There’s been a lot of buzz lately about the threat to our way of economic life from robots and artificial intelligence. Many see major problems, and the fears they express tend to draw attention.
Tesla founder Elon Musk, for instance, made headlines predicting the end of the world in what resembled — more than a little — the story line from the Terminator franchise. Others have described robots replacing workers in businesses and prompting unemployment rates of 50 percent or more. And still other naysayers have said they suspect robots will take over production, thereby forcing all humans to become marketers.
But there also have been those who argue that the threat is exaggerated and robots and AI could be a real benefit, as they could free us from hard and dangerous work. These forecasters have said this will be a win primarily for low-skilled workers, an advantage good for business.
Regardless of the eventual outcome, the advances in technology and software will almost certainly change the economy in both the short- and long term. Regulators will respond to the challenges, raising still more dire predictions — this time from within the tech industry — where observers say they fear government will soon have to pay our salaries and therefore should foot the bill by taxing robots as “employeees.”
Whatever the technology revolution eventually looks like, it will change the nature of business and the nature of how startups are managed and decisions made. In short, the robot revolution will affect how we hire and fire, and how human resources operates, in general. In that context, here are four potentail trade-offs every startup will need to make:
1. Flexibility vs. productivity
The robots that are already out there can be enormously productive, working with unbeatable precision and stamina, and never needing a cigarette break. They don’t gossip or bully. They aren’t jealous of the next guy getting a raise. But their downside is that they can perform only one or a few very specific or standardized tasks, whereas an employee can chip in wherever needed.
The truth is, nothing beats a real human being in terms of the scope of things we can do — and do well. That’s why 20th century economist Ludwig von Mises made a point of stating that human labor is unspecific but that other factors of production are not. “More specific” means more productive but less flexible.
In startups, this flexibility is an enormously important quality that beats the productivity of machines any day. Robots and software can carry out specific tasks with superb productivity, but they cannot switch to sweet-talking a neglected customer when that’s what needs to be done.
2. Problem-solving vs. production
Robots can’t solve problems that have not first been clearly defined. And even then, they can be used effectively only if a solution is available and engineered.
This applies to machine learning and AI too, even though those technologies are super-powerful at finding proverbial needles in data haystacks. They can find patterns that are hidden to the naked (human) eye. But — again, the downside: Where patterns don’t persist and things change over time, these technologies simply don’t work well.
The typical startup is also far from the structured environment that these technologies require. This is not a question of data availability, but of soft skills and innovative problem-solving.
Most startups struggle with tweaking the business model, improving consumer relations and putting out (plenty of) fires. Those changes require advance interpretation and understanding; they need the ability to change quickly and go in a new and not necessarily well-defined direction.
And these are qualities that people have but machines do not.
Simply put, if the problems in your startup are straightforward or at least can be solved using codified information, then choose technology over people. Otherwise do not.
3. The creation of value vs. structure
It bears repeating that startups are not smaller versions of the larger company. They’re separate and unique; they do different things and do them differently.
Startups also attempt to find and refine their market niche and value proposition, and they struggle to make ends meet, whereas established firms focus on structuring their organization and standardizing production processes.
In other words, startups are in pursuit of creating new value and trying to discover the extent of their perceived entrepreneurial opportunity.
Large companies, in contrast, are exploiting their opportunity. Their main focus is on profit maximization through cost-cutting, standardizing and streamlining production.
So, large companies are solving an entirely different problem, approaching that solution through structure, control and management — which is fundamentally conducive to automation and, therefore, to machines instead of people.
4. Outsourcing vs. in-house production
While production in mature businesses is more streamlined and structured, these businesses have often established a cost advantage for what they do in-house.
Startups are different and generally cannot afford to think in terms of optimized production volumes or cost minimization. Indeed, cash-flow problems kill 25 percent of startups.
It’s more important for startups to avoid large up-front investments than to find the cheapest way to produce. Purchasing the machines necessary for in-house production makes little sense when the firm’s survival depends on cash flow.
It could be a recipe for success to not produce in-house, to avoid huge outlays — even if that means higher COGS. If a company is in that positon, it should take advantage of other businesses’ cost minimization efforts by outsourcing.
Machines undoubtedly have a place in business, but humans do, too. The advances in robotics and AI shift the boundaries for what machines can do, but machines alone cannot replace humans in everything. We are unbeatable in dynamic milieus and open-ended tasks due to our “soft” skills: creativity, imagination and problem-solving.
And these skills are core to entrepreneurship. So, perhaps there’s no reason to fear technology for the immediate future, if ever.