[co-authors:James E. Malackowski and Eric T. Carnick]
Redefining Asset Recognition and Protection in the Age of Artificial Intelligence
The artificial intelligence revolution presents the legal profession with its most significant practice development opportunity since the emergence of the internet. AI spending across hardware, software, and services reached $279.22 billion in 2024 and is projected to grow at a compound annual growth rate of 35.9% through 2030, reaching $1.8 trillion.[i] AI is rapidly enabling unprecedented efficiencies, insights, and capabilities in industry. The innovations underlying these benefits are often the result of protectable intellectual property (IP) assets. The ability to raise capital and achieve higher valuations can often be traced back to such IP. According to data from Carta, startups categorized as AI companies raised approximately one-third of total venture funding in 2024. Looking only at late-stage funding (Series E+), almost half (48%) of total capital raised went to AI companies.[ii] Organizations that implement strategic AI IP management can realize significant financial benefits.
At the same time, AI-driven enhancements have introduced profound industry risks, e.g., disruption of traditional business models; job displacement and labor market reductions; ethical and responsible AI concerns; security, regulatory, and compliance challenges; and potentially, in more extreme scenarios, broad catastrophic economic consequences. Such risks are exacerbated by the tremendous pace of AI development and adoption, in some cases surpassing societal understanding and regulatory frameworks. According to McKinsey, 78% of respondents say their organizations use AI in at least one business function, up from 72% in early 2024 and 55% a year earlier.[iii]
This duality—AI as both a catalyst and a disruptor—is now a feature of the modern global economy. There is an urgent need for legal frameworks that can protect AI innovation, facilitate the proper commercial development and deployment of AI-related IP, and navigate the risks and challenges posed by this new technology. Legal professionals who embrace AI as IP™ will benefit from this duality. Early indicators suggest significant advantages for legal practitioners who develop specialized AI as IP expertise, while traditional IP practices may face commoditization pressures.
This article addresses that need by presenting a comprehensive framework for AI intellectual property management, grounded in established legal principles and empirical market data. We demonstrate that AI systems constitute recognizable intangible assets under International Financial Reporting Standards, require multi-faceted protection strategies, and create substantial opportunities for legal practitioners willing to develop specialized expertise in this emerging field.
THE NATURE OF AI SYSTEMS AS INTELLECTUAL PROPERTY
AI systems are composed of three foundational components: data, compute, and algorithms. These components can be viewed through the lens of IP, offering distinct opportunities for asset recognition, protection, valuation, and monetization.
Data
Data encompasses the information AI is trained on or information the AI retrieves from the web to augment its responses (Retrieval Augmented Generation, or RAG). Among the three foundational components of AI, data often stands out as the most fundamental and valuable. High-quality, relevant, and contemporaneous data can significantly reduce reliance on complex algorithms and expensive compute resources. In many cases, data can drive superior AI performance, making it the cornerstone of AI value creation.[iv]
The demand for data by AI platforms has been compared to a “gold rush.”[v] Ilya Sutskever, co-founder and former Chief Scientist at OpenAI, stated that “data is the fossil fuel of AI.”[vi] The high demand for data by AI platforms speaks to its value. Data for AI can come in various forms: text, audio, video, images, numerical data, and sensor data, among other types. Such data can be proprietary or public-facing. Depending on the data asset and a company’s business objectives, such data may be protected in a variety of ways.
Data can be protected as a trade secret. The Uniform Trade Secrets Acts (UTSA) defines trade secrets as information that (i) “Derives independent economic value, actual or potential, from not being generally known to, and not being readily ascertainable by proper means by, other persons who can obtain economic value from its disclosure or use” and (ii) “Is the subject of efforts that are reasonable under the circumstances to maintain its secrecy.”[vii] Thus, a trade secret is information with economic value due to its secrecy and the owner's efforts to keep it confidential. Trade secrets can encompass technical information, such as manufacturing processes, pharmaceutical testing data or commercial information, such as business strategies, supplier and client lists, and distribution methods.
Data can also be creative works, like books, articles, and artistic works. Here, data can be protected under copyright law. For creative works, copyright protection is the foundation for protecting the author's rights and fostering a vibrant creative environment. It allows authors and artists to control how their work is reproduced, distributed, displayed, and adapted, ensuring that they can reap the rewards of their creative labor. This protection is rooted in the idea that by offering such rights, creators are incentivized to produce and share new works, enriching the public good.
AI platforms have executed hundreds of licensing deals with book, news, and other media publishers, as well as creators of audio and/or visual works, to secure rights to creators’ content. We discuss these deals further in the sections below.
Compute
Compute refers to the underlying computational power that enables AI to train models, process data, and perform calculations.[viii] Compute is often measured in terms of floating-point operations per second (FLOPS), which is the number of calculations that a processor can do in a second.[ix] AI innovations that solve technical problems through specific implementations and produce improvements in system performance or efficiency can qualify for patent protection or could be protected as a trade secret. These practical applications and technical improvements often relate to the hardware (e.g., specialized chip) and physical infrastructure (i.e., datacenters) that house the computing hardware, storage, and networking equipment.[x]
Algorithms
At a high level, algorithms are a set of instructions or mathematical rules that AI systems follow to perform tasks like learning, predicting, or generating outputs.[xi] Algorithms may be protected by multiple types of IP. Algorithms may start as a conceptual framework. Patents can cover the functional aspects of an algorithm if they are new, non-obvious, and tied to a specific application, e.g., not purely an abstract mathematical formula. The source code implementing an algorithm can be eligible for copyright protection. This protects the specific expression of the algorithm in software code, not the functionality or any abstract logic. Trade secrets may protect confidential algorithms that cannot be reverse-engineered, like specific model weights or parameters.
Downstream Applications of AI
Downstream applications of AI involve combining AI systems with other products are services, such as mobile apps, cloud platforms, hardware, or physical products. IP protection depends on the components involved and how they are integrated, but patents, trade secrets, and copyrights may all be relevant. For example, a system and method for an autonomous vehicle may combine sensor data, software code for processing the data, and physical components of the vehicle.
To establish AI innovations as financially recognizable intellectual property, we must demonstrate that they meet established criteria for intangible asset recognition. International Accounting Standard 38 (IAS 38) provides the globally recognized framework for intangible asset identification and recognition, establishing four key criteria that AI innovations must satisfy.[xii]
The identifiability requirement mandates that intangible assets be separable from goodwill and arise from contractual or legal rights. AI innovations clearly satisfy this requirement through their technical distinctiveness and the ability to license, transfer, or independently commercialize specific AI components. Training datasets can be licensed separately from applications, model architectures can be transferred independently of implementation systems, and concrete applications of AI algorithms can be patented as distinct technical innovations.
The control criterion requires that organizations exercise legal and practical control over the asset's future economic benefits. AI developers typically maintain control through technical measures (access controls, encryption, proprietary interfaces), legal protections (patents, trade secrets, employment agreements), and operational controls (restricted access to training processes, proprietary methodologies). This multi-layered control structure satisfies the IAS 38 control requirement while providing practical protection against unauthorized use.
The measurement requirement demands that the asset’s cost be reliably determinable and that future economic benefits be probable. AI development costs are typically well-documented, including computational expenses, personnel costs, data acquisition expenses, and infrastructure investments. The probability of future economic benefits is demonstrated through AI systems' ability to improve operational efficiency, enable new revenue streams, reduce costs, or create competitive advantages (at least in certain industries) that translate into measurable financial returns.
The monetization criterion is satisfied through AI innovations' proven ability to generate economic value through internal use, external licensing, technology transfer, or enhanced business valuations. Recent market data demonstrates substantial monetization potential, with Dow Jones (i.e., publisher of the Wall Street Journal) and OpenAI agreeing to a license worth more than $250 million over five years.[xiii] Likewise, Amazon entered into a multiyear licensing deal to pay The New York Times at least $20 million a year to use a broad range of content from the media company.[xiv]
Life Sciences and Healthcare: High-Stakes Innovation
The life sciences sector presents exceptional opportunities for AI intellectual property development due to the high-value applications, substantial development investments, and regulatory requirements that create natural protection barriers. AI innovations in healthcare often involve life-critical applications where performance advantages translate directly into improved patient outcomes and substantial commercial value.
Drug discovery applications represent the frontier of AI patent protection in healthcare. Machine learning algorithms may identify promising drug compounds, predict molecular interactions, or optimize clinical trial designs that solve specific technical problems while creating substantial commercial value. Patent protection for these innovations, which were achieved through significant human contribution, may provide market exclusivity during the lengthy drug development process, potentially generating billions in licensing revenue.
Diagnostic imaging applications create additional patent opportunities through technical innovations that improve accuracy, reduce processing time, or enable new diagnostic capabilities. AI systems that enhance medical image resolution, detect subtle pathological indicators, or integrate multiple imaging modalities solve concrete technical problems while improving patient care.
Clinical decision support systems present complex IP protection challenges that require coordinated patent, trade secret, and regulatory strategies. The algorithms that process patient data to generate treatment recommendations, the knowledge bases that encode medical expertise, and the interfaces that present information to healthcare providers each require different protection approaches.
Regulatory compliance and validation present unique opportunities for AI IP protection in healthcare. The methodologies used to demonstrate AI system safety and efficacy, approaches for maintaining regulatory compliance as systems evolve, and frameworks for validating AI performance in clinical settings represent valuable intellectual property that facilitates market entry and competitive positioning.
Manufacturing and Industrial Applications: Operational Excellence
Manufacturing applications create substantial AI IP opportunities through systems that optimize operations, improve quality, and reduce costs. The tangible nature of manufacturing improvements makes patent protection particularly valuable, while the operational knowledge embedded in AI systems creates significant trade secret opportunities.
Predictive maintenance systems may represent high-value patent opportunities through innovations that prevent equipment failures, optimize maintenance scheduling, and extend asset lifecycles. AI algorithms that analyze sensor data to predict failures, optimize maintenance interventions, and balance maintenance costs against downtime risks solve concrete technical problems while generating measurable cost savings.
Quality control and defect detection systems may create additional patent opportunities through computer vision applications that identify product defects, classify quality issues, and optimize production processes. The technical innovations that enable accurate defect detection, robust performance under varying conditions, and integration with existing manufacturing systems may provide strong foundations for patent protection.
Supply chain optimization applications present complex IP challenges that span multiple protection mechanisms. The algorithms that optimize inventory levels, predict demand fluctuations, and coordinate multi-tier supply networks may warrant patent protection, while the data sources, supplier relationships, and operational knowledge that enable superior performance require trade secret protection.
Safety monitoring and incident prediction systems create critical IP assets through innovations that prevent accidents, ensure regulatory compliance, and protect human safety. AI systems that monitor workplace conditions, predict safety incidents, and optimize safety protocols solve important technical problems while creating substantial liability reduction value.
AI as IP in Industry Broadly
Numerous other industries are evolving due to AI technologies. Transportation was one of the first sectors to begin this evolution through AI innovations like autonomous vehicles, traffic and fleet management and optimization, and predictive logistics. In agriculture, AI systems can provide more precise farming and crop optimization, better pest and disease detection, and help build more sustainable, environmentally friendly techniques. AI systems in the energy and utility sector provide more intelligent infrastructure, optimizing the energy grid usage, dynamic load balancing, and forecasting. The broad applicability of AI across numerous industries and types of IP creates a robust environment for the valuation and monetization of AI as IP, as described below.
Understanding AI Asset Valuation in M&A Contexts
The rapidly evolving AI marketplace creates unique valuation challenges and opportunities that require sophisticated analytical approaches. As described above, the share of total venture capital flowing to AI companies is significant. Recent market data also reveals that AI companies command substantial premiums over traditional software companies, with a median EV/Revenue multiple of 29.7x for AI companies, compared to 3.0x for traditional software companies.[xv] [xvi] This premium reflects both the growth potential of AI technologies and the scarcity of proven AI assets in the marketplace. AI M&A transactions demonstrate consistent premium valuations for companies with strong intellectual property portfolios, with strategic acquirers paying premiums for companies with defensible AI IP positions. These premiums reflect acquirers' recognition that protected AI IP assets provide sustainable competitive advantages and reduced integration risks. Companies with patent-protected AI innovations can demonstrate more predictable revenue streams, while trade secret protection provides cost advantages and competitive moats that support premium valuations.
Licensing Revenue Generation and Market Development
AI licensing presents substantial revenue opportunities. The modular nature of AI components (data, compute, and algorithms) enables flexible licensing arrangements that can generate significant revenue streams while accelerating market adoption and technology dissemination.
Content licensing represents a significant opportunity in the AI licensing market. Although this market is still in development, the key building blocks, e.g., willing buyers and sellers, pricing mechanisms, market intermediaries, and executed transactions, are already in place. According to ResearchandMarkets.com, the market for AI training data sets is expected to grow at a CAGR of 27.7% from $2.82 billion in 2024 to $9.58 billion in 2029.[xvii] Individual deals between major publishers/content creators and AI companies have reached upwards of $250 million.[xviii] Despite the current legal uncertainty surrounding fair use, which has significantly depressed these license payments and created headwinds for licensors, these arrangements demonstrate the substantial value of high-quality training data. This substantial value is also exhibited by the many intermediaries and technology platforms that have entered the market to facilitate such licensing arrangements, including Personal Digital Spaces (PDS), Tollbit, Human Native, and the Copyright Clearance Center (CCC), among many others. These intermediaries and technology platforms enable content creators to catalog, track, license, and/or monetize their content.
Figure 1 illustrates the growth of the AI content licensing market. Specifically, the data reflects license agreements between AI platforms and various publishers or content creators for textual works.
Figure 1: AI Content Licensing Deals for Textual Works By Quarter
(See Appendix 1)
In addition to content, technology licensing opportunities extend across multiple other AI component categories. The broad applicability of AI as IP in many different industries can allow companies to monetize core innovations while maintaining competitive advantages in their specific industry. Cross-licensing arrangements will likely become increasingly important as AI patent portfolios mature and potential infringement risks increase. Companies with strong AI patent positions can leverage those assets to gain access to complementary technologies while avoiding costly litigation and enabling faster innovation cycles.
Strategic Partnerships and Joint Development Arrangements
AI intellectual property creates unique opportunities for strategic partnerships that combine complementary capabilities while sharing development costs and market risks. The interdisciplinary nature of AI development and the substantial investments required for successful commercialization make partnership arrangements increasingly attractive for companies seeking to accelerate AI innovation.
Technology development partnerships enable companies to combine AI expertise with domain knowledge, creating innovations that neither partner could develop independently. These arrangements require careful IP allocation agreements that protect each partner's contributions while enabling shared ownership of jointly developed innovations.
Data-sharing partnerships present complex IP challenges that require sophisticated legal frameworks. Privacy regulations and competitive concerns often limit data-sharing arrangements. Successful partnerships develop frameworks that enable data utilization without compromising competitive positions or regulatory compliance.
Commercialization partnerships create opportunities for AI companies to leverage established market channels while enabling traditional companies to incorporate AI capabilities without developing internal expertise. These arrangements require clear IP licensing terms that protect AI innovations while providing partners with adequate rights to commercialize jointly developed products.
Research collaborations with academic institutions provide access to cutting-edge research while creating complex IP ownership issues. Successful arrangements establish clear frameworks for IP ownership, publication rights, and commercialization opportunities that enable continued research while protecting commercial interests.
IMPLEMENTATION FRAMEWORK FOR LEGAL PRACTITIONERS
Developing AI IP Expertise and Service Offerings
The emergence of AI as intellectual property creates substantial opportunities for legal practitioners willing to develop specialized expertise in this rapidly evolving field. The complexity of AI technologies and the multifaceted nature of IP protection requirements create natural barriers to entry that enable practitioners with appropriate expertise to command premium fees while building sustainable competitive advantages.
Technical competency requirements for AI IP practice extend beyond traditional IP law knowledge to include an understanding of machine learning concepts, data science methodologies, and AI system architectures. Practitioners need not become technical experts but must develop sufficient understanding to communicate effectively with AI developers, assess the patentability of AI innovations, and draft protection strategies that address the unique characteristics of AI technologies.
Industry specialization creates additional opportunities for practitioners to develop niche expertise that commands premium rates. AI applications in healthcare, manufacturing, transportation, and other regulated industries require specialized knowledge of industry regulations, market dynamics, and commercial requirements that enable more effective IP strategies.
Client development strategies for AI IP practice should focus on proactive engagement with companies developing AI capabilities rather than reactive responses to traditional IP needs. The rapid pace of AI development and the novelty of AI IP issues create opportunities for practitioners to establish relationships early in the innovation process, enabling more comprehensive and effective IP protection strategies.
Service Integration and Partnership Opportunities
The multidisciplinary nature of AI IP protection creates opportunities for law firms to develop integrated service offerings that address technical, legal, and business aspects of AI innovation. Successful AI IP practices combine traditional legal expertise with technical analysis, business strategy, and market intelligence capabilities.
Technical partnership arrangements enable law firms to access specialized AI expertise without developing internal capabilities. Collaborations with AI consultants, technical experts, and industry specialists can provide the technical competency needed for effective AI IP practice while maintaining focus on legal service delivery.
Business development partnerships with accounting firms, management consultants, and investment banks create opportunities for integrated service offerings that comprehensively address AI development, IP protection, and commercialization needs. These partnerships enable law firms to participate in broader AI development initiatives while providing specialized IP expertise.
International collaboration becomes particularly important for AI IP practice given the global nature of AI development and the need for coordinated protection strategies across multiple jurisdictions. Law firms that develop international partnership networks for AI IP protection can provide comprehensive services while accessing global markets.
Risk Management and Professional Development
An AI IP practice presents unique professional risks that require careful management and specialized expertise. The novelty of AI technologies and the rapid pace of development create potential liability exposures that practitioners must understand and address through appropriate risk management strategies.
Professional liability considerations for AI IP practice include potential exposure for inadequate protection strategies, missed patent opportunities, and failure to identify trade secret misappropriation risks. Practitioners must develop appropriate expertise and quality control procedures to minimize these risks while providing effective representation.
Continuing education requirements for AI IP practice exceed traditional IP law professional development needs. The rapid evolution of AI technologies and the emerging legal frameworks requires ongoing investment in technical education, legal development, and market knowledge that enables effective practice.
Quality control procedures for AI IP practice should include technical review processes, specialized documentation requirements, and enhanced due diligence procedures that address the unique characteristics of AI innovations. These procedures help ensure effective representation while minimizing professional liability risks.
REGULATORY AND COMPLIANCE CONSIDERATIONS
Emerging Regulatory Frameworks and Compliance Requirements
The regulatory landscape for AI is evolving rapidly, with 59 AI-related regulations introduced by US federal agencies in 2024, representing a doubling from the previous year.[xix] This regulatory acceleration creates both compliance challenges and IP protection opportunities that require coordinated legal strategies.
The European Union's AI Act represents the most comprehensive AI regulatory framework currently in force. The law establishes risk-based classifications for AI systems and imposes substantial compliance requirements for high-risk applications. These regulations create both barriers to market entry and opportunities for companies with compliant AI systems to gain competitive advantages through IP protection.[xx]
Sectoral regulations in healthcare, financial services, automotive, and other industries create additional compliance requirements that impact AI IP strategies. The intersection of AI innovation with existing regulatory frameworks often creates opportunities for patent protection through technical solutions that address regulatory requirements while improving system performance.
Data protection regulations, including General Data Protection Regulation (“GDPR”), California Privacy Protection Act (“CCPA”), and emerging privacy frameworks, significantly impact AI IP strategies through restrictions on training data use, requirements for algorithmic transparency, and limitations on automated decision-making. These regulatory constraints create opportunities for companies that develop privacy-preserving AI technologies while imposing additional complexity on IP protection strategies.
International regulatory coordination remains limited, creating opportunities for companies that develop AI systems capable of meeting multiple regulatory requirements while establishing barriers to entry for competitors lacking such capabilities. IP protection for regulatory compliance technologies becomes increasingly valuable as regulatory requirements proliferate and compliance costs increase.
Ethical AI and Responsible Innovation
The growing emphasis on ethical AI and responsible innovation creates new categories of IP protection opportunities while imposing additional requirements on AI development and deployment. Companies that develop technical solutions to ethical AI challenges can gain competitive advantages while building sustainable IP portfolios.
Bias detection and mitigation technologies present significant IP opportunities through innovations that identify and correct discriminatory outcomes in AI systems. The technical challenges of measuring bias, developing correction algorithms, and maintaining system performance while ensuring fairness create substantial opportunities for IP protection.
Explainability and interpretability technologies create additional IP opportunities through innovations that make AI decision-making processes transparent and understandable. The technical challenges of developing interpretable AI systems without sacrificing performance create IP opportunities while addressing regulatory requirements and market demands.
Privacy-preserving AI technologies may represent high-value patent opportunities through innovations that enable AI development while protecting individual privacy. Techniques such as differential privacy, federated learning, and homomorphic encryption solve technical challenges while addressing regulatory requirements and competitive concerns.
Robustness and security technologies create critical IP assets through innovations that protect AI systems against adversarial attacks, ensure reliable performance under varying conditions, and maintain system integrity. The increasing deployment of AI systems in critical applications creates substantial market demand for these technologies while providing strong foundations for IP protection.
FUTURE OUTLOOK AND STRATEGIC IMPLICATIONS
Market Evolution and Competitive Dynamics
The AI intellectual property landscape will continue evolving rapidly as the technology matures and market structures develop. Early indicators suggest increasing consolidation around platform providers while creating opportunities for specialized applications and integration technologies.
Patent landscape development indicates accelerating filing activity in AI-related technologies, with potential for significant patent thickets in core AI technologies. This evolution creates both opportunities and risks for companies developing AI innovations, requiring sophisticated patent strategies that balance protection needs with freedom to operate considerations.
Trade secret protection will likely become increasingly important as AI technologies mature, and competitive advantages shift from core algorithms to implementation expertise, operational knowledge, and data assets. Companies that develop comprehensive trade secret protection programs will be better positioned to maintain competitive advantages as AI technologies become more widely available.
Licensing market development suggests increasing standardization of AI licensing arrangements and the emergence of patent pools for core AI technologies. These developments will create opportunities for more efficient technology access while requiring strategic decisions about participation in collaborative arrangements versus independent development.
Legal Practice Evolution and Opportunity Assessment
The legal profession's response to AI intellectual property challenges will determine both the development of effective protection frameworks and the distribution of economic opportunities created by AI innovation. Early indicators suggest significant advantages for practitioners who develop specialized expertise, while traditional IP practices may face commoditization pressures.
Practice area development opportunities extend beyond traditional IP law to include AI governance, regulatory compliance, technology transactions, and strategic planning services. Law firms that develop comprehensive AI legal capabilities will be better positioned to serve clients' evolving needs while capturing premium fee opportunities.
Client relationship evolution suggests increasing integration of legal services with business strategy and technology development activities. AI IP practice requires closer collaboration with technical teams and business leaders than traditional IP practice, creating opportunities for deeper client relationships while requiring enhanced technical competency.
Competitive positioning will increasingly depend on technical expertise, industry specialization, and integrated service capabilities rather than traditional legal credentials alone. Practitioners who develop appropriate expertise and service offerings will benefit from favorable supply-demand dynamics, while others may face displacement by more specialized competitors.
CONCLUSION
The emergence of artificial intelligence as intellectual property represents a significant market opportunity for both innovative organizations and the legal profession. Companies that implement strategic AI IP management can realize substantial competitive advantages, premium valuations, and new revenue streams through licensing and technology transfer. Conversely, organizations that fail to protect their AI innovations face significant risks, including competitive disadvantage, reduced valuations, and missed monetization opportunities.
The legal profession stands at a critical juncture in responding to these emerging needs. The complexity of AI technologies, the multi-faceted nature of protection requirements, and the rapid pace of market development create natural opportunities for practitioners who develop appropriate expertise while posing threats to those who fail to adapt to evolving client needs.
The framework presented in this article provides a foundation for both AI innovators and legal practitioners to navigate this rapidly evolving landscape. By recognizing AI innovations as intellectual property, implementing comprehensive protection strategies, and developing appropriate expertise, organizations can capture the substantial opportunities created by the AI revolution while mitigating associated risks.
The stakes are substantial, and the window for establishing leadership positions is limited. Organizations and practitioners who act decisively to develop AI IP capabilities will be well-positioned to benefit from this new technology, while those who delay may find themselves at permanent competitive disadvantages in an AI-driven economy.
Future research should focus on developing more sophisticated valuation methodologies for AI assets, creating standardized licensing frameworks for AI technologies, and establishing best practices for AI IP portfolio management. As the market matures and regulatory frameworks develop, these areas will become increasingly important for effective AI IP practice.
The AI intellectual property revolution has begun. The question is not whether AI will change how we create, protect, and monetize intellectual property, but whether organizations and practitioners will position themselves to lead or follow in this changing environment.
Appendix 1
ACKNOWLEDGEMENT
The authors would like to acknowledge and thank David Ngo, Senior Analyst, Ocean Tomo, for his research and contribution.
[1] Grand View Research. (2024). Artificial Intelligence (AI) Market Size & Trends Analysis Report. Available at: https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market
[1] Carta. (2024). Five Charts Showing How AI is Dominating the Venture Fundraising Market. Available at: https://carta.com/data/ai-fundraising-trends-2024/
