If we look beyond the metaverse – what other tech trends will affect our lives in one, or five years? We gathered some of them, with help from Schibsted News Media experts.

Techlash 2.0

In the last few years, we’ve seen more and more regulation hitting the tech giants and big corporations in the EU, the UK and the US. The regulations put in place by the EU are expected to eventually be copied by the US and the UK, and there will likely be more laws put in place to hold Big Tech accountable.

Bye, bye passwords!

We are moving from endless lists of passwords and password managers will soon be replaced by biometric passkeys. The FIDO authentication credential that provides “passwordless” sign-ins to online services. Already widely used by Apple, think fingerprint scanning and Face ID, it’s likely that more companies will adopt the technology for using users’ biometric data to create safer login processes, making password leaks a thing of the past.

Social shopping becomes mainstream

Shopping hauls and unboxings have been a social media tradition for years on Instagram and YouTube, but TikTok – and its Chinese counterpart Douyin – have taken the phenomenon into the mainstream. Businesses, from clothing brands to restaurants, are livestreaming to engage with viewers, and they are seeing increased revenues from the social shopping aspect.

Service fragmentation will grow

We’re already seeing the streaming world become severely fragmented, with new services announced all the time. Though the giants may still have the lead, the competition is growing fiercer and the consumer has more choices than ever. We will likely see these developments in other spaces as well, as social media is well on its way and new apps for podcasts are fighting for the users’ attention, too. As users become more interested in niche platforms and products, the fragmenting of our digital services will follow.

The war for tech talent

The war on talent isn’t news at this point, but tech talent is an especially sought-after commodity worldwide. New ways of working and the ability to demand more from employers will have tech workers picking and choosing, while the companies work to improve their offerings, whether at the office or remotely.

Our time is value

We’re seeing an increase in the fight for the users’ time, not necessarily their money. For publishers and social media, attention and usage are becoming far more important in the long run, as exemplified by Netflix’s choice to make a cheaper subscription tier that comes with advertising. Of course, this is not a new phenomenon in the publishing industry, where advertising-based revenue versus subscription-based revenue has been the question for decenniums. The fact that a user’s time is considered more valuable is becoming common knowledge, and we’ll likely see that mirrored in more companies’ business models in the future.

Your home will be even smarter

Apple’s development of Nearby Interaction will likely spur similar new features from other companies. Nearby Interactions allows Apple users to connect to other devices and accessories depending on their location. Recently announced Background Sessions would enable users to use their accessories hands-free. For example, you could set your music to turn on when you enter your home or a specific room, or you can trigger other actions on connected accessories. This type of technology will probably grow more popular soon, making your smart home even smarter.

Vertical video is winning

TikTok keeps winning ground over other social media platforms, and the rest are left scrambling to keep up. The vertical video format will likely keep gaining in popularity, whether in short- or long-form. And vertical video is expected to be used in other formats as well, with its potential to make news products stronger as publishers work to engage with the medium. Many social media-forward publishers already have large teams in place for Instagram and TikTok, and there is no question that others will follow suit.

It’s been a rough few years for a handful of US tech companies, due to a seemingly endless stream of scandals and harsh criticism from politicians on both sides of the Atlantic. The result? “Big Tech” is bigger than ever. But what if they have only started to flex their muscles?

Several executives reacted with shock, according to the people in the room. The proposal meant crossing a line, unleashing a hitherto unused weapon. The code name was “Project Amplify”, and it was a new strategy that social media behemoth Facebook hatched in a meeting in early 2021, as reported by the New York Times. The mission: to improve Facebook’s image by using the site’s News Feed-function to promote stories that put the company in a positive light.

The potential impact is enormous. News Feed is Facebook’s most valuable asset. It’s the algorithm that decides what is shown to users when they log in to the site. In essence, it is the “window to the world” for their users, who, totalling nearly three billion, constitute more than a third of all humans on planet Earth.

“Truth” is now the same as “what makes Facebook look good”

For many years Facebook founder Mark Zuckerberg defended the company’s policy on free speech with the mantra that the social network should not be the “arbiter of truth” online, i.e., they would not censor content that people posted. Critics would say that Facebook has been doing this all along, letting its algorithms prioritise what is presented to users. What shows up in the News Feed is what people perceive as important, a form of personal truth for every individual. “Project Amplify” would mean something entirely new. By actively promoting positive news stories about the company, “truth” is now the same as “what makes Facebook look good”.

Silicon Valley veteran and social media-critic Jaron Lanier referred to the major social media networks as “gigantic manipulation machines”, possessing the power to alter emotions and political views among billions of people by pulling digital levers. Now Facebook has decided to use its machine for its own purpose.

We will return to the implications of this, but first, it is important to understand why Facebook and Mark Zuckerberg would want to do this. It is no bold assertion to say that Facebook’s public image is in acute need of a facelift. The company has been plagued by scandals for years. In 2018, it was revealed that the company Cambridge Analytica had harvested data from 87 million Facebook users, data that had been used in Donald Trump’s presidential campaign. The revelation not only tarnished Facebook’s reputation, but it also had real financial consequences. When the story broke in March 2018, Facebook’s stock tanked. In July the same year, Facebook announced that growth had slowed down due to the scandal. The stock fell 20 percent in one day. In a few months, 200 billion USD of the company’s market capitalisation was wiped out.

Facebook’s reaction can be summarised as follows: we are sorry and promise to do better. This has been repeated every time new, negative stories about the company emerge, such as the spread of disinformation, the negative impact Facebook’s product has on the mental health of young people, and how the network was used to instigate genocide in Myanmar, among other things.

If the stock market is a reliable gauge of the future, and it often is, the conclusion is clear: these companies are untouchable

But behind the many apologies it seems Facebook has continued with business as usual. In September 2021, the Wall Street Journal published “The Facebook Files”, a damning investigation showing that the company, including Mark Zuckerberg, was very aware of the harm the platform was causing. The company’s own researchers identified problems in report after report, but the company chose not to fix them, despite public vows to do so.

From the company’s perspective, its strategy has been a success. Advertising revenues have continued to rise and in autumn 2021, Facebook’s stock market value broke one trillion USD, double of what it was before Cambridge Analytica.

The same can be said of the other tech giants. Companies including Google, Amazon and Apple have been at the crosshairs of public debate for years, both for alleged abuse of their dominant market positions and for the negative effect their products and business models can have on people and society.

But if the stock market is a reliable gauge of the future, and it often is, the conclusion is clear: these companies are untouchable. Despite a storm of criticism, court cases and billion-dollar fines, stocks have continued to propel ever upwards. How is this possible? Let’s start with breaking down the different ways Big Tech dominates the world today.

When discussing this topic, parallels are often drawn to the influential corporations of the late 1800s and early 1900s, Standard Oil for example. These comparisons are misleading. Standard Oil and its owner John D. Rockefeller could never dream of the amount of power that rests in the hands of the Silicon Valley-titans of the 2020s.

The new economic superpower

In 2010, the total market cap of Apple, Google, Microsoft, Facebook and Amazon was more than 700 billion USD. That was equivalent to the GDP of the Netherlands. The ascent had been amazingly fast; at this point Amazon was 16 years old, Google twelve and Facebook only six. In autumn 2021, their combined value had reached 9,500 billion USD, more than the GDP for Japan and Germany combined. The total annual revenue for these five corporations is north of one trillion dollars, more than the defence budgets of USA, China, and Russia combined.

The market superpowers

Facebook owns four of the five largest social media networks in the world. Google, owner of the second largest (Youtube), has a 92 percent market share on search. Apple’s and Google’s operating systems, IOS and Android, control 99 percent of the global smart phone market outside of China. Apple takes in 65 percent of the global revenue on mobile apps, and Amazon has 50 percent of the e-commerce market in the US, as well as 32 percent of the global market for cloud services, followed by Microsoft. The list goes on. This not only creates huge profits but also creates an enormous asset in form of the 21st century’s most valuable commodity: data.

The innovation superpowers

Up to 50 percent of the venture capital raised by start-ups circles back to Google and Facebook in the form of advertising, almost as a “tax on innovation”. If new, competing services emerge, Big Tech can either try to buy them or launch competing products. Their headway in terms of resources and user base makes it extremely difficult – if not impossible – to pose a real threat.

The perception superpowers

Twenty years after the 9/11 terrorist attacks, one in 16 Americans believe the US government knew about the attacks and let them happen. Conspiracy theories and disinformation have become the new normal, and research has shown social media plays an important role. What Google and Facebook choose to allow, or not allow, on their platforms shapes our view of the world. In 2012, Facebook conducted an experiment among 700,000 users to see if their states of mind could be altered by changes in News Feed. The answer was yes.

The infrastructure superpowers

In December 2020, Google went down, meaning users could not access Gmail, Google Docs or Youtube. Although the outage only lasted 45 minutes, it made headlines all over the globe. The same thing happened to Facebook in October 2021. As an expert said to CNN: “For many people Facebook is the same as internet”. After the 2008 financial crisis it was clear that a small number of banks were “systemically important”. This is now very true for Big Tech. Serious disruptions in their services would quickly have severe and costly consequences.

The political superpowers

Big Tech has surpassed Big Oil as the biggest spenders on lobbying in Washington D.C., with an increase in spending from 20 to 124 million USD between 2010 and 2020. In the election cycle of 2020, a total of 2.1 billion USD was spent on political ads on Facebook and Google. In a manifest published in 2017, Mark Zuckerberg noted that in elections across the world “the candidate with the largest and most engaged following on Facebook usually wins”. In other words: use us or you lose.

The capital markets superpowers

It could be argued that the stock market has become the most important gauge for global decision-makers. It takes decades to make them do anything to combat climate change, but if stocks drop dramatically, decisive action from politicians and central banks are delivered within days or even hours. This was last seen in early 2020, when fears of the economic impact of the pandemic brought the Dow Jones down by 13 percent in one day. The direction of the stock market is, in turn, more and more intertwined with that of Big Tech. Apple, Google, Facebook, Amazon and Microsoft constitute a quarter of the S&P 500 index.

The AI superpowers

“Dark patterns”. That is what scientists call the tricks that digital companies deploy to manipulate users. Sometimes the purpose can be quite trivial, like making people sign up for a newsletter or share their email. The point is that you as a user do something that you didn’t intend to do. With artificial intelligence these tools become more and more powerful and potentially deceptive. The more data an AI-algorithm can use to train on, the more effective it becomes. This places Big Tech in a unique position to use these techniques. The problem with this is best summed up by Meredith Whittaker, a former Google engineer and now head of the AI Now Institute at New York University: “You never see these companies picking ethics over revenue.”

In all the ways mentioned above, the power of Big Tech is growing bigger every day. It is important to say that not everybody thinks this is a problem. However, it seems like there is a consensus among democratically-elected leaders in both the U.S. and Europe that the influence of these companies must be reined in. The U.S. and the European Union recently agreed to take a more unified approach in regulating big technology firms. In fact, even the people who work in the industry share this view. In a survey of 1,578 tech employees made by Protocol, 78 percent said that Big Tech is too powerful.

So, what can be done? A variety of options are already on the table, from forcing companies to break up to altering laws that give social media companies a free pass compared to traditional media. If the New York Times publishes hate speech they are liable, when Facebook does the same, they are not. In the US, this legislation is referred to as “Section 230”, and there is a debate around whether to change it. At the same time, numerous lawsuits have been filed around the world against the Big Tech-companies on anti-trust issues. The stock market has sent the message that the idea that any of these measures could seriously harm these companies is simply unfounded. And that view could very well be justified. There are several reasons why Big Tech-titans can sleep well at night. Let’s run through some of them.

Breaking up is almost impossible

The businesses of Big Tech are deeply interconnected. It would take years of litigation to make such a decision a reality. With 300 billion dollars of annual profits, the legal coffers of Silicon Valley are limitless.

Fines would have to be astronomical to make a difference

Between 2017 and 2019, the EU slammed Google with a total of eight billion USD in fines. That is less than seven percent of the company’s pre-tax profit during those three years. As the stock market often regards fines as a “one-off”, it is not clear if even larger fines would hurt the market cap at all.

Too drastic of measures could trigger a stock crash

In theory, politicians could of course make new laws that severely hurt Big Tech. This would very likely lead to correction of their stock prices, which in turn would weigh heavily on the start-up ecosystem and the economy at-large. To have voters lose trillions of dollars, or even worse their jobs, is not a price any politician is willing to pay.

The companies could fight back

This is the most underestimated scenario of all. What if Google eliminated negative news stories about Google from searches, or they monitored Gmail and Google Docs to stop whistle-blowers or investigative reporters?

What if Facebook took down the accounts of politicians who are critical of Big Tech? What if Youtube only recommended documentaries that showed how fantastic Silicon Valley is for humanity?

This might all seem rather dystopian, but the question must be asked. After all, anything that can be done with technology tends to be done. With Facebooks “Project Amplify”, this is already inching towards reality. Most importantly, what could anyone do to prevent this? The answer is: nothing.

As things stand now, Facebook and Google are controlled by Mark Zuckerberg and Larry Page/Sergey Brin who own more than 50 percent of the voting power. An American president can be thrown out of office, but no one can sack Mark Zuckerberg. And the reality is that Big Tech can use the power of their platforms for more or less any purpose they please. As Facebook whistle-blower Frances Haugen told CBS 60 minutes:

“The thing I saw at Facebook over and over again was there were conflicts of interest between what was good for the public and what was good for Facebook, and Facebook over and over again chose to optimise for its own interests, like making more money.”

To satisfy Wall Street, Big Tech-giants must deliver constant growth and more profits every year

Here we arrive at the crux of the problem. Silicon Valley’s algorithms govern the world, but these giants are in turn governed by an even more powerful algorithm: the paradigm that is called shareholder value.
To satisfy Wall Street, Big Tech-giants must deliver constant growth and more profits every year. And in the choice between ethics and profit, the answer is, more often than not, profits.

Silicon Valley author and entrepreneur Tim O’Reilly has called Big Tech “slaves under a super-AI that has gone rogue” – meaning the financial markets.
Breaking out of this cycle is easier said than done. Companies like Apple, Google and Facebook use their stock to pay their employees, which means they are highly dependent on stock prices rising.

But bad ethics also runs the risk of alienating these same employees. Internal protests have rocked Google, Amazon, and Microsoft in recent years.

Hurt society or hurt the stock price? Lose staff over scandals or over bad pay? These are the dilemmas that the most powerful companies in history face. Whether Big Tech has really become too-big-to-stop remains to be seen. Ultimately the power rests with you. Without the billions of daily users, Silicon Valley’s influence amounts to exactly zero.

So, if your kids or grandkids one day ask how a few individuals acquired so much wealth and power, the answer is simple: we gave it to them.

Facebook is hiring 10,000 people to work on it. The Metaverse is no longer science fiction – some say it’s the next Internet.

Throughout the last few decades, much of what we previously considered science fiction has become more science than fiction. We may not have flying cars yet but asking your fridge to create a grocery list is an everyday occurrence. And – as has been stated so many times before – our precious smartphones have more processing power than NASA did when it sent Neil Armstrong and Buzz Aldrin to the moon. So it shouldn’t come as a surprise to us when terms like “the metaverse” are no longer constrained to The Matrix and Ready Player One. But before we look at how the metaverse will impact our future, let’s understand it.

A futuristic virtual world

The term “metaverse” was coined by author Neal Stephenson in his 1992 novel “Snow Crash”. He used the term to refer to a 3D virtual world inhabited by avatars of real people. The term comes from “meta” (beyond) and “verse” (from universe), and it is typically used to describe a futuristic virtual world on the internet in some form. Still, the term, as it pertains to an actual, real-world phenomenon, doesn’t really have a universally accepted definition.

Venture capitalist and author of “The Metaverse Primer”, Matthew Ball, writes that “The Metaverse is best understood as ‘a quasi-successor state to the mobile internet’”. This is to say, it’s a technology that will completely change how we operate in the world but also take a long time to develop based on many different, secondary innovations and inventions.

Since metaverse is not a completely developed term with a clear definition, it’s tricky to pin down, but I’ll give it my best shot. Currently, as far as we can define it, the metaverse looks like a successor to the internet in which its users are more tangibly connected to the virtual experiences taking place there. That could be via everything from voice interfaces, to VR headsets and haptic wearables.

We’re already unlocking our phones via facial recognition and buying digital art via non-fungible tokens.

In the long run, it is believed that all of these experiences will be connected and collected in the metaverse, just like the internet is the collection of a vast universe of 2D websites. The Verge has broken down the parts of the metaverse that most excite the tech industry right now, things like “real-time 3D computer graphics and personalised avatars,” and “a variety of person-to-person social interactions that are less competitive and goal-oriented than stereotypical games.”

Previously mentioned author Matthew Ball believes that the metaverse will have as big of an impact on our daily lives as the electricity revolution and the mobile internet. It’s also an interaction of the same kind – the internet (and its iterative version, the mobile internet) couldn’t have happened without the electricity revolution, and the metaverse couldn’t happen without the internet.

Ball writes that the “metaverse iterates further by placing everyone inside an ‘embodied’, or ‘virtual’ or ‘3D’ version of the internet and on a nearly unending basis. In other words, we will constantly be ‘within’ the internet, rather than have access to it, and within the billions of interconnected computers around us, rather than occasionally reach for them, and alongside all other users and real-time”. Facebook CEO Mark Zuckerberg, who is a big proponent of the metaverse, has described it as “an embodied internet”.

10,000 new workers

His plan is to shift Facebook from a social media company to a metaverse company, and he’s already put this plan in motion. The company will change its name – the social network will keep Facebook but the parent company will be renamed, much like Google’s approach with its parent company Alphabet. This could be to avoid the less than stellar reputation linked to Facebook, but it also symbolises the shift in course. Zuckerberg plans to employ 10,000 new workers in Europe to join the company’s components and create this metaverse.

Now, that all sounds very sci-fi. But it’s really just the next step in the development of things we already take for granted. We’re already unlocking our phones via facial recognition and buying digital art via non-fungible tokens. Admittedly, it feels more far more far-fetched that we’ll be having our team meetings remotely via VR setups, sitting on our couches at home while appearing at a conference table in a virtual space with our colleagues.

Independent tech analyst Benedict Evans explained the current discourse around the metaverse like “standing in front of a whiteboard in the early 1990s and writing words like interactive TV, hypertext, broadband, AOL, multimedia, and maybe video and games, and then drawing a box around them all and labelling the box ‘information superhighway’”. Essentially, we’re merging tech and concepts such as augmented reality, virtual reality, mixed reality, gaming, cryptocurrencies, non-fungible tokens and more, under one umbrella.

Connectivity is the key difference

I’ve started thinking about the theory of the metaverse like electricity. The idea is that we’ll be using it so naturally in our daily lives, when swapping our glasses for AR glasses with interactive screen overlays, that we don’t even think about it unless it goes down. As I mentioned before, we already use an extended internet, in which our physical bodies, voices and gestures are connected to our devices (think face scans, voice assistants, and VR). The metaverse is the extension of this. Perhaps we’ll all have virtual avatars representing ourselves online, for which we buy virtual designer clothes and virtual bespoke art pieces with non-fungible tokens.

The key difference between that and what we have today is connectivity – between ourselves and the virtual world, and between the many different existing virtual worlds. Especially following the giant disruptor that is the Covid-19 pandemic, the need to physically engage with other people is huge, but so is the need to connect with people over vast distances in just a matter of seconds. The metaverse could be a merging of the two needs.

The major challenge with this, of course, is that not everyone has access to the tech fuelling this development – a lot of people don’t even have access to a reliable internet connection. Just as the smartphone accelerated the mobile internet, other personal devices will be the driving forces of the hypothetical metaverse. The smartphone is relatively accessible for the majority of people today, but that wasn’t the case when ideas about a mobile internet arose. Just like all other technical developments throughout history, the metaverse will surely develop into something completely different than how we first imagine it now. But until then, I’ll look forward to meeting your avatar form in my virtual palace filled with non-tangible art in the near future.

Is quantum computing the next major revolution in computer science or will it remain a dream scenario for the foreseeable future? Sven Størmer Thaulow, EVP Chief Data and Technology Officer, looks into an area that is still surrounded by myths.

The fields of quantum mechanics and quantum computing are difficult to understand, even for people who have studied them at university level. But what are they, and what makes their application in computer science so interesting?

First we need to take a step back. Data processing traditionally operates via a digital computer language; everything – images, sounds, text, etc – is broken down into 1s and 0s. When I write “S” using my keyboard, it represented as “01010011” – the ASCII character code in binary format. This is done by feeding current into eight “transistors” in a processor (or chip), with different voltage levels representing the binary states of “1” or “0”. A thing inside the computer reads this and displays “S” on my screen.

Packing transistors

In data processing, building more powerful computers has largely been a matter of packing as many transistors as possible into a chip and getting the clock frequency (the speed at which the computer computes) as high as possible. Many will be familiar with Moore’s Law describing the increase in processing power. It states that the number of transistors on a chip will double every other year. It’s hotly debated, but for some years now, many have claimed that Moore’s Law will soon be dead and that we have reached the limit for how many transistors can be packed into a chip. We’re currently down to three nanometres between the transistors, with the standard distance on your Iphone chip being five nanometres. Attempts to remedy this are being made by designing processors in 3D and other techniques.

However, increased computing power is not just about the number of transistors on a single chip; today we buy vast amounts of computing power in the cloud and no longer have to rely on having our own computers in-house. This means that we can all easily access vast resources to solve computing problems precisely when needed, no more, no less.

Machine learning behind the demand

Demand for such computing power has grown especially rapidly due to the need to train machine learning algorithms on large datasets. These algorithms try to find an optimum in a system with a large number of dimensions (for example, housing prices) – a big mathematical problem. Just imagine how many variables that influence the price of a house. The bigger and more complex the optimisation task, the greater the need for computing capacity – a need it will be difficult to keep up with using conventional data processing techniques. Even today, tasks already exist that are so complex that running them on even the world’s biggest computer cluster is inconceivable. This is where quantum computing is emerging as a promising technology.

Quantum computing is about using quantum mechanics – the theory of how things interact at small scale – to create a computer that is insanely faster at solving certain problems than a conventional binary computer. A quantum computer does not have bits (no 0s or 1s) but rather qubits, i.e. bits with more states than just 0 or 1. Qubits draw on two properties that distinguish them from regular bits: first, they can be put into a “superposition state” that represents both 1 and 0 simultaneously. Second, multiple qubits can be entangled, meaning that states in pairs of quibits can be changed immediately.

Another important difference be tween quantum computers and con ventional processors is how computing power is scaled. To double the computing power in a conventional processor, you essentially need to double the number of transistors. In a quantum computer, the computing power is doubled with every additional qubit. This means that the computing power in a quantum computer grows exponentially when the processor is scaled.

Combined, this enables quantum computers to perform multiple computing operations simultaneously, churning their way through computations which today’s biggest supercomputers would take thousands of years to complete.

This sounds incredible, but at what stage are we at in the development of quantum computing?

More than 40 years old

Well, the theory of quantum computing is more than 40 years old; in 1981 the American physicist and Nobel laureate Richard Feynman said: “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy”.

In many ways it can look as if we’ve reached the same point in solving that problem as the internet had in the early 1990s. Most work is currently being run in labs, though industry is beginning to grasp its potential. Big Tech companies (such as Google and IBM) have launched separate research programmes. Venture capital firms are investing in quantum startups. The first exclusively quantum companies have gone public. National authorities are investing strategically in the defence sector, among others, after having financed basic research over several decades.

Yet we’re still lagging behind when it comes to application. We’ve not yet reached the point of “quantum advantage”, at which a quantum computer can solve a problem faster than a computer using conventional data processing. Researchers expect the first case of quantum advantage will be achieved some time in the next three to five years.

The aim of quantum computers is to perform computations that no conventional computers can realistically manage. A major task that lies ahead will be to explore their applications. And to do this we need to think differently. New computational strategies must be developed to take full advantage of these totally new devices. The mathematics and the algorithms underlying the tasks to be performed will be fundamentally different.

Easy to miss the mark

Researchers and innovators often miss the mark when getting to grips with new innovations: Thomas Edison thought that the phonograph he invented would be used primarily for language learning; the developers behind text messaging thought it would primarily be used by courier companies to notify their customers of parcel deliveries. So what do we think quantum computers will be used for? Three likely areas stand out:

• Large-scale optimisation problems where the task is to maximise an output based on an inconceivably vast number of variables simultaneously. Some practical examples of application are in the transport sector, for finding optimal routes, or in finance for optimising profit based on a seemingly endless list of constraints and variables.
• Classification tasks using machine learning. A typical example of a classification task involves putting labels on images, for example: “dog”, “tree” and “street”. Quantum computing has proven to be more efficient at performing complex classification tasks.
• Simulation of nature, such as in molecular modelling. Modelling anything other than the most basic chemical reactions is mathematically impossible for conventional computers, but with a quantum computer this may be doable. Development of medicines and batteries are two practical examples of potential areas of application.

A supplement

The key point here is that when or if quantum computers become commercially available, they will serve as a supplement to conventional data processing. State authorities, hyperscalers (Big Tech companies) and large universities are expected to be the early adopters of quantum computers due to the fact that they will probably need to operate at extremely low temperatures in dedicated facilities. So the number of quantum computers will likely be small initially – that is, given today’s technological constraints.

That said, quantum computers will more extensively be offered as a cloud-based service, on par with conventional data resources, and be made available using much simpler user interfaces (high-level programming language) than those we have today, where developers in reality need to understand quantum mechanics in order to program the machines.

So what does this mean for Schibsted? We will monitor developments, but will probably wait a few years before we start experimenting with the technology – and when that day comes, we will do it using cloud-based quantum computing.

The pandemic changed our way of working. But for many Schibsted companies, the road to a distributed work style started in Krakow ten years ago when the tech hub in Poland was established.

It was a biting cold January day in 2012. A group of Polish and Norwegian product and tech people were gathered in a conference room in Andels Hotel in central Krakow, Poland. The atmosphere was tense as the group tried to get to know each other. Only one topic was on the agenda: how do we work together as one team that’s located in both Krakow and Oslo?

Little did we know then that ten years later Schibsted Tech Polska would grow to more than 250 employees and become an indispensable part of Schibsted’s product and tech organisation.

But this is also a story about how teams from different Schibsted brands started working together for the first time – and how the first distributed development teams were created.

Back in 2011, working from different locations was unimaginable for most teams in Schibsted. Practically all software engineers were based in the same locations as the brands they supported. Video meetings were equally rare. But with the Poland hub, an early form of hybrid work emerged, where all teams were distributed.

Collaborating across cultures

Almost no teams operating out of the Poland hub have all their people in Poland alone, which is why video conferences and digital collaboration tools have been key to collaboration since the beginning. Another important aspect has been collaboration across cultures and brands – and building a hub that attracts tech talents.

“In Schibsted, the tech hub in Poland is unique because it is a pure tech company. Basically, all the employees are software engineers. This, together with the Scandinavian work culture, is how we can attract the best talents,” says Konrad Pietrzkiewicz, who joined in 2012, and is today part of the Schibsted Tech Polska management team.

Schibsted Tech Polska grew out of Media Norway Digital, a joint product development unit for Schibsted’s subscription-based newspapers in Norway.
In 2011, the urgency of planning for a digital transition was weighing heavily on the top management in the media houses. This led to a flow of ambitious digital product plans.

With some 20 employees at the time, Media Norway Digital struggled to respond quickly enough. Many more developers were needed. But they were almost impossible to recruit in Norway.

A radical decision was made: We would have to look abroad for our next colleagues.

Several cities were considered. In the end, Krakow was chosen because of its proximity to Scandinavia and the many technical universities producing a steady flow of young software engineers.

The new company was established at record speed in the autumn of 2011. Within a few months offices had been rented, furniture from Ikea assembled, and the first two teams recruited. Video conferencing equipment was shipped from Norway and the new colleagues met for the first time.

The even bigger nut to crack was how to develop mutual trust between the new Norwegian and Polish colleagues. Many were sceptical at first, especially since many other companies had failed in setting up this kind of distributed team structure.

Starting with the meeting in Andels Hotel, ground rules were set to establish the right culture:

• We would always talk about being one team – across countries. Always say “us”, never “they” and “we”.
• The standard of offices, laptops, video conferencing systems and other equipment would be at least as high in Krakow as in Oslo, if not higher.
  The teams were encouraged to meet physically and often to get to know each other on a personal level, and to make sure that everyone had the same understanding of the problems to solve.
• All teams had to meet every day on video. This rule was relaxed later, but in the beginning it was crucial to put in place a culture of frequent communication.

The first offices only had space for 30 people. But in only a few weeks, VG and Aftonbladet asked if they could also set up teams in Krakow. Already by May 2012, the company had completely revised its strategy. A new and bigger office space was leased. It would now be a tech hub welcoming all Schibsted brands.

But to join the brands had to promise to collaborate. There should be open and free knowledge sharing across the brand teams, and all code produced in Krakow could be freely reused by other Schibsted companies that also had teams in Poland.
The new offices soon looked like a United Nations of Schibsted, with brand logos filling up the walls: VG, Aftonbladet, Aftenposten, Stavanger Aftenblad, Bergens Tidende, Finn, Distribution Innovation and more.

Almost every month new brands joined, and within a year there were close to 100 employees.

Like a cool small start-up

“We had been told several thousand people worked for Schibsted. But the reality is that we felt like we all worked in a cool and small start-up. It was fast-paced, informal and everyone knew each other,” Konrad reflects.

He was recruited as team leader for VG in Krakow in May 2012. VG had decided to give one of its most important projects to the Krakow team. The project was to build a new web-TV platform.

“I had worked for a Polish media company before. This was my chance to step up the game with an international media group,” Konrad explains.

Konrad and his team embraced the VG culture immediately. And as with the other teams, they were eager to prove they could be trusted with the projects they had been given.

“I would say it was a healthy competition between the brands. In many ways we inherited the culture from the newspapers in Scandinavia, and we were all eager to demonstrate that our team was the most innovative,” he says.

Konrad’s team was also the first to develop a product that would be scaled and used by other media houses. Today all Schibsted’s news brands use the streaming platform developed in Krakow.

“I am really proud of that. And it was exciting. We really had to push our limits to make it happen.”

Informal atmosphere

Over time the new colleagues learned to work well together, despite cultural differences. Many Polish developers came from companies with a more hierarchical structure than in the laidback Scandinavian work culture. They were surprised about the informal atmosphere, and especially the friendly tone between managers and employees. On the other hand, Scandinavians were taken aback about the ambitious dedication and high competence level of their new colleagues.

In 2014, a second office was opened in Gdansk.

This site became a base for teams working with Schibsted’s brands within ventures and financial services. Today more than 250 people work for Schibsted Tech Polska.

Konrad Pietrzkiewicz is now part of the management team. He is still responsible for Schibsted’s streaming solutions, and his team is one of the most long-standing development teams in Schibsted.

He has stayed on for almost ten years for two reasons.

“First I strongly believe in video in media – and love taking part in developing the best solutions. But equally important: I am passionate about integrity in the news. In Schibsted I can combine these two.”

With Schibsted Account, Ida Kristine Norddal and her team has a powerful tool to explain Schibsted offers, Armin Catovic is developing contextual advertising and Ralph Benton is making sure Schibsted is safe. Get to know some of our people.

Unlocking the Schibsted universe

With more than three million users logged in every day, Schibsted Account is a player you can count on. The service is used to log in to Schibsted’s newspapers, marketplaces and other digital services, and it has become the way that most end users engage with the Schibsted brand.

Ida Kristine Norddal’s product team and related engineering and UX teams, all within the User Foundation unit, are growing rapidly to keep up with user needs and to further develop the service and experience.

“We want to step up our game, to make sure the users understand who Schibsted is and that they can trust us with their information,” she explains.
Schibsted Account’s most obvious task is to enable Schibsted users to prove who they are, and by doing so, they get access to our products and services, it also enables subscription offers. But the potential and future ambitions point towards so much more – Schibsted Account is on its way to becoming an important key to the Schibsted universe.

“We also want users to be able to discover and explore all the different things that we offer”, adds Ida Kristine.

She describes this as Schibsted’s hidden treasure – news, buying and selling, ordering breakfast, finding a handyman and a lot more – services that the users can access with their Schibsted account.

“Our ambition is to simplify the whole user experience, making it easy to go between our different services, using the same log-in and, when needed, the same account information”, says Ida Kristine.

No doubt, her team has a lot to do – and many treasures yet to reveal. In between, they also communicate with all these users. Every month they send out more than three million emails, another reason why Schibsted Account is the Schibsted brand’s most important ambassador.

Ida Kristine Norddal
Product Lead, Schibsted account
Years in Schibsted: Almost 4

Serving ads without using tracking

Data is what makes Armin Catovic tick. It’s also one of the reasons why he joined Schibsted – in addition to the fact that he now can use it to do good.
His team has developed a contextual advertising product, which uses data models to identify specific content in articles on Schibsted’s news sites.

“This makes it possible to offer ad segments to our customers, based on news content alone. We don’t need to use cookies or tracking”, Armin explains.
More specifically, it means that advertisers can pinpoint certain keywords to which they want to be connected. The data models find those words in articles on Schibsted’s news sites, and an ad can be placed there.

Recently, Armin and the team developed a more advanced model that can identify broader contexts.

“For instance, bicycle retailers would typically want to be connected to cycling or Tour de France. Now they can have a wider perspective and choose to be seen in stories about climate change, since bikers often cycle to reduce their environmental impact.”

And the advertisers are happy about it.

“Feedback from both our own product specialists and the advertisers has been very positive, and we’ve now reached a monthly revenue target of
1 million SEK/NOK.”

Armin Catovic
Senior Data Scientist & Tech Lead
Years in Schibsted: Almost 1

“It’s about protecting society”

The number of cyber-attacks has increased dramatically in the last ten to five years. Today everyone is a target.
“Like many others, we really need to improve in this area”, says Ralph Benton.

As CISO he has initiated a cyber security program to improve information and IT security, how Schibsted detect and respond to cyber attacks and to educate all employees on security risks.

“It’s about protecting yourself, your colleagues, Schibsted – and in the end the whole society.”

That last thing is particularly valid for a company with media outlets. Except attacks where someone’s trying to steal customer information, or ransomware attacks where someone brings a site or service down – in the fake news era, news sites are facing risks that their content might be manipulated.

As an individual, you should protect your digital identity.

“When possible, use multifactor authentication – like Bankid or OKTA – when not possible, use strong and unique passwords, and do not use the same password everywhere”, urges Ralph.

The good news is that in Schibsted we have already learned a lot.
“When we sent out the last fake phishing attack, a lot of employees reported it to the IT Service Desk and that is really good”.

Ralph Benton
Chief Information Security Officer
Years in Schibsted: 2.5

We are entering a new era of medicine, where AI will revolutionise healthcare and treatment. It might also lead to more empathetic doctors.

Pancho was 20 years old when he was paralysed and lost his ability to speak. He was a Mexican-born amateur football player and a field worker in the California vineyards, until one summer Sunday after a match, he was in a car crash. After surgery, he suffered a stroke and his life changed.

When he woke up from his coma, he tried to move. He couldn’t. He tried to talk and discovered he couldn’t form a word. Then he started to cry, but he couldn’t make a sound.

At that point he wished he hadn’t come back from the coma at all, he recently told a New York Times reporter. Pancho felt like his life was over.

Today Pancho can speak again

Fifteen years later, his life would change again, when a group of American scientists implanted a chip with 128 electrodes into his brain, plugged a cable into his skull and trained deep learning software to read his mind. Today, Pancho can speak again. When he thinks of words, the software decodes the signals from his brain and his words are spoken in a gravelly, synthetic voice.

Interpreting his brain signals and translating them into words is only possible thanks to an artificial neural network interacting with Pancho’s organic one. The AI software was trained on his thoughts during 50 sessions in which Pancho was trying to speak words, until it recognised the patterns of brain signals that corresponded to certain words. (Thanks to his new ability to speak, the patient has expressed that Pancho is the nickname he prefers, to protect his privacy.)

Pancho’s AI-assisted rehabilitation may seem like the stuff of science fiction. It is all the more remarkable for being real, and part of an AI revolution in medicine and health care.

The AI research field was conceived at Dartmouth College in 1956, a year when James Dean ruled the silver screen and Elvis Presley had his first number one hit, Heartbreak Hotel. Since then, AI has had its ups and downs, exaggerated expectations followed by troughs of disillusionment. The term “AI winter” was coined specifically to denote periods of disenchantment, when investment in AI dried out and the pace of progress slowed.

Notably, AI systems have defeated humans at games like go, Dota and Jeopardy.

Since Pancho’s accident in 2003, however, AI has made great strides thanks to the advent of artificial neural networks. The theory behind it had been established decades earlier, the idea that software networks mimicking the arrangement of neurons in the human brain would provide an important step in the evolution of thinking computers. But it wasn’t until the 2000s that it bore fruit, when processor speeds, storage capacity and data availability finally reached a tipping point in which it became feasible to train large neural networks on vast amounts of data, quickly leading to improvements in image recognition, automatic translation, and other domains.

Notably, AI systems have defeated humans at games like go, Dota and Jeopardy. Social media corporations have used machine learning to find the best methods to keep us refreshing our feeds in endless loops of distraction, and self-driving cars are inching ever closer to arriving on our streets. But only in the last few years has medical AI started taking off in a big way. And if you think hooking a cable to Pancho’s skull and reading his mind is something of a miracle, well, that’s just the tip of the iceberg.

Alphabet’s AI research company Deepmind is perhaps best known to the public for creating Alphago, the go-playing AI that runs circles around human go masters. But their work on Alphago has turned out to be useful in another domain as well. In 2020, Deepmind entered their tweaked AI, Alphafold, in a contest to solve the problem of protein folding. To nobody’s surprise, Alphafold crushed the competition.

Mapped out proteins of the Corona virus

Biologists have hailed Alphafold’s success as the first time AI has solved a significant scientific problem. An improved understanding of protein folding is already significantly reducing development timelines for drugs and vaccines. For instance, during the Covid-19 pandemic, Alphafold’s predictions were used to map out the proteins of the virus.

Currently, AlphaFold is being used to help develop new drugs for tropical diseases. It has already helped scientists find a safe drug for treating sleeping sickness, replacing a previous drug that is highly toxic.

Other areas where AI is currently being used include:

Medical imaging

Interpretation of visual data is key in fields such as dermatology and radiology, and fertile ground for machine learning algorithms. Tech companies, hospitals and universities around the world have developed machine learning algorithms that surpass human experts at detection of various skin conditions, cancers and other abnormalities. While human specialists can be highly skilled at examining rashes and reading x-ray plates, they require years of training and they are a scarce resource. AI speeds up the process, makes these skills more accessible and can, in some cases, detect anomalies that human doctors cannot.

Health records analysis

Electronic health records create new opportunities for machine learning algorithms to find previously unknown patterns in symptoms, diagnoses, medications and treatment effects. Researchers in Uruguay have developed a system that analyses the text a doctor enters in a health record and pulls up similar cases that may contain valuable insights. Spanish researchers have created a system that analyses a patient’s health record, and predicts risks for diseases based on their historical data along with data from their family’s health records. As health record data grows at a rapid pace, human physicians cannot keep up, but they can learn from the analysis performed by AI systems.

Diagnosis

Symbolic AI was first tailored to diagnose disease in the 1970s, using rules-based decision trees to create “expert systems” to assist professionals in their decision making. Recent methods instead rely on artificial neural networks and have proven themselves useful in diagnosing a vast range of diseases, relying on techniques such as image recognition, symptom evaluation and natural language processing.

Disease control

On December 31, 2019, the Canadian medtech company Blue Dot alerted its clients about a new flu-like virus spreading in Wuhan, China. They were a week ahead of the World Health Organisation and the Centers for Disease Control, thanks to their use of machine learning algorithms that were processing 100,000 news items and disease reports in 65 languages every day. AI-supported systems such as the Blue Dot platform can monitor the spread of new diseases, but also predict future trajectory based on data such as travel patterns, hospital admittance, historical data from previous epidemics and mathematical models.

Drug interactions

For patients taking a large number of medications, drug interactions can be potentially harmful as well as hard to track by individual physicians. Spanish and Chinese researchers have shown that machine learning algorithms can scan databases, medical literature and electronic health records and find patterns indicating adverse reactions to drug combinations.

Drug discovery

AI has proven its ability to speed up drug discovery dramatically. It is useful in several ways, such as predicting the 3D structure of target proteins, predicting drug-protein interactions and toxicity risks, designing biospecific drug molecules as well as assessing drug activity. While Deepmind has turned its attention to neglected tropical diseases, British medtec start-up Exscientia and Japanese pharmaceutical firm Sumitomo Dainippon Pharma used AI to create a treatment for obsessive-compulsive disorder. Development took only twelve months (compared to the expected timeline of five years) and the drug is now in human trials. Many other research teams around the world are currently using AI to find new drugs, and Andrew Hopkins, CEO of Exscientia, recently predicted that all new drugs will be created with the help of AI by 2030.

Neural prosthetics

With machine learning, Swiss researchers have trained algorithms to interpret the muscle signals of an amputee, to control a prosthetic hand. The task is complex because muscle signals are noisy and traditional approaches have yielded clumsy results. Machine learning changes the game, and the AI-powered prosthetic is more precise and quicker to react than previous prostheses. Advances in this field are bearing fruit in many research labs and Pancho’s new ability to talk is another example of this approach, called a speech neuroprosthesis.

As Pancho’s example and all the ongoing research demonstrates, we are entering a new era of medicine where machines can read the signals of our bodies, unfold amino acid molecules, replace lost abilities and tailor drugs to the protein structure of specific viruses. There will be disputes over privacy and security, as AI systems crave massive amounts of data to further increase their capabilities. But the technology is so powerful and the stakes are literally life and death. As evidence of AI’s potential continues to grow, odds are that privacy advocates will be fighting a lost cause. After all, no government would prioritise privacy over a cure for cancer or prevention of the next pandemic.

Humans doctors can relate

Will friendly and competent doctors be replaced then, by robodocs with superior diagnostic skills and knowledge of drug interactions? Well, not so fast. If there is one domain where humans are irreplaceable, it’s emotional work. No algorithm will feel genuinely sympathetic about your knee pain. If you want cold, hard facts, go see Dr Alphacure. If you want someone who can relate to your problems, go see someone made of the same stuff as you: flesh, blood, bone.

Sometimes, you see your doctor just to get your prescription renewed. But often, when you go because of a new ailment or injury, you are not only hoping for an intelligent analysis of your symptoms. You want to be taken care of, by someone who understands – from the inside – what it is to have your body starting to fail. You want someone who knows what it is like to wake up at 4 a.m., chest pounding, fearing that your expanding mole might be a mark of death. In time, AI could be taught to simulate these emotional skills. But fake empathy, in the end, is just manipulation.

While human expertise can be replaced by AI, empathy cannot. But as AI unburdens medical practitioners from many of the mechanical tasks of the profession, physicians may have greater opportunity to develop their human-to-human skills. In this way, medtech AI will not only revolutionise drug research, give voice to the speechless and improve diagnostic accuracy. It may just lead to more empathetic doctors, too.