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From the Console to the Cloud: The Disruption of the Gaming Industry

Delian Partners
Mar 3, 2021
3 min read

With restrictions on travel and social interactions, people have started spending even more time in front of screens, seeking alternative forms of entertainment: A clear beneficiary is the Video Gaming Industry which reached $159.3 Billion during 2020, increasing by 9.3% from 2019 - Video Game Statistics.

This unprecedented boom in the Gaming industry was supported mainly by Cloud Gaming technology, a kind of "Netflix for gaming" which allows users to play a wide range of games which would usually require specific high-powered hardware. Thanks to the Cloud, users can play games remotely just by subscribing to the service, avoiding the need to download files into their devices or purchasing expensive consoles. This new business model is called Game-as-a-Service (GaaS) and it is completely changing how video games are consumed.

Video game company Activision Blizzard is one of the first major pioneers of this model. Its great success came from the blockbuster “Call of Duty” franchise: Whether the user is playing on a PS5, Xbox, PC, or smartphone, he can access the entire video game saga and engage with other players from any device just by subscribing to it. 2020 was a record year for Call of Duty, with more than 100 million monthly active players engaging with its free-to-play experience.

Another successful example is Epic Games and its popular video game Fortnite. Fortnite is a multi-platform and no-cost game whose first source of monetization comes from micro-transactions. Everyone can access the basic game experience, and monetization happens when gamers decided to buy additional features such as new costumes, weapons, and even special missions.

GaaS has been around for several years, but the growth and the engagement of users has been accelerated in recent years by the live streaming. Amazon-owned platform Twitch is a game streaming platform where people can truly live the social side of gaming. Through Twitch, top video game players can make money broadcasting games and monetizing fans’ views: In March 2018, Fortnite reached about 630,000 viewers, while in 2019 another event hosted by Fortnite attracted more than 7 million viewers. In 2020, Twitch had about 26.5 million daily users and 6.9 million streamers creators each month, and these numbers are continuously rising – Twitch Traker.

In 2020 Google has entered a media partnership with Activision to show the online format of The Call of Duty League through its own video-platform YouTube. The online event was the most watched CoD e-sports match in the game’s history: The Call of Duty League reached 1.1 million subscribers on YouTube, with a peak of 331,000 viewers - Forbes.

The GaaS business model is disrupting the game industry as never before: The main difference from traditional forms of gaming is that through the Cloud, games are streamed and not sold at once. Instead to purchasing a product, users must pay for a subscription service, and that is why game developers cannot stop updating their services: By introducing exclusive new content, users become more willing to spend money to enhance their gaming experience, generating a potentially unlimited stream of revenues. To maintain a competitive edge, Game developers must communicate and cooperate with users to generate a virtuous cycle of revenues and achieve a win-win situation.

As reported by Statista, the Video Games industry will reach more than $150 billion in 2021, attracting companies outside the gaming sector: The evolution of Cloud services, combined with advancements in 5G network globally, allow big technology companies such as Amazon, Google, and Tencent, to heavily invest in network infrastructures and technology innovation to be part of the future expansion of the industry.

The information in this article should not be regarded as a description of services provided by Delian Partners SA. The opinions expressed in this article are for general informational purposes only and are not intended to provide specific advice or recommendations for any individual or on any specific security or investment product. It is only intended to provide education about the financial industry. The views reflected in this article are subject to change at any time without notice.

Tech innovation is shaping the future of the healthcare sector

Delian Partners
Sep 23, 2020
3 min read

Historically driven by analog processes, healthcare activities have been significantly altered by the technology revolution. The increasing application of Artificial Intelligence (AI) systems is becoming more common in the healthcare industry and is helping healthcare businesses to be faster and more efficient.

Already pre-pandemic, about 80% of hospital leaders said cloud investments were a moderate, high, or critical priority for 2020. Going forward, the convergence of Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) will further accelerate innovation adoption and related applications in the healthcare realm.

Technologies like Cloud computing, AI, IoT and machine learning are disrupting the health market and providing patients with new innovative services: for example, by engaging with digital providers, today patients can receive personalized medicines tailored to their specific needs, lifestyles, and genetic code, or can be visited by doctors directly through their smartphone.

Population aging around the world is another major tailwind for digital, patient-centric healthcare services. According to the World Health Organization (WHO), there were 703 million persons aged 65 years or over in the world in 2019. The number of older persons is projected to double to 1.5 billion by 2050. Technological advancements in screening processes, smartphones and wearables can bring point-of-care testing to the patients and represent a strong opportunity for providing sensitive, low-cost, rapid, and connected diagnostics.

There is increasing awareness that AI applications enable to analyze patient's health conditions and identify anomalies at a speed that humans cannot achieve, helping physicians to optimize and avoid time-consuming tasks, and reduce margins of error of diagnosis.

For example, today AI is already just as capable as (if not more capable than) doctors in diagnosing patients heart diseases, blood infections, and detect signs of potentially cancerous cellular growths. IBM’s AI program called Watson was recently challenged to analyze the genetic data of tumor cells. The human experts took about 160 hours to review and provide treatment recommendations based on their findings. Watson took just ten minutes to deliver the same kind of actionable advice.

Despite the rapid advancements in AI and machine learning in HC, we are still a long way from a total replacement of human intervention in medical processes. A research from Harvard showed that patients are reluctant to use health care provided by medical artificial intelligence even when it outperforms human doctors. The main reason is that patients believe that their medical needs are unique and cannot be adequately addressed by algorithms. For this reason, patients were less likely to utilize AI based services and wanted to pay less for it

The most likely evolution is that doctors will be supported by AI to perform repetitive tasks and increase quality of diagnosis at a fraction of time and costs. A recently developed machine-learning algorithm based on deep learning nearly matched the success rate of a human pathologist in interpreting pathology images, at about 96% accuracy. But the truly exciting thing was that combining the pathologist’s analysis the AI diagnostic method, the result improved to 99.5% accuracy,”

In summary: when it comes to healthcare, implementing AI solutions and machine learning will not necessarily mean replacing doctors, but optimizing and improving their abilities. The convergence between the healthcare industry with AI, Cloud computing, IT, and machine learning systems will further catalyze new innovative applications, providing patients with an early and accurate response to treatment and enabling healthcare organizations to reach new quality standards at a lower cost.

Deep Learning algorithms are leading the next wave of technological disruption

Delian Partners
Sep 3, 2020
3 min read

The Defense Advanced Research Projects Agency (DARPA) recently announced that an AI algorithm piloting an F-16 Fighting Falcon in a simulated dogfight against a seasoned US AIR Force pilot achieved a flawless 5-0 win, with the human pilot never scoring a single hit.

Regardless of how realistic “Computer vs. Human simulations” can be in such a complex environment, the most significant aspect is the fact that the specific AI system was developed less than one year ago using so-called “deep reinforcement learning”: starting with a complete lack of understanding about basic flight, the AI software autonomously learned fast, gaining the equivalent of 12 years of experience over the course of 4 billion simulations.

This AI revolution has been decades in the making, but only in the last decade have advances in computing power enabled a new era of AI training. The most recent technological disruption is enabled and accelerated by “Deep Learning”, the most advanced AI state in which machines can learn autonomously by analyzing vast amounts of unstructured data.

As the cost of memory and compute came down dramatically, Deep Learning breakthroughs enabled computers to process a wide range of information across different and complicated data-driven applications, as in image recognition tools, speech recognition (NLP), image recognition (GPU), data discovery, and extraction.

The era of Deep Learning, source Nvidia and JP Morgan

One of the most impressive examples of advances in machine learning is Alpha Zero, developed within the DeepMind division of Alphabet, which obliterated the highest-ranked chess program in the world (Stockfish). Given only the rules of the game, Alpha Zero learned how to play chess within four hours learning from playing against itself. The crucial advancement of this technology is the capacity for life-long learning: this AI system can acquire new information and keep in mind those already experienced to solve progressively more tasks, without losing information previously learned, a hurdle known as “catastrophic forgetting”.

The exponential advancements in AI models are supported by ever larger AI chips which are embedded with significant amounts of fast memory to handle the demands of AI algorithms. The race for manufacturing the largest AI chips includes both public and private companies: while Xilinx has announced the chip with the highest logic density on a single device ever bult, featuring 35bn transistors, private startup Cerebras has recently showcased the largest chip ever built, with 1.2 trillion transistors and 3000x more in-chip memory. Finally, Alphabet announced a major breakthrough in quantum computing, whereas their processor “Sycamore” took about 200 seconds to complete a task that would take a state-of-the art supercomputer approximately 10’000 years.

Regardless of potential winners and losers, technologic advancements are attracting large amount of investments. According to MarketsandMarkets, the overall Deep Learning market is estimated to reach $18.16B by 2023 from $3.18B in 2018, at a CAGR of 41.7% over this period.

The investment implications are staggering and long-term in nature since companies are increasing technology investments to survive the rapidly changing landscape of the Fully Connected Economy. As a result, AI spending is projected to grow 28% annually from $19bn in 2018 to almost $100bn in 2023.