Growth drivers.

Macro-trends driving growth in the Meta Asset Industry is represented by the conglomeration of trends across 3 industrial segments.  

Positive drivers for growth include:

• Growth Opportunities via Increased AI Adoption as a Business Imperative & Data-Driven Organizations
  This Digital Twin algorithm market continues to see the prevalence of AI adoption as a core business imperative.  Findings from a 2021 survey indicate that AI adoption is continuing its steady rise: 56% of all respondents report AI adoption in at least one function, up from 50% in 2020 (Global Survey: The State of AI in 2021, 2021).  Out of all commonly adopted AI use cases across industry, by function, 27% are targeted towards optimization of operations (O&M) and service (Global Survey: The State of AI in 2021, 2021).  Additionally, predictive analytics continues to be a key focus area as 93% of companies indicate that they plan to increase investments in the area of data and analytics (Gusher, 2022).  This is on top of the 97% of major worldwide organizations, including all of the Fortune 500, focusing investments into big data and AI (30 Big Data Statistics Everybody's Talking About, 2022).  Leaders and laggards in AI adoptions are beginning to become delineated: a recent study indicated at least 5% of earnings before interest and taxes (EBIT) is attributable to AI driven capabilities by 27% of survey respondents, up from 22% in the previous study (Global Survey: The State of AI in 2021, 2021).

• Growth Opportunities via the Commoditization & Democratization of Data Science
  AI is poised to meet the same fate as IT: the commoditization of AI has already begun (On the Commoditization of Artificial Intelligence, 2021).  The commoditization of analytics platforms for Digital Twin algorithm creation, in parallel with the democratization of data science in the hands of the masses, allows organizations to re-evaluate their attempts to bring data science capabilities in-house - which is becoming increasingly difficult due to skilled labor shortages across industries (further exacerbated in industrial segments).  The prevalence of business models related to AutoML, composable algorithm building and analytics, machine learning toolkits and APIs, out-of-the-box machine learning models, and enhanced by self-service analytics visualization packages.  Ultimately, this catapults forward the technological data science adoption curve towards Algorithms-as-a-Service (AaaS) (Cloud AutoML Custom Machine Learning Models, n.d.).  Algorithms-as-a-Service refers to a new area of offering for organization in which algorithms are offered as an AI service containing a library of pre-built machine learning algorithms explicitly designed to address a set of common challenges (Algorithms as a Service: The Future of Computing, 2022), (On the Commoditization of Artificial Intelligence, 2021).

• Growth Opportunities through the Creation of Web3 Products & Services Portfolios
  As a part of innovation mandates and future strategy roadmaps, the enablement of metaverse capabilities are on the radar for every large scale industrial software company.  Evolving traditional offerings (licenses, platforms consumption, etc.) in consideration of data products, as-a-Service offerings, and new monetizable use cases will be accelerated with Web3 capabilities (Digital Twins: From One Twin to the Enterprise Metaverse, 2022).  If the architectural foundations of the Industrial Metaverse are defined by novel approaches to data management and Digital Twins (as well as interconnected Digital Twins), companies can now begin fractionalizing or tokenizing offerings further to sub-components in a technology stack, rather than the singular offer a complete application, platform, or system.  Analytics and algorithms can now be decoupled from the digital container and construct it has been housed in, to be offered as standalone offerings that can be re-configured and re-utilized by other companies in very specific applications custom-suited to their needs.  This can only happen if algorithms themselves can be packaged to retain ownership, IP, and other parameters.  The future of the Industrial Metaverse is also one in which on top of the Digital Twin foundation, companies can start to build seamless data management layers that allow for the interactions of Digital Twins together (Digital Twins: From One Twin to the Enterprise Metaverse, 2022). 

• Growth Opportunities in the need to Accelerate Commercialization Speed-to-Market
  The Digital Twin algorithm market and the ability to simulate end-to-end real-world manufacturing, and design-iterate-test cycles to reduce speed-to-market, is now becoming critical if not vital to global supply chains, securing forward competitive advantage, and maintaining productive business operations.  For example, semiconductor chip manufacturing and the on-going “chip shortages” are reflected in it being the most capital and R&D intensive manufacturing process on the planet today (Chipmakers Are Ramping Up Production to Address Semiconductor Shortage. Here's Why That Takes Time, 2021).  1400 production steps, 26 weeks of manufacturing, and perfecting the process to increase throughputs and yields only intensifies this manufacturing time (assembly, test, package) (Chipmakers Are Ramping Up Production to Address Semiconductor Shortage. Here's Why That Takes Time, 2021).  As chip-integrated logic becomes standard in more consumer interface goods such as vehicles, demands continue to mount - and the ability to utilize Digital Twins to simulate optimized performance and production variables will become key (Morgan, 2022), (Rust & Lambert, 2022).  Other companies such as Boeing and Airbus have also made declarations that next generation aircraft will be designed and production will be accelerated in large part by leveraging high-fidelity Digital Twins that will also have the ability to test edge-case conditions (Johnson & Hepher, 2021).  

• Growth Opportunities in the Absence of In-House Skills & Talent Gaps
  Difficulties remain severe in hiring top-talent with blended backgrounds across industry combined with technology (otherwise known as the “OT-IT” abridged knowledge-base), as a result of skills gaps.  By 2030, the US could see 2.1 million unfulfilled jobs with the cost of such a gap costing potentially US $1T in 2030 alone (2.1 Million Manufacturing Jobs Could Go Unfilled by 2030, n.d.).  However, tactical and strategic campaigns now focus on re-skilling, education, and Diversity, Equity, and Inclusion (DEI) in order to attract and increase the penetration of women within the workforce to close talent gaps (Beyond Hiring: How Companies Are Reskilling to Address Talent Gaps, 2020).  Catapulting forward on the technological data science adoption curve but at the same time addressing the lack of data science in-house capabilities within organizations, along with the commoditization of analytics platforms, and in parallel the democratization of data science - will invariably lend itself to the creation of emergent business models related to the creation, sharing, and packaging of Digital Twin algorithms themselves (Cloud AutoML Custom Machine Learning Models, n.d.).

1. Industrial Metaverse Algorithm Market: Growth Opportunities

Positive drivers for growth include:

• Growth Opportunities via Use Cases driving New Value Propositions & Business Models
  Overtime, the Cryptographic Smart Digital Asset market including NFTs are experiencing a diversification of use cases that extend past digital art representation (Trends Driving NFTs Growth, n.d.).  At the moment, top NFT use cases primarily are focused on conferring ownership and some form of special access provisions and include uses cases of: avatars, profile pictures, one-of-one artworks, generative art, collectibles, photography, music, play-to-earn games, event tickets, membership passes, domain names, etc (Exmundo, 2022).  From the world of marketing, to the world of interactivity and experiences, NFT ownership allows for a consumer to be an investor, a brand shareholder, a member of a group - supporting altogether new business and profit models (How NFTs Create Value, 2021).  At the moment, the development of use cases specific value propositions and therefore companies is also driving hyper-market fragmentation which sets conditions for M&A activity to dominate growth (Non-Fungible Token (NFT) Market - 43% of Growth to Originate From APAC | Evolving Opportunities With Asynchronous Art Inc. & Binance Services Holdings Ltd. | Technavio, 2022).  Furthermore, in the servicing of specific use cases is also the creation of NFT ecosystems, marketplaces, and platforms to enable communities, exchanges, transactions, etc. (How NFTs Create Value, 2021).  The sheer numbers also point to positive growth: there has been an over 100% increase in the number of Ethereum-based NFT projects and collections in 2022. There are now a total of 80,300 NFT projects and collections on the Ethereum blockchain alone, up from 15,540 year over year (2022) (Zhuang, 2022).  

• Growth Opportunities through Ability to Design & Develop New Markets
  The Web3 pivot ascertained by the technological-convergence of business models relying on blockchain structures to represent exchanges of value of digital-physical commodities presents the ability to design entirely new markets (Susman, 2022).  In this way, NFTs are a tool for market design, fundamentally changing the market itself for digital assets with clear property rights (How NFTs Create Value, 2021).  Moreover, NFTs can be structured within Decentralized Autonomous Organizations (DAOs) to remove a central management organization (C-suite, Board, etc.) controlling all decisions related to the governance of the NFT and thereby the company tied to the NFT (Stelzner, 2022).   

• Growth Opportunities related to Tokenization of Intangible Digital Assets
  The largest growth opportunities related to Cryptographic Smart Digital Assets are in relation to the tokenization of intangible items.  As it relates to Digital Twin algorithms…

• Growth Opportunities with Increased NFT Awareness, Collections, Transactions, Investors
  Irrespective of nascent use cases, participation in NFT communities continues to grow as shown by numerous statistics.  

• NFT Transactions: NFT transactions have risen from $40.96 million in 2018 to $338.04 million in 2020. That’s an increase of over 8x in two years (Howarth, 2022). 

• NFT Buyers: Between 2020 and 2021, there has been a 450% increase in the number of unique NFT buyers. It went from a monthly volume of 10,000 buyers to 40,000 (NFT Market– Statistics 2021-2022, n.d.). 

• NFT Platforms: The OpenSea platform has at least 250,000 monthly active users selling and buying NFTs (NFT Market– Statistics 2021-2022, n.d.).

• NFT Collections: Among all the Ethereum-based NFT collections, BoredApeYachtClub still reigns as the most valuable NFT project, owned by 6,422 addresses, with its market cap reaching 1.05M ETH, equivalent to $3.18B. CryptoPunks and MutantApeYachtClub are the second and third biggest NFT series by valuation, respectively, worth 471.43K ETH and 456.23K ETH (Zhuang, 2022). 

• NFT in Wallets: Only 1.4% of the wallets held 1+ NFTs a year ago, in 2022 that number has gone up to 4.64% (Zhuang, 2022). 

• NFT Minting: Number of unique wallets participating in minting activity also witnessed a slight increase in average mints per wallet during this period at 3.65 mints per wallet, up from the previously reported average 3.16 mints (January to June 30, 2022) (NFT Sales: Where Did the ETH Go?, 2022). 

• NFT Demographics: 23% of Millennials collect NFTs: A survey conducted this year found that Millennials are the most likely generation to collect NFTs. 23% of Millennial respondents claim to collect NFTs as either a hobby or an investment (Howarth, 2022).  A March 2021 survey found that 20% of US adults are “somewhat interested” in acquiring, investing in, or trading NFTs. And 9% of US adults claimed to be “very interested” in getting involved with NFTs (Howarth, 2022).  Men are over 3x more likely to collect NFTs than women (Howarth, 2022).

• NFT Geographies: Americans make up over half of NFT investors (Howarth, 2022).

Positive drivers for growth include:

• Growth Opportunities via continued importance of IP Assets as a Source of Competitive Advantage for Companies & Countries
  In 2019, of the 103 industries identified as commodity exporting industries in the US, 76 were IP-intensive. These 76 industries accounted for $1.31 trillion or 79% of all US commodity exports in 2019 (Exports and Imports by U.S. IP-intensive Industries, n.d.).  In fact, 18 of the 20 top exporting industries were found to intensively use intellectual property.   Intangible assets account for 70% of firm assets and over 70% of equity value in the US; the figure is similar for other developed countries (Heer et al., 2022).  In the mid-1990s, investments in intangible capital (such as computer software and brand development) overtook tangible capital investments in the US; companies are investing more in innovation—the research, development, and commercialization of intangible assets—than they are in the purchase of existing equipment and machines to spur growth (Exports and Imports by U.S. IP-intensive Industries, n.d.).  IP-intensive industries accounted for 63 million jobs, or 44% of all US employment in 2019. About 33%, or more than 47 million jobs, were directly supported by IP-intensive industries (Exports and Imports by U.S. IP-intensive Industries, n.d.).  The software publishing industry is by far the largest services exporter among the IP-intensive industries, followed by computer system design, miscellaneous financial services (including portfolio management), and services related to sciences and technology (Exports and Imports by U.S. IP-intensive Industries, n.d.).

• Growth Opportunities of IP & Patents related to Machine Learning & Algorithms
  It is found in 40% of all AI-related patents studied using machine learning techniques and patent filings grew at an average rate of 28% every year from 2013-2016.  Mentions of deep learning in patent filings grew annually at an average rate of 175% from 2013-16. Mentions of neural networks grew annually at an average rate of 46% over the same period.  Computer vision, which includes image recognition (critical for self-driving cars, for instance), is the most popular functional application of artificial intelligence (AI). It was mentioned in 49% of all AI-related patents and grew annually at an average rate of 24% over the period 2013-16.  The other two top areas in functional applications are natural language processing (14% of all AI-related patents) and speech processing (13%). (The Story of Artificial Intelligence in Patents, n.d.)
  
  Top 3 application fields in which AI-related patents are filed:

• (1) Telecommunications: 51,273 patent applications (15%)

• (2) Transport: 50,861 patent applications (15%)

• (3) Life & Medical Sciences: 40,758 patent applications (12%)
  (The Story of Artificial Intelligence in Patents, n.d.)

Most AI-related patent filings are made at the patent offices in the United States of America (152,981 filings) and China (137,010). Both countries combine a high number of innovations in AI and potential as a market for AI-related inventions. (The Story of Artificial Intelligence in Patents, n.d.)

• Growth Opportunities related to Disruption of Patent Process & IP Management Market
  Patents provide a distinguished source of competitive advantage.  They protect intellectual property and allow for ownership of inventions and any products that are created as a result of the patent.  Patents also form a preventative mechanism that deters others from creating, making, or selling a product that utilized patent protected invention - giving legal recourse to infringement.  It forms a mechanism to confer legal rights, ownership, licensing, and selling of patents (inventions).  Once a patent is approved, legal rights to a patent are owned for 20 years (How Long Does It Take to Get a Patent: Everything You Need to Know, n.d.).  But just as Bitcoin and cryptocurrencies now allow for decentralization and disintermediation in the realm of the financial system and financial economy, NFTs that package Digital Twin algorithms will also allow for world-wide ownership, exchange, legal rights, provisions, agreements, selling, and perpetual licensing or royalties or fees or commissions.  This creates disruption in an industry and a market that has been largely untapped and untouched. 
  
  As it currently stands, out of the 1.2 million registered lawyers in the US, only 31,000 are experts in patents (How Long Does It Take to Get a Patent: Everything You Need to Know, n.d.).  This bottleneck of legal filing and then later review by the US Patent and Trademark Office (USPTO) means patents can sometimes take 3 to 4 years before successful approval (How Long Does It Take to Get a Patent: Everything You Need to Know, n.d.).  Patents also need to be filed in all countries in which patent protection is sought.  Between legal consultations, filing fees, draft patent applications and more, the US national average cost of a utility patent is $56,525 with $12,100 going to the USPTO and $44,425 going to patent attorneys (How Much Does A Patent Cost?, n.d.).

• Growth Opportunities related to IP Management Platforms & Software
  The increased acceptance of outsourcing IP management services by various large enterprises is contributing to the growth of the market. Furthermore, in-house IP management is a vast resource-demanding task that requires heavy investments. Outsourcing IP management helps SMEs and large enterprises in productivity growth, profitability improvement, protecting revenues, and cost reduction. Thus, the growing emphasis of enterprises on protecting innovations and increasing revenues, and streamlining business operations is expected to drive the intellectual property services segment, thereby fueling the market growth over the forecast period. (Intellectual Property Management Software Market Report 2028, n.d.)

References