Trends and Extrapolations in the Evolving AI Landscape in 2024

Ali Arsanjani
17 min readDec 30, 2023

In this article we will cover 15 business focused/ business level trends and extrapolations, then 15 technically oriented ones, and finally a call to action with 10 recommended action items.

Section 1: A Business Perspective in 15 Points

As we approach the horizon of 2024, the field of Artificial Intelligence stands poised for a period of significant transformation and widespread impact. It is within this context that I will explore and analyze the trends and extrapolations [“predictions”] for 2024 in the AI space from two perspectives, first a business oriented perspective and then a more detailed, more technical perspective in section two where I will cover an extended, more technically oriented outline of the key trends in motion for 2024.

AI is experiencing a transformative phase, influenced by a myriad of factors ranging from legal challenges to the rapid advancement of AI technologies. This transformation is characterized by an intricate interplay between evolving legal complexities, technological evolutions and progressions, and shifts in business strategies and emergence of new business models.

At the forefront of these changes are notable legal battles, such as the lawsuit initiated by The New York Times against a Microsoft and OpenAI . This case has set a precedent in the AI industry, particularly concerning the use of copyrighted content for AI model training. It has sparked a series of negotiations among prominent publishers like News Corp., IAC, and Gannett with OpenAI. These discussions are not just about reaching agreements on content usage but are also shaping new models for data licensing in AI. The outcome of these negotiations is poised to redefine how AI models access and utilize third-party content, emphasizing the need for transparency and ethical practices in AI development.

In response to these legal challenges, the AI community is exploring a variety of data licensing models. These range from exclusive agreements to tiered licensing and royalty-based arrangements, each with its implications for the future of AI content utilization. Alongside these developments is a growing demand for increased transparency in AI model training. Publishers are advocating for clear disclosures regarding data sources and training methodologies, pushing the industry towards technological measures that prevent unauthorized use of content. Google has proposed a Watermarking technology coming out of DeepMind that addresses concerns with image generation. This shift towards more secure and ethical AI development practices is a crucial aspect of the ongoing transformation in the AI landscape.

The negotiations and legal challenges are likely to result in a significant shift in power dynamics within the AI ecosystem. Publishers, armed with new leverage, might secure better revenue deals and exert a more substantial influence on AI development. This could lead to the emergence of standardized practices for copyright and licensing in AI training data, benefiting both AI developers and content providers.

Looking at the technological advancements, there’s an undeniable growth in the sophistication of AI models and technologies. Developments like Google’s Gemini Family of multimodal models all the way to Mistral-Medium, Mixture-of-Experts and other models that are emerging by the week, and the implementation of state-of-the-art frameworks such as the LLMCompiler are testament to this diverse progression. These advancements, coupled with the rise of multimodal models and complex agent-to-agent interactions mediated by larger LLMs, set new standards in efficiency and capability in the AI domain.

Beyond this, the scope of AI applications is expanding significantly, moving beyond digital-only realms to encompass physical applications in IoT, edge computing, and robotics. This broadening of AI’s scope illustrates its growing penetration across various domains, from the digital world of enterprise, games, AR-VR, and the metaverse, to the physical world of IoT and robotics.

As we navigate through these changes, the AI landscape is on the cusp of significant developments in content licensing, data transparency, and model development. The relationship between AI developers and publishers is undergoing a pivotal transformation, which, coupled with the advancements in AI technologies and applications, is set to shape the future trajectory of the entire industry.

Analysis and Extrapolations for 2024: [Image generated by Author]

B1 Legal Challenges and Copyright Dynamics

One of the most significant developments in the AI sector is the legal battle initiated by The New York Times against Microsoft and OpenAI. This lawsuit represents a critical juncture in the use of copyrighted content for training AI models. It has spurred a wave of negotiations among major publishers, such as News Corp., IAC, and Gannett, with OpenAI. These discussions are pivotal, potentially reshaping the landscape for content licensing in AI. They highlight the necessity for transparency in AI development and raise questions about ethical practices in the utilization of third-party content. There is a need to de-risk AI applications possibly improper use of copyrighted data that will drive specialized models that are tuned on a company’s own data with adequate data provenance traceability.

B2 Data Licensing Models and Transparency

In response to these legal challenges, the AI community is actively exploring various data licensing models. These range from exclusive agreements to tiered licensing and royalty-based arrangements. The specific terms of these licensing models are crucial as they determine how AI models will access and use external content in the future. Alongside this, there’s a growing demand for AI model transparency. Publishers are pushing for clear disclosures regarding data sources and training methodologies, leading to a push for technological safeguards against unauthorized content usage.

B3 Power Dynamics and Industry Standardization

The ongoing negotiations and legal challenges are likely to cause a significant shift in power dynamics within the AI ecosystem. Publishers, equipped with new leverage, might secure better revenue deals and gain more influence over AI development. This shift could lead to the emergence of standardized practices for copyright and licensing in AI training data, which would benefit both AI developers and content providers.

B4 Technological Advancements in AI Models

The advancement of AI models like Google’s Gemini Family of multi-modal models, Mistral-Medium, a variety of mixture-of-experts, and the implementation of frameworks such as the LLMCompiler reflect the growing sophistication in AI technologies. These developments are setting new benchmarks in efficiency and capability, fostering a competitive and innovative AI marketplace. Additionally, the rise of multimodal models and agent-to-agent interactions mediated by larger LLMs showcases the increasing complexity and capability of AI systems.

B5 Emerging Business Models in AI

The negotiations between publishers and AI developers like Google and OpenAI are paving the way for innovative revenue streams. This could include content subscriptions for specific AI models or offering curated datasets. Moreover, collaborations between publishers and AI developers could lead to the development of AI-powered content creation tools and personalized news delivery systems.

B6 Expanding Scope of AI Applications

LLMs are expanding their scope from digital-only applications to broader domains, including IoT, edge computing, and robotics. This expansion signifies a penetration of AI into a multidimensional spectrum, covering digital realms like enterprise domains, games, AR-VR, and the metaverse, to physical applications in IoT and Robotics.

B7 AI in the Legal and Regulatory Sphere

The evolving legal landscape, including landmark AI regulations like President Biden’s Executive Order on Safe, Secure, and Trustworthy AI, and the EU AI Act, is prompting companies to meticulously document model development. Tools like those in Vertex AI, and Weights & Biases and others, are becoming essential for tracking data lineage and managing AI model workflows.

As we navigate these evolving landscapes, the AI industry stands at a critical crossroads. The relationship between AI developers and publishers is undergoing transformative changes, with implications that will shape the future of AI across various industries. Coupled with advancements in AI technologies and applications, the AI landscape is set for significant developments in content licensing, data transparency, and model development.

B8 The Two-Tiered Model Market: Diverging Costs and Resource Constraints

While horizontal models may become more affordable, the cost of vertical models and the computational resources required for their training and fine-tuning are likely to increase. This could lead to challenges in terms of access to these advanced models, necessitating creative solutions and resource optimization strategies that optimize between cost, latency, model quality and specialization in addition to ease of use, traceability, compliance, demonstrable groundedness.

B9 Heightened Scrutiny and the Rise of AI Literacy

The uncritical acceptance of AI pronouncements will give way to a more discerning public, demanding rigorous validation of the promised value propositions. Businesses will face increased pressure to demonstrate tangible results, shifting the focus from mere technological novelty to demonstrably beneficial applications.

B10 Navigating the Content Conundrum: Data-quality, “dumb-data”, Deepfakes and the Truth Imperative

The proliferation of AI-generated content, encompassing both genuine and fabricated data, necessitates the development of sophisticated detection and mitigation tools. Deepfakes pose a significant threat to the dissemination of accurate information, and robust techniques for identifying and countering these fabrications will be crucial in safeguarding the integrity of the information landscape.

The other side of GenAI is the production of low-grade, low-quality content; e.g. to fill in content and look sophisticated. This firehose of low quality data that can now be easily generated is a serious challenge to the efforts of decreasing entropy, disinformation, misinformation and just raising the bar on data quality.

Content should be curated to achieve higher quality data for data preparation pipelines and instances, but its misuse, deliberately or inadvertently will result in low-quality content explosion at ‘best’ to flooding channels with so much data, the only way to consume will be to use a GenAI at the other end to distill the information and decrease its entropy content — i.e., make it more useful as information, as knowledge.

B11 The AI Wars: Niche Battles and the Emergence of New Contenders

The established players in the AI landscape will face increasing competition from smaller, agile companies specializing in niche domains. This “AI gold rush” will lead to the development of a diverse range of industry-specific and domain-focused models, catering to ever-more specialized needs and applications.

B12 Data: The Fuel of AI Progress and the Challenge of Equitable Access

Organizations will fiercely guard their unique data sets, recognizing them as the vital fuel for their AI engines. Advancements in synthetic data generation, differential privacy, and federated learning will play a crucial role in enabling secure and collaborative data utilization, while addressing concerns surrounding data ownership and equitable access.

B13 Democratizing AI: Empowering Individuals and Organizations

As AI tools and platforms become more user-friendly and accessible, the playing field will be leveled, enabling individuals and organizations to leverage this powerful technology for their own purposes. This democratization of AI has the potential to unlock a wave of innovation and creativity across diverse fields.

B14 The Human-AI Partnership: A Collaborative Future

It is crucial to emphasize that AI is not intended to replace human intelligence, but rather to augment and complement it. The future of work and problem-solving lies in a synergistic partnership between humans and AI, where each leverages its unique strengths to achieve remarkable outcomes.

B15 Every Company will be an AI-Driven or At Least a AI-Marketing Driven Company

Evolving Consumer Expectations and Market Competitiveness will drive this trend. The increasing public scrutiny and literacy regarding AI imply that consumers and businesses are becoming more discerning about the technology’s practical applications. This evolution in expectations is pushing companies to adopt AI-driven strategies, especially in marketing, to demonstrate innovation, efficiency, and a cutting-edge understanding of customer needs. AI’s ability to analyze complex consumer data and provide actionable insights makes it indispensable for creating targeted, effective marketing campaigns.

Industry-Specific AI solutions and data utilization will further enable the AI-driven messaging. The emergence of specialized AI tools tailored to different industries allows companies to enhance their operational efficiency and marketing effectiveness. Leveraging AI in analyzing vast data sets helps in crafting personalized marketing messages and strategies. Furthermore, AI’s role in content generation and integrity (such as countering deepfakes) is becoming crucial in maintaining brand reputation and trust, a core component of marketing.

Accessibility and democratization of AI technologies with lower the barrier to entry and FOMO will dominate. The trend towards more accessible and user-friendly AI tools enables businesses of all sizes to leverage AI capabilities. This democratization means that even small and medium-sized enterprises can harness AI for marketing, leveling the playing field and making AI-driven strategies a norm rather than an exception. Additionally, advancements in AI technologies, like leaner and portable models, make AI integration more feasible, further encouraging its adoption.

Strategic AI direction and Ethical integration of AI will start to gain more trust in executive circles. As AI governance and ethical considerations gain prominence, companies are motivated to integrate AI in a manner that aligns with regulatory standards and ethical practices. This strategic integration is vital in marketing, where transparency and ethical use of customer data are paramount. Moreover, the advancements in AI, such as multimodal models and real-time analytics, offer sophisticated tools for marketers to craft more effective and engaging campaigns.

Section 2: An Analysis of 15 Technical AI Trends and Extrapolations for 2024

This section provides a more detailed, slightly more technical yet still holistic analysis of the current state and future trajectory of AI, highlighting the key trends, challenges, and opportunities shaping this dynamic field.

1. Dimensional Reduction in AI Models

Embracing efficiency, AI trends are moving towards models that are leaner and more portable, utilizing advanced compression and distillation techniques. This shift indicates a preference for models that are easier to deploy and manage, reflecting a strategic trend towards more pragmatic AI solutions.

2. Development of AI Hubs for Model Management

AI’s landscape is morphing into a dynamic port of models, where AI hubs manage the intricate lifecycle of diverse AI models. These hubs integrate complex systems for training, tuning, governance, and monitoring, mirroring bustling ports where various ships dock and manage their cargoes.

3. AI Governance and Intelligent Model Registry

The future of AI is not just about capability but also about governance. The rise of more intelligent model registries will better ensure and further de-risk AI models in terms of adherence to ethical and compliance standards. This governance will play a pivotal role in making AI models not only effective but also fit for purpose.

AI Governance will help chart a course for ethical, grounded, less biased LLM Application and Model implementations. Recognizing the potential societal implications of AI, the concept of AI governance will rise to prominence. This framework for guiding the responsible and ethical development and deployment of AI technologies will be essential in ensuring their alignment with human values and societal well-being.

4. Challenges in AI Legislation and Compliance

The legislative scene in AI is complex and still rapidly evolving. AI systems are gearing up to adapt to a diverse range of international regulations, although enforcement challenges and lack of agreement will present significant hurdles.

5. Increased Importance of Model Metrics and Monitoring: The Forging of Robust Evaluation Frameworks

In AI, the emphasis is increasingly going to focus on metrics and model evaluation. Enhanced benchmarking tools and more objective performance metrics are becoming crucial, with models being more rigorously evaluated for performance, ground truth divergence — making this an in-demand field. To facilitate this shift, we can expect the development of robust AI evaluation pipelines and frameworks. These tools will provide standardized benchmarks for assessing the performance and efficacy of AI models, fostering a more objective and data-driven approach to evaluating their real-world impact.

6. Balance Between Fit-for-Purpose and Monolithic Models

The GenAI domain is witnessing a balance between specialized, efficient, often task-specific / fit-for-purpose models and large, monolithic models that continue to dominate their respective areas. This dual trend showcases the diversity and adaptability of AI models across various applications.

7. The Rise and Snowballing of Multimodal AI Models

Multimodal LLMs (or Large Multimodal Models (LMMs)) are expanding AI’s horizons, venturing into realms that combine different modalities of data during pre-training, tuning and inference. This development significantly broadens their applicability and utility, marking a significant evolution in AI capabilities. Read my blog that focuses on this transformative domain of LMMs.

8. Orchestration via Specialized LLMs

Larger LLMs are taking on the role of conductors in an orchestra of AI agents. These specialized LLMs manage complex interactions, ensuring efficient and coordinated performance across various applications. This will start with simple routing and evolve into mediation and orchestration. Smaller, more specialized language models will proliferate, offering targeted expertise in specific domains. Managing this diverse ecosystem of LLMs will necessitate the development of effective orchestration tools, capable of routing tasks and queries to the most appropriate model based on factors like expertise, efficiency, and cost.

9. Multi-agent Systems, Swarms and On-behalf-of Human Agents: Agent-to-Agent Interactions in AI Systems

AI systems are evolving to facilitate more foundational and dynamic agent-to-agent interactions, leading to autonomous reasoning, planning and acting on behalf of human actors as well as contributing to decision-making processes and behaviors. This will be covering a spectrum of individual systems to multi-agent collaborations.

10. Contrast in Data-Driven Models: Advanced RAG vs. Model Fine-Tuning

The tension between the two forces of RAG and fine-tuning will intensify. The data-driven AI model landscape is diversifying, with advanced RAG models focusing on integrating external data for real-time retrieval, contrasting with fine-tuning and parameter-efficient fine-tuning methods that strike a balance between customization and resource efficiency. The killer app will be an optimization of these two forces, resolved not in the abstract, but in specific domains and use-case categories even to the extent of observing Enhanced Inference where the generic model will invoke specific data only at inference time. Again the key will be the combination, balance and optimization of these two complementary approaches for maximum gain in speed, cost, quality and ease of use and ultimately fit-for-purpose in the domain in question.

11. Real-Time Analytics for Model Selection

The dynamic AI market will grow competitively and increasingly reliant on near real-time metrics, KPIs, and analytics to determine the “best” [optimization function of latency, proximity, cost, model quality and capability] model for specific production tasks, navigating the complexity of model selection.

12. Expanding Scope of LLMs Across Digital to Physical Spectrum

LLMs are penetrating deeper into not only enterprise commerce and business systems but digital twins, simulations, games, AR/VR, Metaverse but also and simultaneously transcending digital confines, venturing from Cloud and on-prem into IoT, Edge, Hybrid and Robotics. This multi-directional expansion signifies their penetration and permeation into a multidimensional spectrum, encompassing digital realms and physical applications as standalone applications up to multi-agent based systems.

13. Explainable, Verifiably Grounded AI for Enterprise, Regulation/Legislation and Compliance

As AI navigates complex legislative landscapes, there’s a growing demand for systems that are not just compliant but also explainable and verifiably grounded. Such AI systems will be developed to provide more traceability in data, training, RAG, and transparent rationales for their decisions and actions.

14. The Data Platform for the GenAI [R]Evolution

The future of AI heavily relies on the evolution of data handling and management in the context of an all encompassing data platform:

There’s a growing need to produce data that can be used for automated labeling via LLMs (companies like snorkel.ai, labelbox,etc.), and reinforcement learning with AI feedback in addition to just RLHF which is costly and time consuming, that are crucial for LLM tuning and training.

Vector databases and of course embeddings (including realtime conversions) are set to become ubiquitous (see Vertex AI Vector Search, AlloyDB , Google BigQuery Embeddings, DataStax, Elastic, MongoDB, , Pinecone, SingleStore,W eaviate, etc.), integrated into every major data platform, enabling data to be GenAI-ready for use in RAG-style and other model augmentation approaches.

Graph databases and knowledge graph representations will see a significant adoption increase, showing current limitations of systems and pushing for more advancement in areas that are not stress tested today, becoming more prevalent as they evolve to offer more sophisticated and interconnected data structures that are increasingly consumable by LLMs or LMMs (see Neo4J, as an example).

Data Privacy and Training of LLMs has legal, ethical, technical challenges. Synthetic Data (e.g., Gretel.ai) will go a long way in alleviating many of these issues.

Federated Learning will start to go mainstream to alleviate the issues of data privacy even further.

15. Solution Completeness vs Model Emphasis. There are multiple patterns to implement across the entire GenAI lifecycle that result in multiple components, each of which are necessary for a given state of AI Maturity. The over emphasis on models will move the center of gravity to a more balanced view of solution completeness : who has a set of implementations for the patterns across the GenAI lifecycle.

Conclusion and Call to Action

As we approach 2024, the landscape of AI is evolving rapidly, marked by significant transformations in both legal and technological realms. This evolution presents a unique blend of challenges and opportunities, and it’s crucial for businesses, developers, and policymakers to actively engage with these changes. Staying informed about legal developments, participating in policy discussions, and contributing to a balanced framework that supports innovation while ensuring responsibility is vital.

The rapid advancement in AI technologies, such as multimodal models and AI governance tools, offers a significant opportunity for various sectors. Embracing these new technologies, understanding their potential, and exploring their transformative impact on operations and strategies are essential steps for staying ahead in this dynamic field. Alongside the adoption of new technologies, fostering transparency and ethical practices in AI model training and content utilization is becoming increasingly important. This includes clear disclosures about data sources, methodologies, and adopting technologies that safeguard against unauthorized use of content.

The evolving relationship between AI developers and publishers is leading to the emergence of innovative business models and partnerships. This shift provides an opportunity to explore new revenue streams, collaborate on AI-powered tools, and leverage AI for more personalized and effective solutions. Additionally, as AI extends its reach into IoT, edge computing, robotics, and more, it’s crucial to understand how AI can be integrated into various aspects of a business and stay ahead in leveraging AI for diverse applications.

In an era of heightened scrutiny, promoting AI literacy and critical engagement is essential. Cultivating a deeper understanding of AI within organizations and among stakeholders ensures informed and critical engagement with AI technologies. The increasing accessibility of AI tools democratizes AI, enabling businesses of all sizes to harness its capabilities, fostering innovation and creativity across various fields.

Furthermore, the focus should remain on developing AI solutions that augment rather than replace human intelligence, emphasizing the collaboration between humans and AI. This synergistic approach will be key to harnessing the full potential of AI in problem-solving and innovation.

Finally, the AI landscape is dynamic and evolving very rapidly such that 30–60–90 day plans have become 3–6–9 day plans. Stay agile, keep abreast of the latest trends and developments, and be ready to adapt strategies and operations. Engage in the broader conversation about the future of AI, share insights, learn from the broad community, and actively participate in shaping the AI landscape of tomorrow; these are essential for harnessing the full potential of AI while ensuring its ethical, responsible, and beneficial use for you, your business and your community.

Actions

Here are ten action items to consider as you navigate the evolving AI landscape of 2024 :

  1. Engage with Legal Changes: Actively participate in understanding and shaping the legal framework surrounding AI, focusing on balancing innovation with ethical and responsible practices.
  2. Adopt New Technologies: Research, test and embrace the more stable if not latest advancements in AI, such as multimodal models and governance tools, and explore their potential to transform your operations, business model and strategies.
  3. Prioritize Transparency and Responsible AI: Implement transparent and ethical practices in AI model training, tuning and content utilization, including clear disclosures about data sources and methodologies.
  4. Explore Innovative Business Models: Capitalize on the changing dynamics between AI developers and publishers to explore new revenue streams and collaborative opportunities based on solution completness of vendors, not just models.
  5. Prepare for AI’s Broadening Scope: Understand and prepare for the integration of AI into various domains like IoT, edge computing, and robotics to stay abreast of leveraging AI’s diverse applications for your business, domain and industry.
  6. Promote AI Literacy: Cultivate a deeper understanding and critical engagement with AI technologies within your organization and among stakeholders.
  7. Leverage AI Democratization: Take advantage of the increasing accessibility of AI tools to empower your team and foster innovation across different fields. Look for vendors with Solution Completness and not just the “latest” models.
  8. Foster Human-AI Collaboration: Focus on developing AI solutions that complement / augment human skills, emphasizing a collaborative environment where both can thrive.
  9. Stay Agile and Adaptive: Remain flexible and responsive to the rapidly changing AI landscape, adapting your strategies and operations to stay relevant. Also, consider planning for 3–6–9 days versus only for 30–60–90 days.
  10. Participate in the AI Conversation: Engage in the broader discourse on AI’s future, sharing insights and learning from others to actively shape the AI landscape.

These actions will collectively help in navigating and maximizing the opportunities presented by the rapidly evolving field of AI in 2024. Godspeed!

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Ali Arsanjani

Director Google, AI/ML & GenAI| EX: WW Tech Leader, Chief Principal AI/ML Solution Architect, AWS | IBM Distinguished Engineer and CTO Analytics & ML