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Preparing for disintermediation: Or what will the future look like in a global gig economy?
A presentation given at the tolk- en vertaalcongres in Breda on March 9, 2018 titled “The Language Industry 4.0: Embracing the future?”
Please, let me introduce myself briefly. I am an independent business consultant, helping customers choose and implement best-suited technologies and redesign their business processes to get the best in multilingual content production, translation, and localization.
I have been working in the language business since 1982.
And with machine translation (MT) since 1991.
Today, this presentation will raise a few basic questions following the classic “Five Ws (and One H)” rule of rhetoric: What is the gig economy? Who benefits from it? Where does it apply? When does it prevail? Why is the translation industry affected? How is disintermediation relevant? These questions will in turn raise a few more ones. Let’s start with a brief recap first.
While the translation profession as we know it today was born in between the two world wars, with the development of world trade, the so-called translation industry was born between the late 1980’s and the early 1990’s, with the spread of personal computing. In practice, with the burst of technology, in a few decades, a century-old single practice rapidly evolved into shops and then into an industry.
The same irruption of technology has led to two new industrial revolutions. In fact, in 2011, the German government coined the term “Industry 4.0” to indicate the “fourth industrial revolution” with smart machines capable of autonomously exchange information, triggering actions and control each other independently via the Internet, big data analytics, and AI. Does the language industry fit “4.0”? With some effort and a little imagination, the translation industry could be halfway between “2.0” and “3.0.”
Let’s now address the six fundamental questions. The first one is, what is the gig economy? The term gig was coined in the 1920s by jazz musicians to mean “engagement.” The concept of gig economy was introduced in 2009, when the financial crisis began to bite badly, to describe the economic activity of people using digital platforms for short-term engagements to make a living.
A gig economy typically develops after the disruption of markets following the establishment of technological platforms connecting businesses and independent professionals. In this respect, any market is exposed to the gig economy if its players can be digitally connected to customers regardless of their size and position. The use of self-employed workers is not a peculiarity of post-crisis years. Businesses have been trying for decades to replace the traditional employment model to escape taxes and labor laws. Previously, intermediaries were used instead of digital platforms.
It has been happening, from consumption and leisure to services and manufacturing. Companies like Airbnb, Amazon, Foodora, Netflix, Uber, Upworks have been disrupting their sectors and nothing can apparently stop them, not even the class actions of drivers and riders or the efforts to have them pay their dues to the communities they thrive on.
The business model is roughly the same as that of the gig economy. The parcellation of jobs, the infinite quest for the lowest remuneration, the way jobs are dispatched, and how people are hired and remunerated in the gig economy is no news in the translation industry. So even the most celebrated companies of the gig economy have little to teach to their translation industry counterparts except, maybe, for the tech element and the sophistication in tax elusion.
The promises of the gig economy may sound appealing. Digital technologies let workers become entrepreneurs, free from the drudgery of traditional jobs, while making extra cash in their free time. Indeed, workers in the gig economy are often manipulated into working long hours for low wages and continually chasing the next gig, while companies exploit the many loopholes in the tax and labor laws. The surge of the digital economy has led to a new feudalism and those who own the platforms are the new vassals.
The gig economy with its new landlords is reaching in to all other industries, and localization is no exception. Digital platforms are disrupting old-fashion markets by parceling out jobs in discrete tasks and matching customers and workers, with pay being determined by demand only. From the customer’s perspective, disintermediation is the answer to their quest for convenience and for cutting out the additional costs charged by intermediaries. Parcellation of jobs has been happening for a few years now in the localization industry. A major difference with the companies of the platform economy is the use of platforms.
The wild side of the sharing economy and the gig economy is that convenience and affordability also come at a price, usually from eluding taxes and laws, thus, eventually, damaging the society. Also, the sharing economy has created a new monstrous type of customer who expects the service level of the Ritz Carlton at McDonald’s prices. And what about the promise of the sharing economy of freedom and additional substantial income? It couldn’t be farther from the truth. In fact, the growth of the sharing economy presents an economic paradox: Productivity is rising, while median income is flatting out. Finally, the on-demand economy was supposed to unleash innovation. Can you see any real innovation coming? Or only a typical Schumpeterian “creative destruction”?
The future is not what it used to be. With computers performing already 99% of translation jobs, a totally new approach should be devised to curb threats and take advantage of any opportunities brought by innovations. Some questions arise then that should be answered however challenging: Will the translation industry survive? How long? What will the translation business look like in five years? Is a career in translation still advisable? What are the options and the strengths to explore? What are the threats and the weaknesses?
Some people claim that the demand for translation is growing and that it will keep growing in the coming years, but the measurement approach followed so far is questionable. As a matter of fact, any growth in revenues may correspond to a growth in volumes, but it may also hide a stagnation if not really a decline in prices, and, possibly, in profits. Looking at production life cycle stages, translation revenues might already have peaked, while profits have possibly been decreasing for a few years now. This would explain the revival of the M&A frenzy: Organic growth is getting harder and harder, more and more investments are required to keep businesses profitable, and consolidation is the easiest way to grow and the most profitable exit strategy.
In five years, the platform war will be over and a bunch of wealthy few will most probably rule the business world. However young, the translation industry is fast drawing near the end of a cycle, and desperately needs to be renovated. Especially in the last few years, translation industry players have been desperately struggling to meet the demands of translation buyers craving to process ever-growing content volumes into more language pairs. Unfortunately, talents don’t combine with the abundance of tools, technology, and data because a varied bouquet of skills is increasingly required, while education initiatives are dramatically lagging. And while MT will keep proliferating, the shortage of talents will be ever more serious. In fact, the emphasis on language knowledge is still overstated when expectations are growing every day that Internet giants are going to solve the pesky language problem once and for all, without any intricacies and possibly at almost no cost. LSPs should then be utterly concerned about the sustainability of their business models. Scrambling for scale might not be enough even for the largest providers: Translation will still be here in five years, it will be here also in twenty years, but the translation industry may not.
Translation education still looks less demanding, thus faster, than scientific and technical education. The lower return is perceived as the result of high costs rather than of low benefits. Yet, however friendly technology may look today, skills other than languages are more and more needed to cope with the growing complexity of the business world. In this respect, with the almost total absence of any real specialization from translation education, newbies and even practitioners will be needing intense continuous training all the time more to specialize and try to keep up with the growing expectations. Unfortunately, with LSPs struggling to keep profiting despite obsolete, inefficient, and costly processes while resisting their customers’ pressures on prices, pays will keep lowering, thus forcing the best resources out of business. At the same time, the harshness of the gig economy will force more and more people with technical and science skills look for additional incomes in translation. No specialization in medicine, biology, law, engineering, etc. would make a translator any better at translation than a physician, a biologist, an attorney, an engineer with the same language pair and the access to the same tools and resources.
Three areas should then be explored, technology, knowledge, and data. Machine translation is now a general-purpose technology and will be a game changer even more than it has been so far. Indeed, MT is going to be so pervasive as to be embedded practically in every tool and application. Don’t forget that the washing machine has changed the world more than the Internet, and yet many would hardly be able to tell how and how much. Knowledge will be as important as technology. Language is a technology too, but it is useless without the necessary ability to exploit it. Just like language, any other technology is no magic wand. Technology does not solve problems, people do with their practical intelligence. The same practical intelligence allows them to devise the processes that enable technology to maximize benefits and minimize risks. Finally, the human brain is still the most powerful processing tool when it comes to reasoning. And knowledge allows people to pick the best data to have machine make inferences and reliable predictions.
The major threat comes from the business model that is common to most translation business players. Not only is this model obsolete and largely wasteful, it is a major reason for disintermediation. And, in fact, industries remaining too long as such with large inefficiencies are ideal candidates for disruption. A major weakness comes from what is conversely often perceived and brandished as a weapon: Information asymmetry. Only distrust and discontent come from the imbalance in transactions due to the inability of buyers to assess the value of service before sale. Another significant weakness is the growing skill shortage. This is due to a killing combination of increasingly lower pays driving best resources out with inadequate educational programs producing poorly-skilled would-be translators. Finally, the constant tide of new entrants and substitutes will help further undifferentiation and minimize any network effect.
The many affordable technologies and the very low financial, commercial, and legal barriers will result in new entrants being more and more often outsiders. But raising barriers is not the solution.
Decreased transaction costs are expunging intermediaries from electronic value chains. This means that also a buyer-seller matching platform for translation could be hard to develop, setup and run profitably. A so-called marketplace is not enough. For real disintermediation, best-matching algorithms are required to shorten the traditional translation supply chain. However, project management can hardly be totally automated especially for large and complex jobs involving several language pairs. The same goes for vendor management. However, for small, single-pair jobs there will be more and more customers searching for translators through portals, willing to use them as virtual one-stop shops. Also, these customers will most probably be more and more expecting to have their content translated nearly for free if not for free. On the other hand, this is a typical sharing economy effect. Will you be still willing to fight for any customer and any job, even for those going for the cheapest price? There will be more and more of them even among the once premium customers in the legendary premium segment.
If your competitors are getting stronger and stronger and you are unable to outdo them, you might band together with them and possibly gain some advantage rather than just giving up.
In other words, you can embrace the sharing economy and try to replicate the success of the companies of the gig economy.
In this case, be ready to embrace Uber’s co-founder Travis Kalanick’s philosophy and get rid of the other dude, i.e. go for complete automation.
Be also aware that high-attrition rates may not be a feasible long-term strategy. Unfortunately, and yet unsurprisingly, when getting bigger and bigger, rather than investing more money and more ability in the employee experience, companies usually become worse places to work. And in this case, things can get very bad if the tide of side-giggers withdraws. Reputation is a unique asset, that is hard to gain and much too easy to lose, with both customers and vendors. So, the next time you find yourself thinking about cutting costs to raise profits or protect your margins, remember that someone might pay your savings and your reputation will eventually be affected.
There is no reason to fear disintermediation. Technology allows mindful players to develop and provide new service bouquets, but this requires a strong brand, the ability to differentiate from competitors, and deep diversification of services. Digital transformation is no child’s play, though, and every business has its own intricacies. When seeking business opportunities in foreign markets, companies are challenged with functions they are not expert of, and must adapt fast. Most of these companies embraced automation and digital transformation way earlier, but they still have issues in handling their digital content.
There are many good opportunities for LSPs there, provided they can re-shape their business models and start adding real value. In times of industry 4.0 companies are no longer willing to partner with old-fashion organizations with virtually no real tech savvy. Mindful LSPs may start by refining their service offering by including consulting services in their bouquets for companies that are trying to do business abroad.
In this respect, the approach to technology should go well beyond CAT, TMS and MT and extend to modern content processing technologies and techniques like machine learning, AI and natural language processing, to make content more useful to humans and computers.
To this end, LSPs should turn their data into assets and make the most of it. Machine learning algorithms are rapidly becoming a commodity, and the cost of even the most advanced of them will soon plummet. The value will not be in algorithms, then, but in data, that is indeed the oil of the digital era.
As a first step, start measuring. Through measurement you’ll know more, reduce uncertainty, and thus risks. Anyway, for correct measuring you must perfectly know your data, master metrics, identify key measurements and the right tools to use, and, above all, develop and continuously refine your methods of measurement. Once you have made your measurements and collected the results, convey this information to customers so that they can positively correlate it with your capabilities.
“Lunch atop a skyscraper” is a very famous picture, but few probably know its title, history, and, above all, who shot it. Even fewer would know who shot the “shooter”. This is the kind of knowledge that might be considered specialized and yet it’s available to all, but you must have the practical intelligence to acquire it. Today a computer system can play chess or drive a car, but still no chess-playing computer can also drive a car. Machines may perform specific tasks, but they lack understanding of the world—sentience—and cannot transfer knowledge laterally between domains. Translation will be more and more an engineering thing, but machines will remain dependent on humans for building their “knowledge” from training data for the foreseeable future.
The future has already begun, tempus fugit, time is running out and it is always less. So, if the question is when, the answer is now, hic et nunc, before it’s too late.
Hic et Nunc
What will the future look like in a global gig