If you’ve taken a look at September’s U.S. unemployment figure – 3.5% — you’re seeing the lowest level of unemployment in over 50 years. And for particular subgroups of the population, they’re enjoying their lowest employment percentages ever — at least since records have been kept.
It’s definitely something to cheer about. But at the same time, it’s become increasingly evident that wage growth isn’t happening in tandem with lower unemployment. And that includes industrial wages as well.
In fact, September results show the first dip in wages – albeit slight – in the past two years.
According to Liu and Leduc, as certain tasks move more toward automation, employees are losing bargaining power within their organizations. When people fear that they could lose their jobs to a robot or a machine, there’s a hesitation to ask for higher wages as that might hasten the eventuality.
The net result is a widening gap between productivity and pay.
The reason? Complexity, volume and margins are often barriers to the implementation of automation in many applications. Just because something can be automated doesn’t mean that there’s a compelling economic argument to do so – particularly if the production volumes aren’t in the league of “mass manufacturing.”
Jobs in engineering and R&D are even less likely to become automated. After all, probably the single most important attribute of employees in these positions is the ability to “think outside the box” – something artificial intelligence hasn’t come anywhere close to replicating (at least not yet).
What are your thoughts about automation and how it will affect employment and wage growth? Please share your perspectives with other readers.
What with the inexorable march of technology – which sometimes seems more like a relay race – it’s interesting to speculate on which occupations will be most in demand five years or ten years from now.
That seems pretty reasonable. But what about 20 years on?
Is it even possible to predict which jobs will be most in demand by then – particularly in the tech sphere? Or is that a fool’s errand, destined to elicit howls of laughter should anyone deign to look back at 2020 predictions when 2040 rolls around?
As it happens, the prognosticators at British multinational defense, security and aerospace company BAE Systems are willing to stick their necks out on the topic. They asked their own futurists to tell the what the top jobs in tech might be in 2040.
In broad terms, the answer is that future jobs will be in professions that bridge technology. More significantly, it will be the technology that is the primary job generator, not the profession itself.
But it you really want to bottom-line it, anyone who focuses on artificial intelligence, virtual reality or robotics should be able to future-proof his or her career. At least, that’s the unmistakable takeaway from the jobs that have been earmarked as the “hottest” ones looking ahead 20 years.
And … here they are:
AI Translator – People in these jobs will train other humans as well as their artificial intelligence assistants or robot counterparts, tailoring AI to meet workers’ needs and tune it to acknowledge and correct human errors. Smart-aleck machinery – it’s just what the world’s been waiting for …
Recommended educational background: IT studies, cybersecurity, mechanical engineering
Automation Advisor – As companies become more reliant on automation and robotics, people in these jobs will make sure that the automated workforce is in line with regulations. Compliance officers for machines – why not?
VR Architect – As AI models are used to predict maintenance, people in these jobs will use virtual and augmented reality to monitor components and manage maintenance activities. That’s OK – plant maintenance has always been a responsibility with a lot of downsides …
Recommended educational background: IT studies, graphic design
Human e-Sources Manager – Differing from today’s human resources managers, people in these jobs will analyze data collected from exoskeletons, smart textiles, wearables and the like to perform predictive and preventive maintenance on human workers. Isn’t that nice; sensors will now send alerts to your manager when you’re overworked, overstressed, overweight or otherwise unwell — brilliant!
Systems Farmer – people in these jobs will help companies grow large multifunction parts with nanoscale features, which will sense, process, harvest energy and perform self-repairs. It’s otherwise known as “chemputing” – and it’s likely as unappealing as it sounds.
Recommended educational background: Biology, chemical engineering, chemistry
AI Ethicist – As autonomous systems are assigned more responsibility, people in these positions will make sure AI devices and robots don’t show bias, and will make decisions that best serve the business. I wonder how well that initiative will turn out?
Kidding or snark aside, it is worthwhile to “navel-gaze” along these lines and think of the “what if” scenarios that could very likely paint an employment picture unlike anything we’ve ever contemplated before.
And indeed, BAE Systems fielded research that found that nearly half of people between the ages of 16 and 24 who were surveyed think that they’ll end up having a career in a job that doesn’t even exist yet.
The only problem is – practically no one surveyed had any sort of clue what that future job will be — or how to prepare for it.
What do you think about which jobs will have the most job security in 2040? Does the list above ring true, or are there others that deserve a place on it as well? Please share your thoughts with other readers here.
It’s a common theme that we hear: Artificial intelligence and robotics are coming for many of the jobs that have traditionally been performed by humans.
But what about the fate of animals?
That prospect was raised recently by David Mantey, a writer for Thomas Publishing, in an article about what’s happening in rice paddy fields. And it involves ducks.
More specifically, aigamo ducks, which are a cross between mallards and domestic fowl. There is a farming method, originating in Japan, that employs these creatures to clear and keep unwanted plants and parasites out of rice paddy fields.
Essentially, it’s an environmentally-friendly practice in which the ducks keep the paddies clear without the need for pesticides. As an ancillary benefit, the ducks’ own waste acts as fertilizer for the rice plants.
The centuries-old practice was revived in Japan the mid-1980s, and has since become a popular natural rice farming method beyond that country, used in places like China, Iran and France.
Broadly speaking, approximately 15 ducks can keep more than a 10,000 sq. ft. area clear of weeds and insects, while also enriching the water with oxygen via stirring up the soil beneath.
It seems like a neat and tidy solution all-around — and one that works based on decades of experience with the farming practice. But as it turns out, it’s something that a robot can accomplish, too (well, maybe not the duck waste bit) — with certain improvements on the original tradition.
While ducks can be “trained” to patrol specific areas of a rice paddy, it isn’t a foolproof proposition. As for the robotic version (which looks more like a white, floating ROOMBA® than it does a duck), it utilizes wi-fi and GPS technology to stir up the soil and keep the bugs at bay.
Reportedly, the robot is more accurate and more consistent in its execution compared to the aigamo ducks.
At the moment, the rice paddy robot is in an experimental phase with beta prototypes patrolling paddies in Yamagata Prefecture in northern Japan — and it’s too soon to know if or when the robot will be deemed ready for commercialization.
But the development goes to show that robots are spreading into some very surprising corners. Indeed, it seems that robotics technology knows no bounds.
Artificial intelligence and machine learning are two phrases that have become regular currency in the marketing world over the past several years. It isn’t hard to figure out why, as both AI and machine learning have the potential to help marketers make sense of the ever-increasing volume (and complexity) of raw data that’s become available in increasing amounts, thanks to the digitization of “everything.”
Some people use the two terms interchangeably, but that isn’t exactly right. According to Thorin McGee, director of content at Fast Capital 360, the distinction is subtle yet significant:
AI is when you develop an algorithm that allows a computer to “think” for you towards achieving a goal.
Machine learning is when you let a computer create an algorithm to find ways to meet the goals you give it, based on large pools of data.
Put the two together, and you have the ability to gain some really deep insights into what your data is actually telling you, thereby improving decision-making success.
On the data front, this great potential is tempered by some significant challenges. Christopher Penn, chief innovation officer of marketing data and analytics consulting firm Trust Insights, characterizes them as the “5 V’s” of data:
Volume — There’s so darned much of it.
Variety — More kinds of data are being churned out.
Velocity — Data is coming at us faster than ever.
Veracity — If data isn’t verified, it can do more harm than good.
Value — In raw form, data isn’t particularly useful. Like oil, data needs to be refined to be of value.
If getting your arms around data seems like trying to hug a stream of water, you aren’t alone in thinking that. Many companies are pretty adept at using data to identify what happened — and maybe even to diagnose problems and why they happened. But it’s less easy to predict what will happen based on data … and even harder to use data to determine with confidence what should happen.
The biggest challenge — but also the one with the biggest potential payoff — is tapping machine learning to process and use data in forging future business as you wish it to be.
To date, very few companies have come all that close to becoming AI-powered enterprises. But it’s where we’re headed in the coming decade. It represents one of the biggest opportunities for differentiating one company from another. But it will require a disciplined and concerted effort: talent acquisition (developers and data scientists), tapping outside vendors, along with taking available open-source code and building upon that to implement the appropriate marketing technologies.
Oh, and committing to a multi-year initiative and budget even after all of those other pieces are in place.
Surveying the current landscape, are there particular entities that you see as on the leading edge in applying AI and machine learning to their marketing endeavors? Please share your observations with other readers.
Recently, Lee published a column in which he described ten job categories that he feels are “safe” for human workers – regardless of how the AI world may develop around us.
His list is predicated on several fundamental weaknesses Lee sees with AI in handling certain aspects of job performance. Those weaknesses include:
An inability to create, conceptualize or manage complex strategic thinking
Difficulty handling complex work that requires precise hand-eye coordination
An inability to deal with unknown or unstructured spaces
The inability to feel empathy and compassion … and to react accordingly
In short, Lee discerns a particular weakness in AI’s ability to perform “humanistic” tasks – ones that are personal, creative and compassionate. Hence, the type of jobs that rely on such qualities will be safer from disruption, he believes.
As for career categories that Lee singles out as generally safe from AI disruption, he cites these ten in particular:
Computer Science – Lee predicts that a substantial portion of computer engineers, IT administrators and technology consultants will continue operate in job functions that aren’t automated by technology.
Criminal Law – The legal profession won’t be adversely affected, considering the persuasive powers that are needed to sway juries with legal arguments. However, some paralegal tasks such as document review will likely migrate to AI applications.
Management – Simply put, there are too many “moving parts” to management – and aspects that require human interaction, values and decision-making – to make it a field that’s amenable to AI. Of course, if a manager is more along the lines of a bureaucrat carrying out set orders, that type of job may be more susceptible to AI disruption.
Medical Care – Lee envisions a symbiotic relationship between humans and AI — the latter of which can help with the analytical and administrative aspects of healthcare but cannot handle most other healthcare responsibilities.
Physical Therapy – Dexterity is a challenge for AI, which makes it unlikely for AI to replace jobs in this field (also including massage therapy).
Psychiatry – Positions in this category, which encompass social work and marriage counseling in addition to strict psychiatry, require keen emotional intelligence which is the domain of humans.
R&D (particularly in AI-related field) – While some entry-level R&D positions will become automated, increased demand for R&D talent will likely outnumber the jobs replaced by AI.
Science – According to Lee, while AI will be of tremendous benefit to scientists in terms of testing hypotheses, it will be an amplification of the discipline rather than taking the place of human creativity in the sciences.
Teaching – While AI will be a valuable tool for teachers and schools, instruction will still be oriented around helping students figure out their interests and providing mentorship – qualities that AI lacks.
Writing – Specifically fiction and other creative writing, because “storytelling” is an aspect of writing that AI has difficulty emulating.
So, there you have it – Kai-Fu-Lee’s fearless predictions about the job categories that will remain safe in an increasingly AI world. Can you think of some other categories? Please share your thoughts and perspectives with other readers.
As a marketing professional for the better part of four decades, I can’t imagine any of us doing our jobs without soaking up as much data as possible to help with our decision-making.
And data accessibility is miles ahead of where it was when I first entered the marketing field. Back in the day, “finding data” meant hitting the reference libraries to access government or other reporting – especially if you were lucky enough to be located within a reasonable distance of one.
There was the phone for real-time information-gathering … and also the FAX machine for quick receipt of “facts in brief” — not to mention the “wait-and-wish-for” mail and package delivery services.
If it was insight you needed from customers or prospects about a new industry or business venture, primary research was always an option — if you had the money and the time to allocate to the effort.
As for “first-party” data, that was available as well – but how often were we at the mercy of the bureaucratic machinations of in-house IT departments to get even basic data requests processed in a timely way?
All of which is to say that marketers have always used data – but the quantity wasn’t as great, while the timeframe of data acquisition was at a snail’s pace compared to today’s reality.
But now, after having become quite spoiled at the availability of all sorts of information, might it be that we’re regressing a little?
In particular, third-party information purchased in bulk, often from data aggregators, seems to be where the backsliding is occurring.
Consider ad targeting and building audiences: We have access to valuable first-party data thanks to website analytics and studying the results of our own e-mail campaigns.
There’s also been third-party behavioral data from three big behemoths — Google, Facebook and Amazon – that can be used for MarComm targeting purposes. But of those three platforms, just one of them allows third-party data to be made publicly available to end-users.
This poses challenges for the suppliers that aggregate and sell third-party data, as the quantity and quality of their information isn’t on the upswing at all.
Fundamentally, finding a good source for third-party data entails understanding what sources each data aggregator is using and the methodology it employs to collect the data. Factors of scale, quality, reputation and price also come into play.
But despite best efforts, when testing third-party data for MarComm campaigns and lead-generation efforts the results are often pretty ugly — the data loaded with inaccuracies and basically terrible for efficiency metrics.
It doesn’t help that with the rise of Amazon as yet another “walled garden” of data, the “open web” represents a ever-smaller portion of the total ad spend — and hence also a decreasing amount of the third-party data that’s available to end-users.
With the veracity of third-party data becoming more suspect, it’s had an interesting effect on data management platforms, which are now focusing more on the actual messages themselves and not the “personas” of the people receiving the messages or how they were identified and targeted.
Is it possible for third-party data to provide good information to AI systems — intelligence that can verify and augment the value of the first-party data? If leading ad platforms can use such third-party data to enhance the accuracy and value of what they sell to advertisers, there still may be valuable material to work with. As it stands, though, I’m not sure that’s the case.
What are your experiences? Please share your perspectives with other readers here.
Occasionally I run across an opinion piece that’s absolutely letter-perfect in terms of what it’s communicating.
This time it’s a column by marketing über-specialist Gord Hotchkiss that appeared this week in MediaPost … and he hits all the right notes in a piece he’s headlined simply: WTF Tech.
Here is Hotchkiss’ piece in full:
By Gord Hotchkiss , Featured Contributor, MediaPost
Do you need a Kuvée?
Wait. Don’t answer yet. Let me first tell you what a Kuvée is: It’s a $178 wine bottle that connects to WiFi.
Ok, let’s try again. Do you need a Kuvée?
Don’t bother answering. You don’t need a Kuvée.
No one needs a Kuvée. The earth has 7.2 billion people on it. Not one of them needs a Kuvée. That’s probably why the company is packing up its high-tech bottles and calling it a day.
A Kuvée is an example of WTF Tech. Hold that thought, because we’ll get back to that in a minute.
So, we’ve established that you don’t need a Kuvée. “But that’s not the point,” you might say. “It’s not whether I need a Kuvée. It’s whether I want a Kuvée.” Fair point. In our world of ostentatious consumerism, it’s not really about need — it’s about desire. And lord knows many of the most pretentious and entitled a**holes in the world are wine snobs.
But I have to believe that, buried deep in our lizard brain, there is still a tenuous link between wanting something and needing something. Drench it as we might in the best wine technology can serve, there still might be spark of practicality glowing in the gathering dark of our souls. But like I said, I know some real dickhead wine drinkers. So, who knows? Maybe Kuvée was just ahead of the curve.
And that brings us back to WTF tech. This defines the application of tech to a problem that doesn’t exist — simply because it’s tech. There is no practical reason why this tech ever needs to exist.
Besides the Kuvée, here are some other examples of WTF tech:
The Kérastase Hair Coach
This is a hairbrush with an Internet connection. Seriously. It has a microphone that “listens” while you brush your “hear,” as well as an accelerometer, gyroscope and other sensors. It’s supposed to save you from bruising your hair while you’re brushing it. It retails for “under $200.”
The Hushme Mask
This tech actually does solve a problem, but in a really stupid way. The problem is obnoxious jerks that insist on carrying on their phone conversation at the top of their lungs while sitting next to you. That’s a real problem, right? But here’s the stupid part. In order for this thing to work, you have to convince the guilty party to wear this Hannibal Lecter-like mask while they’re on the phone. Go ahead, buy one for $189 and give it a shot next time you run into a really loud tele-jerk. Let me know how it works out for you.
Denso Vacuum Shoes
“These boots are made for sucking, and that’s just what they’ll do.”
Finally, an invention that lets you shoe-ver your carpet. That’s right, the Japanese company Denso is working on a prototype of a shoe that vacuums as you walk, storing the dirt in a tiny box in the shoe’s sole. As a special bonus, they look just like a pair of circa 1975 Elton John Pinball Wizard boots.
When You’re a Hammer…
We live in a “tech for tech’s sake” time. When all the world is a high-tech hammer, everything begins to look like a low-tech nail. Each of these questionable gadgets had investors who believed in them. Both the Kuvée and the Hushme had successful crowd-funding campaigns. The Hair Coach and the Vacuum Shoes have corporate backing.
The dot-com bubble of 2000-2002 has just morphed into a bunch of broader-based — but no less ephemeral — bubbles.
Let me wrap up with a story. Some years ago, I was speaking at a conference and my panel was the last one of the day. After it wrapped, the moderator, a few of the other panelists and I decided to go out for dinner. One of my co-panelists suggested a restaurant he had done some programming work for.
When we got there, he showed us his brainchild. With much pomp and ceremony, our waiter delivered an iPad to the table. Our co-panelist took it and showed us how his company had set up the wine list as an app. Theoretically, you could scroll through descriptions and see what the suggested pairings were. I say theoretically, because none of that happened on this particular night.
Our moderator watched silently as the demonstration struggled through a series of glitches. Finally, he could stay silent no longer. “You know what else works, Dave? A sommelier,” he said. “When I’m paying this much for a dinner, I want to talk to a f*$@ng human.”
Sometimes, there’s just not an app for that.
Does Gord Hotchkiss’ column resonate with you as it did me? Feel free to leave a comment for the benefit of other readers if you wish.