Generative AI: Where Is the World Heading Towards?

Kunal Jain 05 Jul, 2023 • 7 min read

Introduction

Generative AI | AI evolution

In the ever-evolving world of technology, we are witnessing a rapid surge in the development and deployment of AI products. Over the past six months, established tech giants and ambitious startups have made significant strides in artificial intelligence. While companies like Microsoft and OpenAI have garnered much attention, it’s essential to recognize that they are just the tip of the iceberg.

To remain competitive in the swiftly growing Enterprise AI industry, businesses continuously innovate and explore new avenues. One such strategy involves collaborations and inorganic growth. By leveraging the expertise and resources of other companies in the industry, businesses can develop superior products and services for their customers. Moreover, these partnerships open up new markets and previously inaccessible opportunities. Mergers, acquisitions, and partnerships provide access to novel technologies, talent, and customer bases, allowing businesses to expand their offerings and diversify revenue streams.

From my perspective, the current cycle of AI evolution can be divided into three distinct phases, each driving growth and progress in its way.

Growth Phases in Every Technology

Every technology has three phases of its growth: infancy, maturation, and obsolescence.

  1. Emergence: In this phase, new technology emerges and gains attention. It often starts with a few pioneering individuals or organizations exploring and developing the concept. During this phase, there may be limited applications and a lack of widespread awareness or adoption. The focus is primarily on research, experimentation, and proof of concept.
  2. Growth and Expansion: Once a technology gains traction and demonstrates its potential, it enters the growth and expansion phase. More resources are allocated towards its development, leading to functionality, efficiency, and usability advancements. Increased investments, both from the public and private sectors, play a crucial role in driving progress. During this phase, the technology starts gaining wider acceptance, more companies enter the market, and consumers begin to adopt it. Innovation and competition thrive, leading to rapid advancements and improvements. This phase also triggers collaborations and strategic partnerships.
  3. Maturity and Integration: In the maturity phase, the technology becomes an established part of the industry and society. It reaches a point of stability where the growth rate levels off, and further advancements become incremental rather than revolutionary. Technology has become deeply integrated into various aspects of people’s lives and existing systems. In this phase, the winners start to emerge. Standardization, optimization, and interoperability become important focuses during this phase. While significant breakthroughs may still occur, they are less frequent compared to the earlier stages.

Now, we will see where generative AI as a technology is in terms of evolution. Let’s explore these phases one by one.

Learn More: Generative AI: Definition, Tools, Models, Benefits & More

Phase 1: Emergence – Unleashing a New Era of AI

In recent times, AI has witnessed an unprecedented phase of emergence. Also marked by significant product launches and the dawn of a new era. Notably, the introduction of GPT-3 (Generative Pre-trained Transformer 3) has played a pivotal role in catalyzing this transformation. Consequently, venture capital investments have been pouring into the AI landscape, with numerous startups eagerly joining the AI race to secure funding and develop groundbreaking AI-powered products. It is essential to highlight that Phase 1 is an ongoing process and is anticipated to persist for at least another 6-12 months.

A list of AI tools for different objectives. Learn more here.
Source: Bardeen.ai

During this initial phase, it is expected that the industry’s Innovators will seize the opportunity and surge ahead. A notable example is the collaborative efforts of OpenAI and Microsoft, who have established themselves as frontrunners in this domain. However, their success can be attributed to their immense dedication and hard work to refine their technology behind the scenes. These organizations serve as exemplars of the potential rewards awaiting those who embrace the possibilities offered by AI during this transformative period.

To delve deeper into the significant milestones achieved during Phase 1, refer to the following notable announcements:

These milestones testify to the groundbreaking advancements made in the field, setting the stage for a future brimming with possibilities and ensuring that the momentum of the AI revolution continues unabated.

Phase 2: Growth and Expansion – Acquisitions, Mergers, and Collaborations

Phases of Generative AI | AI evolution | enterprise AI industry

In the fast-paced world of technological advancement, companies often resort to strategic moves such as acquisitions, mergers, and collaborations to strengthen their positions, expand their capabilities, and fuel innovation. Phase 2 is an exciting period characterized by these transformative activities, reminiscent of a similar trend observed in Deep Learning not too long ago.

During the Deep Learning era, industry giants like Google, Apple, Meta (formerly Facebook), and Amazon engaged in an acquisition spree, devouring numerous promising startups within 2-3 years. These strategic moves allowed them to tap into new technologies, leverage specialized expertise, and accelerate their research and development efforts.

Recent Developments

To give you an idea of recent developments in this phase, Here is a list of collaborations / Acquisitions announced in the last few days:

  1. NVIDIA and Snowflake have announced a partnership to help businesses with Gen AI capabilities on the on the cloud. This would mean Snowflke’s customers can now build AI models on their data using Nvidia’s NeMo framework. Link 
  2. Amazon Web Services (AWS) has made multiple models available in its Enterprise offering – Bedrock with Stability, Falcon, and Anthropic models. They are pitching to their customers to pick and choose from the marketplace as they deem fit.
  3. Databricks has acquired MosaicML to provide a unified platform for managing data assets and building secure generative AI models.

Update:

  1. Hugging Face and AMD partner on accelerating state-of-the-art CPU & GPU platforms. Link

Also Read: CPU vs GPU: Why GPUs are More Suited for Deep Learning?

During Phase 2, companies that adopt open approaches, embrace long-term visions, and prioritize continuous innovation will likely emerge as industry leaders. Identifying valuable synergies through acquisitions, mergers, and collaborations enables these companies to stay ahead of the curve, drive technological advancements, and deliver impactful products and services to their customers.

As Phase 2 unfolds, we can expect further exciting developments, shaping the trajectory of technological progress for years to come.

Phase 3: Consolidation 

Phases of AI evolution. Discover more.

As the industry progresses through the phases of technological evolution, Phase 3, known as the Consolidation phase, gradually comes into focus. This phase typically arrives after the initial wave of acquisitions, mergers, and collaborations witnessed in Phase 2. During this period, certain players may start facing funding challenges, leading to an increased level of competition and the emergence of clear winners.

One of the critical characteristics of Phase 3 is the establishment of ecosystems that shape the new landscape of the industry. As companies solidify their positions and integrate acquired technologies and resources, they build interconnected networks of synergistic products, services, and platforms. These ecosystems create a foundation for further innovation, collaboration, and growth.

Winning in Phase 3 requires a combination of the discipline exhibited in Phase 2, such as open approaches, long-term vision, and continuous innovation, along with the ability to sense great deals and execute them in a mutually beneficial manner. The companies that excel during this phase possess a keen strategic foresight and the agility to adapt to changing market dynamics.

Microsoft’s acquisition of OpenAI towards the end of the previous AI cycle, led by Deep Learning, exemplifies successful consolidation in the AI industry.

Industry experts anticipate Phase 3 to occur within a few years from the current stage of the industry. As funding challenges arise and companies vie for dominance, the consolidation phase will set the stage for a new era of stability, maturity, and collaboration.

What Does Convergence Look Like?

Convergence in the technological landscape is an intriguing phenomenon that marks the end of one phase and the beginning of another. As one step concludes, it sets the stage for new developments and advancements. During this transition, notable changes can be observed, particularly among the big players in the ecosystem.

In a period of convergence, the more prominent industry players often engage in a rush of multiple acquisitions within a single year. These acquisitions serve as strategic moves to expand their capabilities, diversify their offerings, and stay ahead of the competition. It becomes a race to acquire innovative startups, intellectual property, and talent that align with their vision and goals.

One significant aspect that comes into play during convergence is hardware-related innovation. With the increasing demand for computational power, the era of groundbreaking hardware advancements can kick off. NVIDIA dominates the GPU chips, but new players like Apple strive to enter this domain. The competition among these powerful hardware manufacturers fuels the development of cutting-edge technologies, as these components serve as the brain of generative AI systems.

Additionally, as convergence progresses, there is a drive to develop lighter and more efficient models. AI models’ focus on optimization enables their utilization for general purposes, expanding their applications beyond specific domains or use cases. The development of such models paves the way for broader adoption and integration of AI across various industries and sectors.

Overall, convergence brings about a dynamic shift in the technological landscape. It ushers in an intense activity where acquisitions, hardware advancements, and the development of efficient models take center stage. This transformative phase sets the foundation for the next wave of innovation and growth in the ever-evolving world of technology.

Conclusion

Collaborations and inorganic growth are pivotal in driving fast business growth in the Enterprise AI industry. These strategic moves enable companies to expand their offerings, tap into new technologies, and provide better options to customers. Moreover, collaborations and acquisitions intensify competition, leading to further value creation within the ecosystem. By embracing these strategies, businesses can position themselves for success in the rapidly evolving AI landscape.

Also Read: The Best Roadmap to Learn Generative AI in 2023

Kunal Jain 05 Jul 2023

Kunal is a post graduate from IIT Bombay in Aerospace Engineering. He has spent more than 10 years in field of Data Science. His work experience ranges from mature markets like UK to a developing market like India. During this period he has lead teams of various sizes and has worked on various tools like SAS, SPSS, Qlikview, R, Python and Matlab.

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