Observations on the Translation Industry

This is a guest post by a frequent contributor on this blog: Luigi Muzii. Here he shares observations on some key trends in the professional translation industry. His observations are presented as pieces of a jigsaw puzzle and readers can connect them or not as they wish. His opinions are his own, but I like to include them on this platform as they often ring true and show a keener sense of observation than we typically find in the localization media.

 He and I have both been saying for many years that disintermediation and disruption are coming to the industry, but we have yet to see a real fundamental change in the way things are done. This may be because the industry is highly fragmented and the inertia requires much more force to enable the needed structural change. There has been some change, but it has been slow and incremental. Or, quite possibly it may simply be that we are both wrong on this prediction of inevitable disruption.

After considering his observations here again, I think that it is perhaps, that the timing is hard to predict. MT has taken over a decade to even moderately penetrate the industry, and it is my opinion that it is still most often sub-optimally or wrongly used in the localization world. For real disintermediation to take place tools, processes, and solutions all have to evolve and align together in a meaningful way.  

Luigi often points to the practice of emphasizing the wrong aspects of the business challenges in the industry in many of his observations. This little clip makes this clear for those who still find his observations somewhat opaque.

“The reason why it is so difficult for existing firms to capitalize on disruptive innovations is that their processes and their business model that make them good at the existing business actually make them bad at competing for the disruption.”

'Disruption' is, at its core, a really powerful idea. Everyone hijacks the idea to do whatever they want now. It's the same way people hijacked the word 'paradigm' to justify lame things they're trying to sell to mankind."

'Disruption' is, at its core, a really powerful idea. Everyone hijacks the idea to do whatever they want now. It's the same way people hijacked the word 'paradigm' to justify lame things they're trying to sell to mankind.
Read more at https://www.brainyquote.com/topics/disruption-quotes

'Disruption' is, at its core, a really powerful idea. Everyone hijacks the idea to do whatever they want now. It's the same way people hijacked the word 'paradigm' to justify lame things they're trying to sell to mankind.
Read more at https://www.brainyquote.com/topics/disruption-quotes

Clay Christensen

“Life’s too short to build something nobody wants.”

Ash Maurya

“If you always do what you always did, you will always get what you always got.”

Albert Einstein

In the last week or so, there has been much clamor about the "magical" and "astounding" GPT-3 capabilities that can "create" and generate text by drawing from a HUGE language model. More data equals better AI, right? They say that GPT-3 is different because it creates. GPT-3 is a text-generation API. You give it a topic, and it spits back a (hopefully) coherent passage. It learns over time, tracking not just what it thinks your topic is about, but how you talk about that topic. 

Some of the examples of GPT-3 intelligence being shared in the Twitterverse are truly remarkable, but while I am indeed impressed, I think we should also maintain some skepticism about this "breakthrough" until we better understand the limitations. I will not be surprised to see overenthusiastic feedback from the LSP industry just as we saw with NMT. This thread has some interesting discussion and varied viewpoints on GPT-3.   

My initial impression is that is indeed a great leap forward, but it has two very serious flaws that come immediately to mind:

1. It lacks common sense as does all deep learning based AI that I have seen,
   
2. It is unable to admit that it does not know.
   

However, GPT-3 already appears to have the potential to displace mediocre marketing content producers, just as MT displaced some mediocre or bad translators. As more competent people test it and play with it, we will uncover the problems it is best suited to address. I look forward to hearing more about the production use of the technology and real use cases.

The difference between stupidity and genius is that genius has its limits. 

Albert Einstein

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A Jigsaw Puzzle

Over the last week or two, several topics have jumbled together in my mind. While they may seem unconnected, I do see a thread that binds them together. Commenting on each of these subjects separately would have meant breaking that thread, so they are presented together here as jigsaw tiles, that the reader may wish to combine to build an overall picture.

Ethnocentrism

Ethnocentrism is the original sin of globalization and one of the capital sins of internationalization. Most often, incorrect localization is like the fruit of the poisonous tree.

Writing full strings with as few variables as possible should be the most basic lesson in a Software Internationalization 101 course.

Context helps, syntactic gimmicks don’t.

Using an active voice is always better than using a passive one.

Gender issues should be left to localizers. Paying too much attention to use gender-neutral forms and words from strings (and content in general) won’t help translators do their job. On the contrary, they will make it harder, forcing translators to develop solutions that hardly sound as natural as the neutral English source material does. These translator modifications are not necessarily as neutral in another language, especially when an ending vowel can make a difference

Beyond being a silly stereotype, “thinking outside the binary box” to prevent using gendered language does not necessarily lead to effective communication.

Removing pork or cow meat from menus will not help per se increasing restaurant sales in Muslim or Hindu countries. However, redesigning the menu probably will. And this is a fundamental lesson in globalization 101.

All this reminds me of the launch of Windows 95 when you consider the initial localization attempts of the “Start” button and the sudden abandonment of the “Start Me Up” guitar chord as the accompanying jingle, which of course, makes much less sense in non-Anglo cultures.

Ethnocentrism could appear even in a theoretically unbiased approach to writing. Being a linguist does not necessarily mean that one is also open, inclusive, and global. The editor of a historically-popular trade magazine, who was also a translator, was also a prominent figure in the formation of the not so inclusive UKIP.

Inclusive language is something localizers and translators need no specific guide for. Sexist, racist, or otherwise biased, prejudiced language and ideas cannot be prevented from spreading, and translators have to deal with this daily. And they know how to cope with this phenomenon. Most importantly, they know how not to be influenced by this in doing their job. It’s called ethics.

It is wise, though, to request that vendors notify customers whenever they find language that isn’t inclusive, at least when inclusiveness is a pre-requisite. A customer’s task requirements guidelines should clarify whether a translator should keep the non-inclusive language intact — requirement specifications: such strange stuff.

Guidelines on using inclusive language may be useful for authors when machine translation is going to be involved. Much too often, people prefer to ignore that bias in AI and MT doesn’t come from algorithms, but from the people who developed the technology, and it reflects their values. Biased preference comes from training data even more than from input data. Training data are examples from which computers learn patterns and build predictive models. And this historical data is usually coming from real examples of human/social attitudes in the past.

Pandemic Crisis ‘Secondary’ Effects

The effects of the ongoing pandemic may have different readings — some of these readings concerns the broadening of the gig economy.

According to recent reports, the gig economy is taking over the enterprise. That more employees opt for a more flexible work structure may be one reading. Another one is that it is invaluable for organizations seeking to streamline and reduce costs.

Gig jobs are no longer limited to lower-paying work performed on-demand, and it seems that organizations have started taking advantage of more valuable employees. Gig jobs in the white-collar world has significantly increased in the past few years. 72 percent of all gig jobs worldwide between 2018 and 2019 were in large enterprise and professional services firms, and, according to Deloitte, gig workers in the US are going to triple to 42 million workers in 2020.

Quoting Gigster’s CEO Chris Keene, “Companies have always valued the ability to increase capacity without increasing costs.”

The impact of the gig economy on professionals that very few seem to see is that it exploits the demand for jobs to push remuneration lower and lower. No one pays attention to building a meritocracy: performance ratings and rankings are just truncheons.

What remains of the gig economy is a blessing for post-pandemic corporate recovery who can avoid hiring back thousands of full-time employees laid off or furloughed. Quoting Chris Keene again, “Coming out of this pandemic, there are a lot of jobs that people are not going to be able to come back to.” The pandemic crisis has had the gig economy jump a decade forward and pushed capitalism and its mission to a peak, i.e., increase profits and reduce costs to the maximum possible level.

This cost reduction focus is an unrelenting mission, as recent German slaughterhouse outbreak cases of the coronavirus showed. The specific impact of the cost-reduction focus, in this case, was to force close contact amongst workers in feverish working conditions needed to produce cheap meat. Of course, reports showed that otherwise despised migrants provided almost all the cheap labor. The German NGG union spoke of “shameful and inhumane conditions.”

The usual justification is that better working conditions involve higher prices. But are low prices really low? Higher prices always hide behind low prices.

Mainstream

Now that machine translation is finally mainstream [in the translation services industry,] nobody questions its use anymore. But still, the debate around MT use has taken on the same quagmire issues as those around localization translation in general. This means that, as Kirti Vashee, wittily notes, “the quality discussion remains muddy.”

Translation industry attention focuses mostly on edit distance, post-editing effort assessment, post-editing practices, and overall effectiveness measurement. Not surprisingly, discussions focus primarily, if not exclusively, on assessing the quality of machine translation output rather than on how to improve overall MT system capabilities, and shoddy tools like DQF receive all too much consideration.

Indeed, data and its understanding draw little or no interest, despite the clear enterprise market interest in an MT offering. This lack of focus is due not only to the fact that the LSP MT offering is not transparent, is unconvincing, and often poorly focused. Despite the interest of enterprise customers in MT, the relatively good performance of (almost) free online MT engines create a disincentive for LSPs to invest. LSPs are reluctant to explore a territory that seems outside their traditional scope of business and expertise.

Helping machine translation systems handle inclusive language is not just a matter of focus on training data, just as producing good content downstream is not just a matter of effective post-editing practices.

Preemptive quality assessment (or a priori risk assessment, as some call it) is only as effective as the training data is useful. Also, error detection and correction capabilities are crucial, at least as long as quality assessment still heavily depend on inspections.

Information asymmetry also applies to machine translation. Estimating risk only for the output without taking into account the source data, process conditions (especially buyer requirements), and the expected results do not raise high hopes per se. If you are unable to measure these three parameters according to consistent and parallel metrics and produce a weighted mean, you will face misleading estimates. Last but not least, insistence on segment-based rather than document-based analysis will not get you out of the narrow enclave in which the translation community has been basking for centuries.

Disintermediation Is Not A Vending Machine

And no ATM either.

At the WWDC 2020, Apple revealed that version 14 of iOS would come with a translation app specifically designed to translate conversations in 11 languages. An on-device mode will also be available to allow offline translations.

Should this be interpreted as another sign of the imminent end of the translation industry? The industry is most probably doomed, but its end is not set to come tomorrow.

The end of the industry will come from disintermediation. Some, including “yours truly,” have been writing (and talking) about this happening for a decade. Others are speaking more quietly about this more recently. More precisely, the usual suspects made some enthusiastic, although scanty, comments when Lionbridge launched its BPaaS platform, onDemand, five years ago or so.

Recently, though somewhat belatedly, SDL has struck back with its self-service, on-demand platform, SLATE.

Disintermediation is almost inexorable in the evolution of the global (digital) village, where intermediaries are generally seen as the villain. However, they are everywhere online, despite the common belief that they are not (e.g., Airbnb, Amazon, Booking.com, eBay, Expedia, Instacart, Uber, the food delivery companies, the app stores, just to name a few). Following the typical marketing model of rechristening old things by giving them glamorous or more palatable names, they are simply renamed as two-sided markets.

Incidentally, Lionbridge’s OnDemand was quickly, abruptly and mysteriously discontinued despite its boasted growth of 68 percent in one year with reportedly impressive scores of 99.8 percent on-time delivery rate, 99.4 percent revision-free project rate, and 99 percent of users satisfied or very satisfied (85 percent) with their customer care.

Lionbridge onDemand’s should have turned language services into items that could be bought through an e-catalog via a procure-to-pay system.

This “productization” approach involved standardizing options and making pricing instant. The idea behind it was to entice business stakeholders with 24/7 access, faster turnaround times and lower prices, while providing higher visibility into total-cost-per-output and rate-card negotiations, thus curbing the vendors’ role and their ability to add fees and lengthen lead times.

The pricing model was the traditional word rate model, while for its self-service platform, SDL offers a subscription model (SLA anyone?).

Today’s fundamental question is the same as then: Who and what are these platforms for?

As Semir Mehadžić brilliantly noted, beyond the aim of ‘cutting out the middleman’ childishly coming from typical middlemen, a BPaaS should come up with a better value proposition than the one currently used, i.e., “fewer clicks” and “avoiding the use of Google Translate.”

In the projected perspective, self-service translation platforms may entice consumers, but hardly any businesses.

The businesses such platforms can entice are typically new to translation and the translation industry, usually, companies entering international markets for the first time. Such companies generally go along a long and painful track of word of mouth and web search to find a vendor that suits their needs. Then inquiries and quotes follow, and leave the business managers puzzled and hesitant with their heads spinning and aching. The many quotes collected differ substantially from one another, and all look invariably too costly, mainly because the service offered is essentially the same.

Therefore, if the ideal recipient for a self-service platform is the consumer (e.g., Translated.net, One-Hour Translations, Lingo24, Gengo, tolingo, etc.), SDL’s offering, with its SLA-like model, is aiming at SME’s while saving on sales and account management costs. Probably because SMEs would typically not approach a large LSP since they presume that they would not find the same responsiveness, flexibility, and speed.

And this only happens if everything goes well because those SME managers described above might easily bump into an LSP salesperson who tries to educate the prospective customer about the intrinsic value of translation and the wonders of CATs and TMSs. Unfortunately, there is no inherent value in service offerings, only a perceived one, and while the prospect customer knows this, maybe the salesman does not. And the selling effort is thus burnt.

Therefore, self-service translation platforms might target the consumer market, where SMEs with occasional translation jobs can also be found. However, to reach the consumer market, substantial investments are required to be always on top of SERPs and get the necessary conversions. The future effect of these platforms may thus further accelerate commoditization of translation service businesses.

This unintended impact should be feared by self-service translation platforms in particular, as it would require that they will need to sell more and more translations just to stay even in revenue terms. The situation is similar to the dilemma of vending machine suppliers. They need to continuously sell more and more vending machines and find cheaper and cheaper products to include in them.

Anyway, all of these DIY instant translation platforms look like their designers know little about how the need for translation arises in business, and how translations are performed and delivered. More importantly, they look as if they don’t know - or even care - about customer satisfaction and how this is expressed and assessed.

It is my observation, that these allegedly “new offerings” are usually just a response to the same offering from competitors. They should not be equated to disintermediation and they often backfire, both in terms of business impact and brand image deterioration. They all seem to look like dubious, unsound initiatives instigated by Dilbert’s pointy-haired boss. And the Peter principle rules again here and should be considered together with Cipolla’s laws of stupidity, which state that a stupid person is more dangerous than a pillager and often does more damage to the general welfare of others.

Luigi Muzii has been in the "translation business" since 1982 and has been a business consultant since 2002, in the translation and localization industry through his firm . He focuses on helping customers choose and implement best-suited technologies and redesign their business processes for the greatest effectiveness of translation and localization related work.

This link provides access to his other blog posts.

A Vision for Blockchain in the Translation Industry

Happy New Year

This is yet another post on Blockchain, a guest post by Robert Etches who presents his vision of what Blockchain might be able to be in the translation industry. A vision, by definition, implies a series of possibilities, and in this case, quite revolutionary possibilities, but does not necessarily provide all the details of how and what. Of course, the way ahead is full of challenges and obstacles which are much more visible to us than the promised land, but I think it is wise to keep an open mind and watch the evolution even if we are not fully engaged or committed or in agreement. Sometimes it is simply better to wait and see than to come to any final conclusions.

It is much easier to be dismissive and skeptical of upstart claims of fundamental change than to allow for a slim but real possibility that some new phenomenon could indeed be revolutionary. I wrote previously about CEO shortsightedness and what I called Roryisms. Here is a classic one from IBM that shows how they completely missed the boat because of their hubris and old style thinking. Gerstner is however credited with turning around a flailing mainframe business.The cost of missing the boat can be significant for some, and we need only look at relative stock price and market value improvements over time (as this is how CEO performance is generally measured) to understand how truly clueless our boy Lou and his lieutenants at IBM, in general, were when they said this. The culture created by such a mindset can last decades as we see by the evidence. Culture is one of a company’s most powerful assets right until it isn’t: the same underlying assumptions that permit an organization to scale massively constrain the ability of that same organization to change direction. More distressingly, culture prevents organizations from even knowing they need to do so.

IBM’s chairman minimized how Amazon might transform retail and internet sales all the way back in 1999.

“Amazon.com is a very interesting retail concept, but wait till you see what Wal-Mart is gearing up to do,” said [IBM Chairman, Louis V. Gerstner Jr in 1999.]. Mr. Gerstner noted that last year IBM’s Internet sales were five times greater than Amazon’s. Mr. Gerstner boasted that IBM “is already generating more revenue, and certainly more profit, than all of the top Internet companies combined.”

AMZN Stock Price Appreciation of 36,921% versus IBMs 211% over 20 years

 IBM is the flat red line in the chart above. IBM looks just as bad against Microsoft, Google, Apple, Oracle, and many others who had actual innovation.

January 11, 2019
Amazon Market Value	$802 Billion	7.3X Higher
IBM Market Value	$110 Billion

I bring attention to this, because, I also saw this week that IBM has filed more patents than any other company in the US in 2018, Samsung was second. In fact, IBM has been the top patent filer in the US for every year from 1996 to 2018. BTW they are leaders in blockchain patents as well. However, when was the last time that ANYBODY has associated IBM with innovation or technology leadership? 1980? Maybe they just have some great patent filing lawyers who understand the PTO bureaucracy and know how to get their filings pushed through. In fact, there have been some in the AI community who felt that IBM Watson was a joke and that the effort did not warrant serious credibility and respect. Oren Etzioni said this: “IBM Watson is the Donald Trump of the AI industry—outlandish claims that aren’t backed by credible data.” Trump is now a synonym for undeserved self-congratulation, fraud, and buffoonery, a symbol for marketing with false facts. IBM is also credited with refining and using something called FUD (fear, uncertainty, and doubt) as a deliberate sales and marketing misinformation tactic to keep customers from using better, more innovative but lesser-known products. We should not expect IBM to produce any breakthrough innovation in the emerging AI-first, machine learning everywhere world we see today, and most expect the company will be further marginalized in spite of all the patent filings. 

Some of you may know that IBM filed the original patents for Statistical Machine Translation, but it took Language Weaver (SDL), Google and Microsoft to really make it come to life in a useful way. IBM researchers were also largely responsible for conceiving of the BLEU score to measure MT output quality that was quite useful for SMT. However, the world has changed and BLEU is not useful with NMT. I plan to write more this year on how BLEU and all its offshoots are inadequate and often misleading in providing an accurate sense of the quality of any Neural MT system.

It is important to be realistic without denying the promise as we have seen the infamous CEOs do. Change can take time and sometimes it needs much more infrastructure than we initially imagine. McKinsey (smart people who also have an Enron and mortgage securitization promoter legacy) have also just published an opinion on this undelivered potential, which can be summarized as:

 "Conceptually, blockchain has the potential to revolutionize business processes in industries from banking and insurance to shipping and healthcare. Still, the technology has not yet seen a significant application at scale, and it faces structural challenges, including resolving the innovator’s dilemma. Some industries are already downgrading their expectations (vendors have a role to play there), and we expect further “doses of realism” as experimentation continues." 

 While I do indeed have serious doubts about the deployment of blockchain in the translation industry anytime soon, I do feel that if it happens it will be driven by dreamers, rather than by process crippled NIH pragmatists like Lou Gerstner and Rory. These men missed the obvious because they were so sure they knew all there was to know and because they were stuck in the old way of doing things.  While there is much about blockchain that is messy and convoluted, these are early days yet and the best is yet to come.

Another dreamer, Chris Dixon has an even greater vision on Blockchain when he recently said:

The idea that an internet service could have an associated coin or token may be a novel concept, but the blockchain and cryptocurrencies can do for cloud-based services what open source did for software. It took twenty years for open source software to supplant proprietary software, and it could take just as long for open services to supplant proprietary services. But the benefits of such a shift will be immense. Instead of placing our trust in corporations, we can place our trust in community-owned and -operated software, transforming the internet’s governing principle from “don’t be evil” back to “can’t be evil.”

========

2018 was a kick-off year for language blockchain enthusiasts. At least five projects were launched[1], there was genuine interest expressed by the industry media, and two webinars and one conference provided a stage for discussion on the subject[2]. Then it all went very quiet. So, what’s happened since? And where are we today?

Subscribers to Slator’s latest megatrends[3] can read that it’s same same in the language game for 2019: NMT, M&A, CAT, TMS, unit rates … how we love those acronyms!

On the world stage, people could only shake their heads in disbelief regarding the meteoritic rise of the value of cryptocurrencies in 2017. However, in 2018 those same people relished a healthy dish of schadenfreude as exchange rates plummeted and the old order was restored with dollars (Trump), roubles (Putin), and pound sterling (Brexit) back in vogue.

In other words, for the language industry and indeed for the world at large, “better the devil(s) we know” appears to be the order of the day.

There is nothing surprising in this. Despite all the “out of your comfort zone” pep talks by those Cassandras of Change[4], the language industry continues to respect the status quo, grow and make money[5]. Why alter a winning formula? And certainly, why even consider introducing a business model that expects translators to work for tokens?! Hello, crazy people!!!

But maybe, just maybe, there was method in Hamlet’s madness[6] and Apple was right when they praised the crazy ones[7]?

Let’s take a closer look at the wonderful world of blockchain and token economics, and how they are going to change the language industry … also the language industry.

 

Pinning down the goal posts

Because they keep moving! Don’t take my word for it. Here’s what those respected people at Gartner wrote in their blockchain-based transformation report[8] in March 2018:

Summary

While blockchain holds long-term promise in transforming business and society, there is little evidence in short-term reality.

 

Opportunities and Challenges

• Blockchain technologies offer new ways to exchange value, represent digital assets and implement trust mechanisms, but successful enterprise production examples remain rare.
• Technology leaders are intrigued by the capabilities of blockchain, but they are unclear exactly where business value can be achieved in the enterprise context.
• Most enterprise blockchain experiments are an attempt to improve today's business process, but in most of those cases, blockchain is no better than proven enterprise technologies. These centralized renovations distract enterprises from other innovative possibilities offered by blockchain.

And now here’s a second overview, also from Gartner, this time their blockchain spectrum report[9] from October 2018:

 

Opportunities and Challenges

• Blockchain technologies offer capabilities that range from incremental improvements to operational models to radical alterations to business models.
• The impact of blockchain’s trust mechanisms and interaction paradigms extends beyond today’s business and will affect the economy, society and governance.
• Many interpretations of blockchain today suffer from an incomplete understanding of its capabilities or assume a narrow scope.

The seven-month leap from little evidence in short-term reality to will affect the economy, society and governance is akin to a rocket-propelled trip across the Grand Canyon! Little wonder that traditional businesses don’t know where to start even looking into this phenomenon, never mind taking on a new business model that basically requires emptying the building of 90% of hardware, software and, more important, people.

But!

Why does Deloitte have 250 people working in their distributed ledger laboratories? Because when immutable distributed ledgers become a reality they will put 300,000 people out of work at the big four accountancy companies[10].

Why are at least 26 central banks looking into blockchain? Because there’s a good chance that private banks[11] will be superfluous in 10-15 years’ time and we will all have accounts with central banks.

Or there will be no banks at all …

Let’s just take a second look at that Gartner statement:

The impact of blockchain’s trust mechanisms and interaction paradigms extends beyond today’s business and will affect the economy, society and governance.

Other than basically saying blockchain will change “everything”, the sentence mentions two factors that are core to blockchain: trust and interaction.

Trust. What inspires me about blockchain is its transparency. A central tenet of blockchain is its truth gene. In a world in which even the most reliable sources of information are labeled as fake, blockchain’s traceability – its judgment in stone as to who did what, when and for whom – makes it a beacon of light.

Just think if we could utilize this capability to solve the endless quality issue? What if the client always knew who has translated what – and could even set up selection criteria based upon irrefutable proof of quality from previous assignments? It is no surprise to learn that many blockchain projects are focusing on supply chain management.

Interaction is all about peer-to-peer transactions through Ethereum smart contracts. It’s not just the central banks that will be removing the middlemen. Unequivocal trust opens the door to interact with anyone, anywhere. To a global community. These people of course speak and write in one or more of approximately 6,900 languages, so there’s a market for providing the ability for these “anyones” to speak to each other in any language. What a business opportunity! And what a wonderful world it would be!

Cryptocurrencies and blockchain: peas and carrots

You’ve gotta love Forest Gump – especially now we know Jenny grew up to become Claire Underwood[12] 😊

Just as Jenny and Forest went together like peas and carrots, so do tokens and blockchain.

Unfortunately, this is where many jump off the train. One thing is accepting the relevance of some weird ledger technology that is tipped to become the new infrastructure for the Internet, another is trading in hard-won Venezuelan dollars for some sort of virtual mumbo jumbo!

1. All fiat currencies are a matter of trust. None is backed by anything more than our trust in a national government behaving responsibly. In 2019 that is quite a scary thought – choose your own example of lemming-like politicians.
2. All currencies (fiat or crypto) are worth what the market believes them to be worth. In ancient times a fancy shell from some far-off palmy beach was highly valued in the cold Viking north. Today not so. At its inception, bitcoin was worth nothing. Zero. Zip. Today[13] 1 BTC = €3,508.74. Because people say so.

Today, there is absolutely no reason why a currency cannot be minted by, well, anyone. There is indeed a school of thought that believes there will be thousands of cryptocurrencies in the non-too distant future. If we look at our own industry, we have long claimed that translation memories and termbases have a value. Why can that value not be measured in a currency unique to us and with an intrinsic value that we all respect and which is not subject to the whims of short-term political aspirations? Why can’t linguistic assets be priced in a Language Coin?

Much has already been written about the concept of a token economy, though little better than the following:

An effective token strategy is one where the exchange of a token within a particular economy impacts human economic behavior by aligning user incentives with those of the wider Community.[14]

Think about this in the context of the language industry. What if the creation and QA of linguistic assets were tied to their own token? What if you – a private company, a linguist, an LSP, an NGO, an intranational organization – were paid in this token for your data and that the value of this data grew and grew year on year as it was shared and leveraged as part of a larger whole – the Language Community[15]. What if linguists were judged by their peers and their reputations were set in stone? What if everyone was free to charge whatever hourly fees they choose, and that word rates and CAT discounts were a relic of the past?

This is why blockchain feeds the token economy and why the token needs blockchain. Peas and carrots!

To end with the words of another Cassandra – a trendy one at that: Max Tegmark:
If you hear a scenario about the world in 2050 and it sounds like science fiction, it is probably wrong; but if you hear a scenario about the world in 2050 and it does not sound like science fiction, it is certainly wrong.[16]

The pace of change will continue to accelerate exponentially, and I believe blockchain will be one of the main drivers.

Already in 10-15 years, there will be some household (corporate) names and technologies that do not exist or have only just started today. And by 2050 the entire finance, food, and transport sectors (to name the obvious) will be ‘blockchained’ beyond recognition.

At Exfluency, we see multilingual communication as an obvious area where a token economy and blockchain will also come out on top; I’m sure that other entrepreneurs in a myriad of other sectors are coming to similar conclusions. It’s going to be exciting!

Robert Etches
CEO, Exfluency
January 2019


[1] Exfluency, LIC, OHT, TAIA, TranslateMe
[2] TAUS Vancouver, and GALA and TAUS webinars
[3] https://slator.com/features/reader-polls-pay-by-hour-deepl-conferences-and-2019-megatrends/
[4] Britta Aagaard & Robert Etches, This changes everything, GALA Sevilla 2015; Katerina Pastra
Krzystof Zdanowski, Yannis Evangelou & Robert Etches, Innovation workshop, NTIF 2017; Peggy Peng & Robert Etches, The Blockchain Conversation, TAUS 2018, Jochen Hummel, Sunsetting CAT, NTIF 2018.
[5] I am painfully aware that not everyone in the food chain is making money …
[6] Hamlet, II.ii.202-203
[7] https://www.youtube.com/watch?v=8rwsuXHA7RA
[8] https://www.gartner.com/doc/3869696/blockchainbased-transformation-gartner-trend-insight

[9] https://www.gartner.com/doc/3891399/blockchain-technology-spectrum-gartner-theme
[10] P.221 The Truth Machine, by Paul Vigna and Michael J. Casey
[11] Ibid. pp163-167

[12] https://en.wikipedia.org/wiki/Robin_Wright
[13] 9 January 2019
[14] P.69 The Truth Machine
[15] See Aagaard & Etches This changes everything for the sociological and economic importance of communities, the circular society, and the sharing society.
[16] Life 3.0 by Max Tegmark

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CEO, Exfluency

A dynamic actor in the language industry for 30 years, Robert Etches has worked with every aspect of it, achieving notable success as CIO at TextMinded from 2012 to 2017. Always active in the community, Robert was a co-founder of the Word Management Group, the TextMinded Group, and the Nordic CAT Group. He served four years on the board of GALA (two as chairman) and four on the board of LT-Innovate. In a fast-changing world, Robert believes there has never been a greater need to implement innovative initiatives. This has naturally led to his involvement in his most innovative challenge to date: As CEO of Exfluency, he is responsible for combining blockchain and language technology to create a linguistic ledger capable of generating new opportunities for freelancers, LSPs, corporations, NGOs and intergovernmental institutions alike.

The Future of Translation in a Gig Economy

 This is a guest post by Luigi Muzii based on a presentation he made recently on the coming industry disruption. Digital disruption is all around us today, and one characteristic of how it manifests is how it sneaks up and is in control before the incumbents even realize what has happened. Sears and other retailers barely saw Amazon coming, taxi companies did not see Uber coming, the hotel industry was caught unaware by Airbnb and so it goes.  

A recent Cisco/IDC suggests that 40% of all companies today will be affected (disappear) by the digital disruption. 

 It is quite possible that a new player could emerge in the translation industry that produces a platform that allows buyers and sellers to congregate and conduct efficient transactions with minimal broker (LSP) support (or just platform support). The translation industry is one that still struggles to talk about quality in a way that is clear and meaningful to customers. A platform that provides CAT tools, properly integrated MT, and a straightforward means to discuss quality and the deliverable, could be a force of disintermediation. Several have tried and failed recently but the platform technology is getting better all the time. It could happen. Soon. There are too many people involved in performing repetitive, mundane tasks in a translation project, and some say it is ripe for change. 

• Platforms ensure consistency, quality, and a good customer experience through the whole buyer journey
• Platforms enable new people to enter the marketplace, both buyers, and sellers and often expand the traditional view of the market place
• Platforms are especially powerful means to create transformation around service offerings 
• TM and MT are not disruptive in themselves, but properly organized into intelligent  AI workflow solutions they can indeed be used to deliver disruptive services
• Mostly, platforms deliver new, no-hassle, customer experiences to industries where people have gotten used to less than satisfactory CX.

 This article suggests that getting legal advice for "standard" legal work is a service that is ripe for disruption. If this is indeed true, then how long before "standard" translation work will also follow?

 Don't be surprised if soon you might be able to ask a chatbot for specific legal advice.

 

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Preparing for disintermediation: Or what will the future look like in a global gig economy?

The following are a few basic questions about the gig economy using the classic “Five Ws (and One H)” rule of rhetoric:

1. What is the gig economy?
2. Who benefits from it?
3. Where does it apply?
4. When is it going to prevail?
5. Why is the translation industry affected?
6. How is disintermediation relevant?

The answers to these questions raise a few more ones that will hopefully be given a tentative answer.
Let’s start with a brief recap first.

Hic et Nunc from Luigi Muzii

The translation industry

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.

Industry 4.0 & Translation

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 translation industry fit “4.0”? With some effort and a little imagination, the translation industry could be halfway between “2.0” and “3.0.”

The Gig economy

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 effects of 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.

Where does the gig economy apply?

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.

When does the gig economy prevail?

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.

Why is the translation industry affected?

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 evasion.

Who benefits from the gig economy?

The promises about 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.

How is disintermediation relevant?

The gig economy with its new landlords is reaching into 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 great decoupling

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

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?

How long will the translation industry survive?

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.

What will translation look like in five years?

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 talent will be much 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.

Is a career in translation still advisable?

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 to 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.

What are the options and the strengths to explore?

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 the machine make inferences and reliable predictions.

What are the threats and the weaknesses?

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.

New entrants

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.

On the eve of disruption

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.

So what?

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.

Side with evil

In other words, you can embrace the sharing economy and try to replicate the success of the companies in the gig economy.

The other dude

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.

Reputation

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 spoil, 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.

Re-intermediation

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 at 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.

Focus on the "S" in LSP

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-fashioned 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.

Exploit technology

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.

Turn data into assets

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.

Measure

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.

Knowledge

“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 Go or drive a car, but still, no Go-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.

Embrace the future

The future has already begun, tempus fugit, time is running out and it is always less than you expected. So, if the question is when, the answer is now, hic et nunc, before it’s too late.

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Luigi Muzii has been in the "translation business" since 1982 and has been a business consultant since 2002, in the translation and localization industry through his firm. He focuses on helping customers choose and implement best-suited technologies and redesign their business processes for the greatest effectiveness of translation and localization-related work.

This link provides access to his other blog posts.