more individual households reap economic benefit.

Labor participation in the United States took a direct hit in September, 2008, and hasn't recovered. After hovering above 66% for over 18 years, it went into sharp decline, bottoming at 62.5% in 2015 and recovering only to 63.2% today. To put it in absolute terms, there are 20 million more jobs in the US today than there were in 1999 (peak labor participation), but the US population has grown by 48 million more citizens. Job growth hasn't kept pace with population growth. This suggests that the economic benefits of productivity gains (through organic labor productivity or technology) are concentrated in fewer hands, implying that the economic benefits of technology gains are asymmetrically distributed.

Yes, labor capacity is a measure, not a driver. From 1950 to 1967, the labor participation rate hovered in the 59% range. And even with a growing population, technological advances can create price deflation that raises the standard of living for everyone: many and perhaps most of those 48 million additional US citizens since 1999 have smartphones, which none of the 279 million Americans had in 1999. Still, there is asymmetric benefit to those technological advances: those not working are not enjoying the totality of economic benefits of increased productivity described in the opening paragraph. As much as proponents advocate that technology improves labor productivity, that same tech is also increasing in the Gini coefficient.

Does technology improve productivity? Undoubtedly. But before hailing any technology as an economic windfall on par with traditional measures of labor productivity, best to scrutinize how it organically it achieves it, how resilient it is, and how widely its benefits are spread around the work force. Technology may eventually change traditional economics, but there is one thing even the best technology cannot overcome: there is no such thing as a free lunch.

But Is It Really a Tech Firm?

There are lots of executives who would have you believe that the business they run is really a tech business. With tech firm valuations still at sky-high levels, it's easy to understand why. Tech commands a premium valuation because (a) the potential for non-linear growth relative to investment; (b) low barriers to entry into adjacent markets amplifies that growth; (c) scale of offerings changes the commercial model from transactional to flat-fee subscription, making a tech firm an unregulated utility; (d) payments take place behind-the-scenes of the tech consumption, creating sustainable recurring revenue; and (e) tech industries tend to be winner-take-all.

Translated into investor-bait, here is what this means. Selling access to movies is all well and good. Selling all forms of entertainment - on different media, on different frequencies, for different prices - is even more interesting. Selling access to it as a service on a fixed price makes it an uninterrupted cash flow. Getting a significant number of people on the planet to pay a fixed subscription fee once a month every month is epic scale.

This kind of reach isn't a new or even a recent phenomenon. You may recall a time when McDonald's restaurants used to show the number of burgers they sold on the golden arches signs, in the tens and later in the hundreds of millions. Then it simply became "billions and billions served." You may not recall that twenty years ago, McDonald's (briefly) targeted their sales in terms of the percentage of total meals consumed globally on a daily basis. Around the same time, the largest of the large banks was targeting a total number of accounts across all product categories relative to the entire population of the planet. 'Twas ever thus: Standard Oil achieved monopoly status over American oil in the late 19th century; Rome achieved hegemony over Europe.

Tech didn't invent the economic harvesting of humans at scale, it's simply the current means of achieving it. In Roman times, it was achieved through territorial conquest (tax revenue through subservience of subjects). In the industrial age, it was achieved by selling productivity, e.g., labor-saving machines and the energy to run them (revenue from products to improve productivity of business and household activities). Today, it is achieved by selling entertainment (revenue from selling services that fill the passive time of individuals). At a time in history when conquest is out of favor and productivity gains have slowed, monetizing everybody's abundant downtime from all those labor-saving products of the industrial age is the next frontier.

Not exclusively, of course. There are still plenty of opportunities for productivity gains. Electric vehicles require fewer components, which means less labor is required to manufacture them. Tax compliance is mostly rules, and rules can be implemented as algorithms and therefore replace large number of auditors. And when cars can drive themselves, individual, on-demand transportation isn't limited by the number of drivers but the accessibility of vehicles. There are still plenty of productivity gains to be realized, and their potential still grabs headlines, but productivity is the old frontier; entertainment is the new.

Regardless the source - political domination, economic productivity, or entertainment - the potential for scale drives equity value. Potential is more lucrative to investors than reality. Bond investors are told the company is growing at a predicable rate and spending is under control, which secures the credit rating and coupon; equity investors are told that it isn't the sky that's the limit, but our ability to fathom every quantum reality of where the business could go, and that tech is the enabling factor. Hence there are plenty of CEOs and CIOs alleging they are "tech companies that happen to operate in the [insert-industry-name-here] industry."

You've probably heard this statement hundreds of times from hundreds of executives, to a point that it doesn't merit even as much as an eye-roll any more. I've always thought it would be helpful to have a consistent and objective means of assessing whether they really fit the bill of a tech firm or not.

Andy Kessler wrote an interesting op-ed in the WSJ a few weeks ago cataloging five characteristics that define a tech firm. They are: growth; R&D intensity; margins; productivity; and tech spending intensity. This is a very useful heuristic.

Growth: "Even though prices go down, units go up faster so you get rapid and sustainable growth." Among other things, that means race to the bottom pricing isn't destructive if volume rises ahead of it. A good litmus test of growth: "Beware of fake growth like market-share growth: If you sell seat cushions for a 50,000-seat stadium, you can double sales every year but eventually you’ll run out of seats. Instead look for giant markets." If the company does not have truly exponential growth potential through tech, they are not a tech firm.

R&D: This is a positive and negative indicator. On the plus side, tech firms have to invest for invention and innovation. On the minus side, "Companies often boost earnings by starving research, a serious red flag." A company not investing in original research in tech is not a tech company. But it isn't the creation of tech that matters as much as how effectively it is mainstreamed. Mr. Kessler wrote another op-ed this past week in which he points out the rapid rise of such things as voice command, live streaming and medical monitoring have gone from new to commonplace and, in some cases, depended upon. The ability to create technology simply yields another Xerox PARC or Kodak digital camera; the ability to operationalize R&D is entirely another.

Margins: "the ideal tech product doesn’t cost anything to distribute—roughly zero marginal cost, like software." This is true for bits, silica and advertisements. Whatever a tech firm is selling should have near-zero marginal cost for each additional sale. By this definition, consulting firms are not tech firms: because they rent bodies, they have direct costs proportional to sales.

Productivity: Marginal improvements in productivity have been with us since the dawn of time; replacing entire swaths of labor activity is transformational. Levers and pulleys allowed humans to power simple devices to create incremental labor saving, while the flywheel engine completely replaced the need for people to perform specific tasks. Organizing people to drive their cars to transport others is not a productivity boost; cars that navigate themselves to people in need of mobility without the presence of a driver is a productivity boost.

Tech spend intensity, or the extent to which a company must continuously upgrade core capacity. A company on the technology treadmill has no choice but to spend capital on enhancement and expansion. Note that this does not apply to self-inflicted woes. I've worked with entirely too many firms that are hostage to tech spend commitments due to poor tech lifestyle decisions: vendor spend is directly proportional to cleaning up mistakes made by those very same vendors. Committed spend for purposes of hygiene is not the same indicator of tech intensity as disciplined spend for purposes of improvement.

This simple heuristic makes it easy to score (Mr. Kessler suggests awarding one point for each). By his reasoning, a score of 3 or above qualifies a company as a tech business, while a 1 qualifies them as a tech user, and quickly applying it to firms with which I'm familiar it winnows out the wanna-bes. Next time somebody is touting their tech credentials, apply this simple rating to see how well they stack up. It will be more constructive than an eye-roll.

The Tortoise Strategy

Three years ago, I wrote that the unstoppable forces of Fintech were running into the immovable force of financial orthodoxy. Specifically, the technology cycle wasn't enough to overcome the credit cycle for peer-to-peer lending firms, which were resorting to selling their loan portfolios to traditional lenders, becoming buyers of last resort themselves when no buyers emerged, and offering deposit insurance for lenders.

Earlier this year, I wrote that incumbents had advantages - specifically, access to greater amounts of patient capital - which they can parlay in a multitude of ways to co-opt a would-be disruptors business: make the disruptor financially dependent by becoming one of their biggest customers buy buying their products (e.g., loan books originated by peer-to-peer lenders), licensing their technology, or investing in their business and getting board seats.

Last month, I wrote that being a late mover can be less financially ruinous than being an early mover. A fast-growth business with low barriers to entry attracts a lot of competitors who burn increasing amounts of capital chasing each other's customers more than new ones. Patience and playing to strengths is a better response than betting the balance sheet in an unfamiliar casino.

This week, the Financial Times ran an interesting article on the trials of peer-to-peer lenders. Finding lenders is hard, and finding borrowers is proving even harder. Incumbent banks have lower costs of capital, which allows them to lend at lower rates. In periods of economic uncertainty or downturn, banks offer safety to cash-holders in the form of insured deposits.

What does the FT article recommend? That the survivors will be those who follow a "tortoise" strategy:

That means working with investors with a low cost of capital [...], and avoiding yield-hungry hedge funds. It means not reaching too hard for high returns, which will become high losses in a recession. It means sticking to niches with good borrowers, who are too hard for big banks to serve. Finally, it means not spending excessively on marketing.

All this, of course, implies slow growth: hence the tortoise. But the hares are set for a very nasty couple of years.

'Tis better to arrive late than not to arrive at all.

Late Mover Advantage

Many years ago, I worked with a company that helped big pharma companies distribute free medical samples to doctors. Having pharma products on-hand is a convenience for doctors and patients alike, mainly because a doctor can initiate immediate treatment for a patient. Having pharma products on-hand is also good for big pharma, as starting somebody on a medication is highly likely to lead to a prescription. So pharma manufacturers were motivated to avail free samples of medicines to doctors.

It's a regulated activity. Doctors can only get medicines appropriate for their practice (e.g., a pediatrician cannot get free samples of Cialis) in limited quantities for specific lengths of time (usually every x number of days). Pharma companies keep track of which doctor got what product on what date. Because doctors exhaust their supply of free product within the allocation time frame, doctors create standing re-order requests. The pharma company decides whether or not to fulfill a doctor's reorder request primarily based on when the last order was fulfilled and the quantity supplied. A pharma manufacturer would not supply more free product to a doctor who had received the maximum volume just a week ago if the reorder window is 21 days.

Because doctors obtain pharma samples from multiple manufacturers, a lot of intermediaries popped up to provide a consolidated service. The value prop of the intermediary was that a doctor need only visit one site to replenish samples from multiple drug manufacturers. Again, because doctors exhaust their supply of free product every few weeks, they were encouraged to set up recurring orders through the intermediary, so a doctor might request multiple products from multiple manufacturers with different replenishment rules. The intermediary made money by charging the pharma company for each free sample request they fulfilled. The pharma company treated it as a marketing expense, effectively treating these intermediaries as a channel partner and paying them a commission. The volume aspect made every doctor acquired very valuable indeed.

The theoretical market numbers were eye-popping. Just one of the big pharma firms measured the total product value they gave away in the form of samples, coupons and vouchers to be nearly $2 billion through all channels of distribution. At the time there were about a dozen or so bulge bracket pharma firms. The free pharma product business was big business indeed.

The company I was working with had a division that was one such intermediary. It was supposed to be a growth business in the portfolio: as mentioned above, pharma samples were a big business and the order volume had plenty of room to grow. But the performance was never all that impressive. When intermediaries made enough mistakes (e.g., process the reorder too early or not at all and doctors don't replenish their sample stock in a timely fashion), and the doctors will sign up with another intermediary. Since the pharma companies were the ones managing the replenishment data (what doctor received what product in what quantity on what date) and enforcing the replenishment rules, and since the pharma companies had no exclusive distribution agreements with any intermediary, a doctor could set up the same reorder profile with a dozen different intermediaries. The first intermediary to process the reorder successfully won the business that day. And, not only were there lots of intermediaries competing for the same business, the pharma companies operated their own direct-to-doctor channels as well - and distributed the bulk of the product that way.

Being a crowded field, intermediaries had no pricing power with the pharma companies. By way of example, at the time the internet travel booking business had a take rate of somewhere between 5 and 10% of the value of the travel services they were selling; intermediaries had a take rate of a tiny fraction of a percent of the value of the product order they were submitting.

In short, there was no customer loyalty, no vendor exclusivity, and no pricing power in this business. There was a market, but no obvious winning strategy. Infrequent site visits meant that user experience wasn't going to provide an edge. Orders duplicated across multiple intermediaries meant that even the smartest algorithms and the fastest technology would provide only a fleeting edge as competitors would quickly catch up. Acquisition wasn't an option as every seller would demand too high a price for little value in the form of assets or cash flow. Industry consolidation would simply formalize the value destruction that had already taken place but not been accounted for.

An intermediary couldn't crush the competition with customer love, innovation, tech firepower, or scale. They were in a state of mutually assured destruction. The only strategy was to hope that your competitors ran out of cash before you did.

Recent analyses in the financial press on the ride sharing, home meal kits and food delivery industries got me thinking about that company again. They compete in crowded fields amid the challenges of low switching costs, low margins, little differentiation, and no customer loyalty. As the Wall Street Journal put it, these companies are now engaged in "a land grab for overlapping customer bases". Every ride from Lyft and Uber is still subsidized by investor capital. There wasn't enough of a market for home meal kits to support the number of firms competing for it.

The Journal makes the point that the would-be disruptors in home meal kits have done more to disrupt one another than they have to established players in retail food, and that's an important point. That these firms are "disrupting" in a different competitive landscape to their technological forebears: building a business at the expense of sleepy competitors in legacy industries (as firms such as Amazon and Expedia benefited from in the 1990s) is much different than trying to do so with evenly-matched competitors.

This casts doubt on the investment case. The long play for all of these companies is winner-take-all: all the chips go to the last player standing. Reuters Breakingviews estimates that the total current market cap of food delivery firms prices in optimistic growth, profitability and value multipliers. Breakingviews goes on to point out that an "... optimistic ending would be one firm knocking out rivals and boosting its pricing power. A more likely one may be that valuations, far from getting hotter and rewarding venture capitalists, grow cold." And what if it isn't a contest worth winning? Groupon won the online coupon competition. It didn't work out too well on a total-return-on-capital basis.

As mentioned above, I've seen this movie before. The Journal article was titled "Mutually Assured Destruction in Silicon Valley." That's apt.

The chattering classes and management consultants advocate for incumbents get into the disruption game themselves. It's certainly good for the pontificators and suits if the incumbents do, because it generates clicks on articles and contracts for services. But doing so asks established firms to enter into very expensive gambles that don't play to any of their strengths, and may offer no payoff whatsoever. Pundits and consultants are very good at spending other people's money - on themselves. Incumbents need to concentrate on their strengths, not their weaknesses.

The incumbent's response to disruption in financial services offers some insights. Clearly, Fintech has had an impact: things like loan origination are far more efficient at banks today than they were just a few years ago. But the disruption storyline in finance is far more muted, in large part because of the way the incumbents responded to it. Incumbent financial services firms didn't try to enter as competitors to the startups, but employed a combination of tactics including infiltration (experienced bankers dominate FinTech boards), co-option (licensing and integrating new technology), and economic might (buying loan books). It should come as no surprise that today, FinTech lenders look more like banks than banks look like FinTech lenders.

Rather than taking a high-risk position well outside of a firm's comfort zone and competencies, patience can be a better strategy. Enter into non-exclusive partnerships and licensing deals and lightly finance the entrants to encourage competition, penetrate their boards to influence their strategy, and alter their book of business to make them economic dependents, all while cleaning up your balance sheet to have more equity and less debt. The incumbent that can do that will have a stronger risk footing when the time is right to strike in changing market dynamics.

Organizing for Innovation, Part VI: Putting It All Together

As we saw in the previous posts in this series, organizations of autonomous teams can scale. Scaling requires different team characteristics (requisite variety, redundancy of function, double-loop learning and minimum critical specification), a different mental model of organization (brain, not machine), a different kind of hierarchy (purpose, not control), and a different style of leadership (guide, not command).

This sounds like it would be chaos in practice on a small scale, let alone enterprise scale. And even if it does work, it sounds like a revolutionary approach to organization. Visualizing it helps explain how all these things combine to create an organization that innovates as well as operates.

The organization of autonomous teams is not chaos. Teams are invested with authority, communication pathways develop along the hierarchy of purpose, the structure adapts itself to the domain, and organizationally-driven objectives plus team incentives align behaviors and outcomes. Of course, this looks nothing like traditional organization, in which hierarchy is the principal means of control, communication, and alignment. Still, while an organization of autonomous teams may be unfamiliar and conceptually unsettling, it isn't chaos.

And, although it may seem revolutionary, it isn't new. Academic research on organizations of autonomous work teams dates to the 1950s, and the early adopters of it were industrial firms. While it may be a way of working that a lot of tech companies happen to adopt for themselves, it is not an organizational phenomenon that has sprung out of tech. So it isn't all that revolutionary, either.

It may not be chaotic or revolutionary, but it is a big leap from how just about every enterprise functions today. The way they work reflects the board's priority for the company. I've written before that companies are financial phenomenon more than they are operating phenomenon. Executives tasked by their boards to prioritize returning cash to investors will look to maximize cash earnings (EBITDA) and free cash flow. In so doing, those executives create an environment where managers must be more concerned with costs than creativity from operations. Managers, in turn, condition employees and contractors to value activity over learning, output over outcomes, and narrow individual independence over broad group autonomy. In this environment, employees, managers and executives are rewarded for every dollar not spent for output; they will not be rewarded for any dollar spent on learning.

With enterprises increasingly feeling the heat from new companies, technologies and products, executives charge managers with extracting more innovations from the business. Managers go about looking at changing ways of working, recognizing that innovation is partially a byproduct of "how" things are done. But as long as the priority of the board is to return cash to investors, the first mission of "how" things are done is to be predictable, because predictability ensures consistent cash flows and consistent cash flows ensure that interest payments, dividends and buybacks can be made while protecting the bond rating. The autonomous team structure is incompatible with predictability.

The autonomous team structure is internally consistent (not chaos) and been around for long enough with enough successes (not revolutionary) that it can succeed, even at scale. Pursuing it is ambitious, and operationalizing it is plenty difficult. But success with it has less to do with operationalizing than it does with the tolerance for it in the capital structure in which it operates. Autonomy - that is, abdication of centralized control - is the price of admission for innovation. Innovation at scale requires autonomy at scale. Autonomy at scale requires the board be committed to innovation, not cash returns, as their top priority.

Organizing for Innovation, Part V: The Leadership Challenge

Organizations of autonomous teams require a different set of behaviors than organizations that are run like a machine. People in a self-directed team form their own appreciations for what should be done, prioritize what will be done, and self-determine how it will be done. They are unencumbered by hierarchy, expected to communicate with anybody in the organization they need. They are unencumbered by role definition, as people simply do whatever work is required even when that means acquiring new skills or knowledge. They are unencumbered by organization, as teams form task-forces to solve for problems that are beyond the scope of any single defined team.

This all sounds great. Who wouldn't want to work this way?

The majority of people working in enterprise IT today, that's who.

Enterprise tech labor is highly codified (bounded responsibilities) and stratified (seniority). Aside from the fact that this serves the interests of a multitude of non-tech corporate functions such as human resources, vendor management and finance, it also provides a great deal of comfort to the individual. The employee knows very precisely what is expected of them to earn a salary increase or advance their career, while the contractor knows what they are obliged to do to satisfy the terms of their contract. Over time, jobs become working annuities that require little servicing (such as skill acquisition or excessively long working hours) for comfortable levels of compensation with job security (by e.g., being the only ones familiar with a technology, service or function).

The autonomous team environment is anathema to this. For starters, the autonomous team operates with a lot of ambiguity. New appreciations change the team's priority constantly, meaning there is no deterministic plan. Plus, people adapt themselves to the work that needs to be done as opposed to working in strict swim-lanes. An autonomous team environment implicitly subordinates the traditional goals of the individual: the success of the team is success for the individual. An autonomous team breaks down when its members put individual achievement over team accomplishment.

Of course, working this way is a question of both skill and will. Whether labor can re-orient itself from machine to autonomy is a debate well beyond the scope of any blog post, but suffice to say that some - few, several or most - will not be able to make the transition from doing what they are told to do by management, to figuring out for themselves what needs to be done and doing it. In addition, whether labor willingly chooses to re-orient itself is another matter entirely. Over-exposure to enterprise change programs - or more likely, over-exposure to failed enterprise change programs - won't inspire enthusiasm for yet another one. There is also the suspension of disbelief people need to make to give devolved authority and autonomous team structures a try.

This brings us to the leadership challenge that creating an organization of autonomous teams poses. Obviously, there are institutional barriers that have to be overcome. HR has to be comfortable with ambiguous roles and titles. Vendor contracts for supply of specialist labor have to be replaced with contracts tied to business outcomes. Finance needs an alternative to predictive planning and financial budgeting.

However, the challenge to leadership goes beyond mechanical processes and structural changes. In an organization of autonomous teams the nature of leadership changes from giving commands to guiding actions. The leader does not direct people's performance to achieve a goal (organization-as-machine), but instead projects a goal that people direct themselves toward achieving (organization-as-brain). The leader in the autonomous organization makes use of:

• Concrete statements of expectations: leaders have to make expectations crystal clear. Saying "all enterprise software is deployed into production every other week" is a clear expectation. Saying "we will be a continuous deployment organization" is a woolly statement: "continuous" will be interpreted relative to the current skills and learned helplessness of the people responsible for enterprise software today. The latter statement also misses the point: how the organization functions (continuous deployment) is less important than describing what it achieves (new software released across the board every other week.) The prescriptive implications of that statement deny the people in the organization the opportunity to figure out for themselves how best to achieve the goal. The leader communicates the expectation; it is up to the people in the organization to figure out how to achieve it.
• Rewards and recognition: obviously, what gets measured is what gets managed. If the goal is to increase frequency of deployments, reward the teams that find ways to make reliable production releases weekly, then twice weekly, then daily, then multiple times per day. Story-telling is important as well, especially as an organization adopts new behavioral norms and its business partners develop new expectations of it. For example, in its early days, FedEx executives were fond of repeating the story of the junior employee who rented a helicopter on his personal American Express card to fly him to a mountain location to repair snow-damaged phone lines so that they could service remote customers. When you're building a reputation for absolutely, positively getting packages delivered overnight, stories like this go a very long way.
• Acknowledging and solving constraints: every team, not to mention the organization as a whole, will be short of the capacity, capability and capital to achieve every organizational goal. While constraints force institutional responses such as prioritization and innovation, they are also opportunities for a systemic change. It is the leader's obligation to work with constrained teams to identify the actions they can take today so that they will be less constrained in the future. For example, if there is a capability constraint, what can the team do now to develop skills and knowledge so that it can do more for itself? If a financial constraint, how can the team build a stronger business case or define a set of experiments to explore potential value? In the face of constraints, the leader's responsibility is to develop an organization's ability to learn how to learn.

At the foundation of the organization of autonomous teams is Theory Y. The aspirational leader of an organization of autonomous teams has to believe that people are motivated, responsible, and do not need close supervision. If you do not fundamentally believe this, do not bother pursuing an organization of autonomous teams. Once you revert to command-and-control (Theory X), you have lost the mantle of leadership in a devolved organization.

In the next installment in this series, we will put all of these concepts together to visualize what the autonomous organization looks like at scale.