Strategy In AI's Shifting Sands: Our First Principles-Based Thoughts on AI 

Introduction

The UAE has emerged as a leading AI change agent, thanks to strategic collaboration across public, private, and academic sectors, as well as early investments in research, planning, and innovation. Similarly, Saudi Arabia is making significant strides, recognizing the opportunities that AI presents both within the Kingdom and across the region.

Today, AI is rapidly moving beyond the research phase into a revolutionary phase, which is in turn enabling the growth trajectory of other transformational technologies such as computational biology and robotics. The UAE's foresight in prioritizing the commercialization of extensive AI research and development efforts that have been undertaken by various government entities, has been a key driver of this shift. This approach both accelerates AI's practical applications and also positions the UAE at the forefront of the global AI landscape, with Saudi Arabia rapidly stepping in to become its regional running mate. As AI continues to evolve and reshape industries worldwide, the region’s forward-thinking strategies ensure it's ready to harness AI's potential for economic growth, societal advancement, and global impact. 

At this juncture, we believe it may be of value to share key themes of BECO’s internal discussions on AI and surface observations and implications for the region, and the wider global business community. It’s important to bear in mind that we’re all operating in a dynamic environment; therefore these observations are not fixed; and will be revisited, as we all progress.  

Over the next few weeks, this four-part series will delve into:

• Part 1: Our First Principles-Based Thoughts on AI 

• Part 2: Decoding Dynamics for Builders and Businesses

• Part 3: The Blossoming of AI in the GCC  

• Part 4: BECO’s AI Investment Thesis

Today, structural tailwinds are positioning the GCC, particularly the UAE, as an incubator for globally competitive AI businesses. In this series, we'll explore how emerging AI players, rooted in the GCC, are positioned to capture global opportunities and reshape the tech landscape.

Our First Principles-Based Thoughts on AI 

• Thought 1: From A Glacial Pace To A Global Phenomenon

• Thought 2: In The New Data Landscape, Centralization Meets Democratization

• Thought 3: GenAI Is Borrowing From Past Revolutions And Creating An Entirely New One

• Thought 4: Governments And Startups Are Competing. Why?

• Thought 5: Watch Your Back, Builders. Balance Innovation With Vigilance

• Thought 6: Unlikely Allies Are Creating A New Gameplay

Thought 1: From A Glacial Pace To A Global Phenomenon

While AI's rise to prominence might seem like an overnight success story, its journey to get here took 70 years. It has been a journey marked by perseverance, and one with no guarantee of making it this far. The field's origins trace back to the 1950s and 1960s, with pioneer scientists Alan Turing and John McCarthy laying the foundational theories and concepts. While Turing's work is widely recognized, McCarthy's equally significant contributions often receive less attention. To fully grasp AI's roots, it's crucial to examine the backgrounds and key insights of these two visionaries:

Alan Turing, a British mathematician, is often regarded as the father of computer science and AI. In his 1950 paper, "Computing Machinery and Intelligence," he introduced the Turing Test to determine if a machine exhibits human-like intelligence.

John McCarthy, an American computer scientist, coined the term "Artificial Intelligence" in 1956 and organized the Dartmouth Conference, which marked the birth of AI. In 1958, he developed the LISP programming language, a standard tool for AI research.

Early AI efforts sparked optimistic predictions, but progress stalled due to limitations in computational power and the scarcity of data, leading to periods known as "AI winters". The field regained momentum in the 1990s and 2000s with advancements in computing, notably through Nvidia's innovations, and the advent of big data. This resurgence culminated in a significant wave from 2015 to 2019, marked by breakthroughs in machine learning and deep learning, demonstrating AI's potential in image recognition, natural language processing, and other complex tasks. 

The stage was set for GenAI's explosive arrival in 2022. The convergence of purpose-built infrastructure (GPUs), cloud computing provided by major players like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), and the Internet's accessible dataset provided the ideal conditions for GenAI and in turn Large Language Models (LLMs) to flourish. The public launch of OpenAI's ChatGPT marked a turning point, sparking widespread interest and excitement.

Thought 2: In The New Data Landscape, Centralization Meets Democratization

It’s often been repeated that “data is the new oil”. Like oil, data requires refinement to unlock its true value. However, unlike oil, data isn’t capitalized based on geographical fortune. The emergence of sophisticated architectures such as data lakes and data warehouses has democratized data science, making it increasingly accessible. This democratization has been a key catalyst in igniting the GenAI revolution, enabling a broader range of participants to harness the power of large-scale data analysis and generation.

Governments in the region are recognizing the potential of structured, accessible data. For instance, the Saudi government has integrated 130 government databases and created a data-sharing marketplace for over 250+ government systems integrated in the national data catalog and accessible via API. This structured approach allows for the leverage of vast datasets with AI, exemplifying how data consolidation can drive AI innovation at a national level.

From such government initiatives to big tech giants investing billions in research and development, and a new wave of well-funded startups, actors at all levels are vying to capitalize on GenAI's potential. This widespread engagement underscores GenAI's perceived transformative power and its potential to reshape industries and economies.

Thought 3: GenAI Is Borrowing From Past Revolutions And Creating An Entirely New One

When is a major technological breakthrough truly a breakthrough? When it achieves mass adoption. When consumers adopt technologies, it democratizes access and spurs an influx of builders and innovators. GenAI's trajectory has been interesting for so many reasons, not least because it’s adopted elements from the Internet, smartphone, and social media revolutions. This entails three key components:

1. Research Origins: The Internet's widespread adoption in the 1990s transformed it from a research tool to a global commercial and social platform, leading to the dot-com boom and the rise of tech giants. Similarly GenAI emerged from decades of academic and industrial research.

2. Applications and Services: Mirroring the smartphone revolution ignited by the iPhone in 2007, GenAI is spawning a diverse ecosystem of applications and services, fostering innovation among developers and startups.

3. New Monetization and Business Models: Similar to how social media platforms like Facebook/Meta and Twitter/X democratized content creation and distribution, GenAI is giving rise to new forms of value creation and monetization.

GenAI's journey from research to application layer is now paving the way for innovative monetization and business models. The triangulation of hyperscalers, data centers, and GPUs - the infrastructure backdrop discussed earlier - has created the perfect environment for GenAI to flourish, unlocking unprecedented creativity and innovation among a new generation of entrepreneurs and users.

Thought 4: Governments And Startups Are Competing. Why?

The AI arena is a unique playing field where diverse teams compete, driving unprecedented advancements and democratizing access to cutting-edge technology. Notable models like Claude from heavily funded startup Anthropic, Falcon from the Technology Innovation Institute (TII) in the UAE, and LLaMA from Meta illustrate how world-leading AI innovations are emerging from a diverse array of stakeholders. These include multi-trillion-dollar companies, multi-billion-dollar companies, startups, and sovereign entities from around the world. This diversity in contributors is particularly remarkable, as few sectors showcase such a wide range of players competing at the highest level.

OpenAI, with its ChatGPT series, has emerged as the face of the AI revolution, single-handedly leading the way for the next wave of innovation at levels likely to surpass what mobile and the cloud achieved, all within a much shorter time frame. While OpenAI popularized and made LLMs mainstream, others quickly followed suit.

The exciting theme that emerges from all these different stakeholders is the fundamental approach they are taking around how they access the data required and their mindset as to whether they build with an open vs. closed source approach and so much more. It’s very hard to say what approach ends up dominating but with the overall theme of all things GenAI, we will likely end up at a place where companies leverage both closed and open source models depending on the use case.

Thought 5: Watch Your Back, Builders. Balance Innovation With Vigilance

As venture capital investors, one of our main concerns is when a founder takes their eye off the ball, or isn’t sufficiently paranoid about competition. It's a life-and-death paradox: one day you're on top of the world, and the next day you’re irrelevant and overtaken by an unassuming competitor. It’s especially amplified in the age of AI. A notable example is the trajectory of Google/Alphabet who had the initial lead from the lot, in 2015 and actually paved the way for GenAI as we know it on the back of the launch of the transformer architecture which is critical in powering Gen AI models.

While 2015 might seem unremarkable to many, it was a landmark year for those in the data science space. Google (now Alphabet) released TensorFlow on November 9, 2015, followed by the founding of OpenAI a little over a month later, on December 11, 2015. TensorFlow, available as an open-source library, had a quick and immediate impact, allowing developers, researchers, and companies worldwide to use it for their Machine Learning projects. Google, a giant in its own right, also embedded TensorFlow into its products, such as Google Search. OpenAI, on the other hand, initially focused on groundbreaking research projects, with its GPT series released gradually over the years. Google continued its AI advancements, achieving a significant milestone in 2016 when its AlphaGo program defeated legendary Go player Lee Sedol. In 2017, the Transformer architecture was introduced which allows models to understand the interplay of words in a sentence (therefore understanding context). Then, in 2018, BERT was released, marking a significant advancement in natural language understanding and paving the way for numerous new applications.

Despite these advancements, Google's initial dominance faced fierce competition as the AI landscape evolved rapidly. Google was met with intense competition from other tech giants and emerging startups. The lesson here is clear: while it’s monumentally difficult to be first; it’s even harder to remain in the lead. The huge strides Google has taken over the years have significantly contributed to where AI is at today and have enabled projects such as Bert and GPT-3/4 to come to life. Yet even industry leaders like Google must remain vigilant and agile to stay ahead in the fast-paced AI race. Their journey, while just one example of many, underscores the importance of continuous innovation and adaptability in maintaining a competitive edge and capitalizing on one's own innovation.

Thought 6: Unlikely Allies Are Creating A New Gameplay

Strategic decisions and partnerships, beyond sheer speed and resources, have shaped the AI landscape, highlighting the interplay among major players and the evolving competitive environment in the tech industry. Examining the trajectories of OpenAI, Microsoft, and Nvidia reveals how pivotal milestones and maneuvers have positioned these companies as leaders in AI, today.

• OpenAI transformed from a research-driven organization into an AI powerhouse with key developments including the launch of GPT-3 and influential partnerships. By making AI more accessible and collaborating with giants such as Microsoft and Apple, OpenAI has solidified its market position and advanced global AI capabilities. With Microsoft, OpenAI has forged an unusual partnership that resembles more of a friendly rivalry. This collaboration gives OpenAI access to all the compute power they need to train and run their models. Additionally, it offers distribution to enterprise customers by bundling OpenAI into Microsoft's Azure offering as well as Microsoft 365 Copilot. On the consumer front, OpenAI's partnership with Apple grants access to over 1.35 billion iPhone users.  

• Microsoft's early recognition of OpenAI’s potential and its strategic integration of GPT models into its products and Azure cloud platform were game-changing moves. This partnership not only provided OpenAI with essential resources but also gave Microsoft a competitive edge in the AI arena.

• Nvidia’s dominance in AI infrastructure is well-established, thanks to its early investment in high-performance computing. While researchers initially drove many of Nvidia's early AI use cases, today its growth is fueled by major players such as Tesla, Meta, Microsoft, Alphabet, and others, with estimates suggesting that these four players contribute close to 40% of Nvidia’s revenue. However, Nvidia now faces increasing competition as industry players invest in developing their own chips and AI services, challenging Nvidia’s once-unassailable position.

Meanwhile, the UAE has carved its own unique path in AI, mirroring the global trajectory where much of the early AI work was research-focused across various fields, including computational biology, robotics, and cybersecurity. Now, having moved beyond the research phase, the UAE is entering the early stages of the Revolution phase, with a clear strategy and thoughtful partnerships. This shift is driven by a priority to capitalize on years of R&D investments made by various government entities. We will delve more into this in part 3 of this series.

Up Next

Meanwhile, in the upcoming segment of our four-part series, "Decoding Dynamics for Builders and Businesses," we will share our thoughts on implications that impact founders and which enterprises may also consider. We’ll explore the meaning of coopetition as a business strategy and explain why it’s a critical consideration for AI founders, particularly in the GCC. We will pose the question of the unprecedented phenomenon of AI's widespread adoption prior to establishing product-market fit, and last but not least, present innovative and emerging business and revenue models that we hope give founders food for thought.