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How deep tech will affect sectors and key markets in 2020-2022

This analyst note evaluates 10 current innovation trends in AI/ML, Blockchain, IoT, and other emerging technologies. And, assess the socio-economic impact, the evolution trajectory and the potential d

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Emerging deep technologies such as Artificial Intelligence and Machine Learning (AI/ML), Blockchain, Internet of Things (IoT) have all been evolving at a rapid pace. Currently, as per our analysis AI/ML is on an eight-month generational cycle, Blockchain on 15-month, and IoT on an 18-month generational cycle. The methodologies, techniques, and protocols are changing and the new ones going mainstream every eight, 15, and, 18 months, respectively for these technologies. And, there are a number of technology, business model, macro and micro-economic and consumer behavior trends that are changing across industries and geographies, on the back of the evolution of these deep technologies.

The innovation in these emerging deep technologies and the associated products and solutions, business, monetization, marketing, and adoption models are coming from different corners of the world, from companies of all sizes – big and small – and some way ahead than the others. In such a situation, we at Convergence realized that analyzing the trends and providing the outlook for a single year might not be right or adequate. So, we are analyzing the trends in deep technologies and predicting the evolution for a three-year period, 2020 to 2022.

Below are the high-level trends and predictions in various areas. Over the course of this year, we intend to dive deeper into each of these and shall publish our analysis in the form of Research Papers, Analyst Notes, Talks, Vlogs, and Podcasts.

Key deep tech trends and predictions for 2020-2022:

1) AI – Machines to go beyond intelligence to crack emotion and human psychology

Over the last few years, deep analytics and machine learning algorithms have been working have been making machines smarter. Tasks such as pattern recognition, predictions, insights, sentiment analysis, and recommendations have been handled by the machines. There has been an effort to push the boundaries and evolve into the realm of “Personalization”. Some of the Advanced Analytics and Machine Learning solutions have been used for profiling and customized recommendations of offerings and incentives. However, to truly personalize, consumers’ emotions need to be dissected and handled by the machines. There is a significant amount of ongoing work in making machines “Emotionally Intelligent” and to get them to interact with humans as naturally and intuitively as possible. This will require AI/ML algorithms to hack human emotions and psychology.

We are witnessing early trends of these solutions evolving. Samsung’s Neon that showcased Neon Avatars at CES a few weeks back, Israeli company INFI’s EmpathAI platform, Emoshape’s EPU III, an Emotion Chip for real-time emotion synthesis and reasoning for AI-based systems (specifically for robots to help them understand and interact with humans better) are all developments in this direction. Over the next 3 years, we expect a significant amount of focus and progress in making machines Emotionally Intelligent through AI/ML algorithms.

2) AI – Chipsets and processing on the edge: The next paradigm shift in AI to come from hardware innovation, not Software

We, at Convergence Catalyst strongly believe that the next paradigm shift in AI innovation is going to be hardware-led. There is continuous and rapid innovation in various Data Science techniques including Clustering Algorithms, Support Vector Machines and low-data techniques. However, the AI chipsets that are being researched and developed by a number of large companies including nVidia, Intel, AMD and private companies such as DeePhi, Cambricon, Groq, KnuEdge, etc., are expected to enable on-device processing of AI algorithms.

The current focus of the industry and innovators is to move AI processing to the edge. Many big tech companies are acquiring smaller, creative companies in the space (Apple’s acquisition of Xnor.ai in January 2020). However, the real edge computing of AI algorithms will be led by on-device chipsets and SoCs (System on Chip). And, the next 3 years, this particular innovation in AI will be in focus.

3) Deep tech – AI is the only deep tech evolving in the consumer space, all others like Blockchain, IoT and Virtual Reality (VR) are evolving in enterprise space

Internet of Things (IoT), in 2016 being at the peak of Gartner’s Hype Cycle, had a number of companies, especially smaller startups that were working on consumer-focused solutions. The combination of sensor-enabled hardware, data analytics platforms, and wireless protocols was very conducive for the potential of myriad creative and innovative consumer applications and solutions. However, lack of proven monetization and business models, scale, investor focus in the consumer-IoT space, coupled with the rise of Industry 4.0 have forced most of the IoT-focused companies to develop Enterprise-based solutions since 2017. Similarly, in the Blockchain domain, despite a number of consumer-facing DApps (Decentralized Apps) spring up in 2017-18, the enterprise deployments of Blockchain based solutions, most of them on Hyperledger architecture are witnessing adoption and growth. Similarly, VR primarily for Industrial Training and Robotics, for Industry 4.0 are all evolving as Enterprise-focused technologies.

AI is the only deep technology that is witnessing significant adoption in the consumer domain. From targeted ads, to differential pricing engines, recommendation engines, chatbots, local language translation engines, news feeds, notifications, computer vision and facial recognition based attendance, access management and purchase intent analysis (in retail shops & malls), etc., AI algorithms are evolving rapidly, finding adoption and changing the face of the consumer tech space.

4) Sovereign cryptocurrencies will start to evolve

Blockchain-based digital currencies (cryptocurrencies) such as Bitcoin, Ether, Ripple, Bitecoin Cash, Litecoin, Stellar, etc., were supposed to enable economic, political and social freedom, if implemented properly. However, owing to wide speculation and unregulated trading of these currencies, most of the countries around the world have banned the trading and transactions of these cryptocurrencies. At the same time, many governments around the world are strong believers and advocates of Blockchain as a technology, and one of the first application they are using this technology to develop are Sovereign Digital Currencies and Cryptocurrencies. These sovereign cryptocurrencies are centralized currencies that are open for transactions and trading (like currency trading and government bonds).

In 2015, Tunisia became the first nation in the world to roll out its national currency, the Dinar, on a Blockchain. From banking the unbanked (Tunisia & Senegal), alternate trading tool and currency for its national asset and commodities (Venezuela’s Petro) to being a part of the inevitable Blockchain-based digital currencies based economic future (China, Russia, and India), many countries have their own reasons to develop sovereign cryptocurrencies. However, the biggest validation and indicator of sovereign cryptocurrencies based future is the development of China’s DCEP (Digital Currency Electronic Payment), which started pilot testing in January 2020. Currently, a number of countries including Russia, Iran, England, China, Japan, Sweden, Australia, the Netherlands, Singapore, and India are all at various stages of developing and testing new digital and Blockchain-based currencies. Going forward we expect this trend to only intensify as many countries around the world commit to develop and launch their own sovereign cryptocurrencies.

5) 5G deployment and growth to be led by China

Globally, Europe won the 2G era, Japan took the lead in 3G, the US evolved strongly and led the 4G rollout and now, China is slated to lead the 5G ecosystem. Chinese operators are expected to spend over USD 220 billion between 2019 to 2025 for the commercial rollout of 5G services in the country. The thriving IoT (Internet of Things – Sensor-based smart connectivity solutions) ecosystem including resources, expertise, and deployments, for which the 5G technology has been developed and optimized for, are expected to play a catalytic role in faster adoption of 5G in China.

6) Super Apps – apart from WeChat, the number of super apps around the world will grow… and, they will be country/region-specific

WeChat evolved as the first super app back in 2015. A mobile Instant Messenger that evolved into a food and grocery delivery, cabs hailing, entertainment, insurance, doctor appointments, digital payments, etc., now the app morphs into a multi-utility, one-stop-shop for over a billion mobile users. Following WeChat’s model, many companies around the world including Facebook, Grab (Singapore & South East Asia), GoJek (Indonesia), Paytm (India) are trying to evolve as super apps. The two primary reasons for these mobile app companies to evolve as super apps are: 1) Lock in their users’ time for as long as possible on their platforms, and 2) Most importantly, find new revenue models and road to profitability.

However, not every app can become a super app, having critical mass and scale, stickiness, adjacencies, strong brand, efficient rollout strategies, and execution capabilities are prerequisites. Also, a country or a region can have just one or a maximum of two Super Apps. This is the path many of the mobile app companies that have garnered significant scale are taking and we expect this strategy to play out more intensely over the next three years.

7) VC Funding – B2B models to lead B2C models in attracting funding. B2B2C to evolve as the new favorite model for investors

The hypergrowth consumer technology business models are failing to witness wider acceptance, especially in the public markets. The cancellation of WeWork IPO in 2019 and not-so-enthusiastic acceptance of Uber and Lyft public market listings have made the VCs and PEs rethink their consumer tech investment strategies. In India, one of the vibrant and dynamic tech startup ecosystem, VC investments in B2B companies have surpassed those of B2C companies for the first time in 2019. We are witnessing an increasing shift in focus on B2B tech startups among investors. VCs are also looking for and preferring B2B2C model startups as they provide the predictability of B2B startups and the scale of B2C world. Going forward, we expect B2B2C models would attract more VC funding.

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8) Deep tech – innovation in business, monetization, marketing, and partnership models led by deep tech to become success factors

We at Convergence Catalyst strongly believe that going forward, as deep technologies convergence evolves, successful companies will not be the ones innovating in technology but will be the ones with innovative and creative business, monetization, marketing, and partnership models. We have evolved from the Industrial era through the Information age and are entering the Intelligence era. Every company is a data company – it either collects, needs, uses or generates data. And, every individual consumer has attributes that can be labeled, identified, processed and profiled. And, digital technologies, primarily led by Advanced Analytics, Machine Learning (ML) and Artificial Intelligence (AI) can generate tremendous value out of such attributes.

The evolution of Differential Pricing Engines powered by Advanced Analytics and Machine Learning into erstwhile flat-priced domains such as e-commerce marketplaces (Amazon & Alibaba), cab and ride-hailing (Uber, Lyft, Grab), food and hyperlocal deliveries (DoorDash, GrubHub, Dunzo, Instacart) etc., based demand and user profiling is a good example of this trend. Differential pricing based on supply-demand and user profiling has existed in the Airline and Hospitality industries for a few decades now… and, these consumer technology companies have adopted that concept and scaled it to a different level duly supported deep technologies. At the same time, innovative startups like SkyHi are using advanced data analytics and machine learning to provide flat-rate pricing for airline tickets on subscription model… turning the existing differential pricing (in industries that have been using them for long) on their head to provide consumer satisfaction. These are good examples of different companies using deep technologies for different purposes in different industries and innovating on monetization, business, marketing, and partnership models.

In the future, every company will need to assess its partnerships, both on the supply and demand side, identify value triggers at every level and forge mutually beneficial partnerships. Conventionally, the value chains in most industries have been linear, and many value chain players aspired to integrate either one step forward or backward to evolve as a key industry player. But, going forward, in the “Deep Tech Convergence” era, with multiple touchpoints, the value chains will be more matrix-based (beehive structure), and companies that adopt the role of ecosystem enablers and drivers forging innovative, mutually-beneficial partnerships will evolve as eventual winners.

9) Geo Boundaries – deep tech innovation-led geographical division to become more apparent… US-China-Europe dynamics to be impacted 

The epicenter of digital technologies innovation is moving East! From the 1950s to 2016, North America led the innovation in AI. But from 2017 onwards, China has taken a lead in the AI R&D. In 2018 alone over 30,000 patents have been filed in China in AI, of which 16,000 are Computer Vision AI patents. China has 19 companies out of the top 50 innovative companies in 2019. The country has a lead in the Computer Vision AI innovation and deployments. Also, in IoT, a technology that uses Hardware, Software and Data Analytics and Synthesis in equal measure, China has an advantage as it was the electronics hardware manufacturing bowl of the world for the last three decades. The US is challenging this rapid advancement in deep technologies innovation by China through a string of socio-political-economic and technological countermeasures. US DARPA, has released a three-point agenda and strategic plan in February 2019 to counter China’s AI innovation pace. European Union has also realized the region’s slow progress and laggard position in deep tech innovation and leadership loss to US and China, and has started a number of initiatives including bigger and easier access to grants for deep tech R&D, relaxation of immigration laws for entrepreneurs and startups in some countries, increased number of funding mandates for tech investors and accelerators in its region, etc. We expect this competition and countermeasures between the three regions to only intensify and have a ripple effect on the socio-political-economic and diplomatic relationships and dynamics.

10) Quantum Computing – significant efforts to solve the fundamental scientific problems in order to make technology more accessible 

Quantum computers of today, are at the stage where classic computers were in 1950s. Prototypes are functioning but it is not clear what shape the machines will eventually take. One big question, for example, is whether “qubits”, which are the quantum equivalent of transistors, will live in tiny loops of superconducting wire cooled to ultra-low temperatures, be ions trapped in magnetic fields or rely on some other technology. Large companies such as Google, Microsoft, and IBM have built Quantum Computers in extremely controlled conditions, showed off the capabilities and managed to encourage a number of startups to drive and grow the ecosystem. Meanwhile, various countries including the US, China, the EU, and India have allocated billions of dollars for Quantum Computing Research and Development.

Quantum Internet is being tested both on terrestrial and satellite networks, Quantum Cryptography is already finding its way into real-world deployments. However, there still exist fundamental, science-based challenges such as highly unstable ions, need for cryogenic operating temperatures, high error rate, exponentially increasing error rate upon integral growth in entanglement of Qubits, shorter coherence and lower circuit depth. These fundamental problems and challenges need to be overcome before the technology can be scaled to make it available for application innovation. And, we expect a significant amount of time and resources to be invested in solving for these issues for the foreseeable future.

This article first appeared on the website of Bengaluru based research and consulting firm Convergence Catalyst. Jayanth Kolla is the founder and partner of Convergence Catalyst.