8 min read
Beyond the Buzz:
A Deep Dive into the Generative AI Horizon
Executive Summary
Unveiled on November 30, 2022, ChatGPT garnered substantial investments from tech giants and captured consumer excitement. However, a crucial question persists: is this a fleeting trend for trivial uses, or does it possess the potential to transform industries globally?
To address this inquiry, we explore the brief history of AI, its technology layers, diverse use cases, impacts, market landscape, and our own anticipations. Historical economic shifts and market disruptions often stem from irresistible economic incentives or breakthrough technologies significantly enhancing product experiences. We identify a potential turning point with the reduction in creation costs driven by AI and Large Language Models (LLMs).
While the initial phase of Generative AI showcased novelty apps, it is now evolving towards a customer-centric approach. The promise lies in delivering deep value and enriched experiences by integrating foundation models into a more comprehensive solution rather than standalone applications.
We dissect Generative AI into three layers: infrastructure, model, and application. The application layer, with its extensive reach, presents compelling investment opportunities. Key focus areas are in healthcare, the evolution of software interaction, digital production, productivity, and education.
Our investment strategy is grounded in the belief that AI advancements will create and disrupt numerous sectors. However, we acknowledge the cyclical nature of technological revolutions, marked by cycles of excitement, investment, bubbles, and market corrections.
Introduction
On November 30th, 2022, the world witnessed a seismic shift in the technology landscape when ChatGPT, a large language model (LLM), was released, and it took the world by storm. It could generate human-like text and engage in conversations that were indistinguishable from those with real people. Yet, amidst the enthusiasm, LLMs and generative AI pose an essential inquiry: Is it merely a fleeting fascination and a tool for trivial amusements like composing poems or does it hold the transformative power to revolutionize numerous industries and profoundly touch the lives of people worldwide, similar to the way the internet did? Summarizing texts, writing poems, and creating avatars seem fun, but what significance do these capabilities truly hold?
To answer this question and guide our vision of the future, we’ve asked ourselves the following questions:
- What have been the driving forces in reaching this juncture?
- Why is now the optimal time to consider investing and building in this domain?
- What concrete use cases can emerge and which sectors are poised for disruption?
- Where can we identify compelling investment prospects?
What are LLMs, and what is their significance
LLMs leverage the transformative power of the transformer model, developed in 2017 by Google researchers. This technology breaks down word sequences into tokens, enabling the model to understand the context by encoding and training on extensive datasets from internet text. The unique self-attention mechanism within the transformer architecture distinguishes LLMs and allows it to capture context comprehensively and understand relationships between words more effectively compared to previous models like recurrent neural networks (RNNs).
Recognizing the nature of LLMs reveals that they function more as pattern-spotting engines than fact-checking systems. They excel at generating text that appears plausible but may not always be factually accurate. However, given the vast number of parameters on which these models are trained, they represent the closest approximation to mimicking the human brain’s capacity to discern patterns and comprehend context. Presently, they surpass human performance in speech and text recognition, reading comprehension, and language understanding.
The proficiency of LLMs in understanding natural language, their capacity for reasoning, and their understanding of context, given their world knowledge in text, has surpassed expectations and marks a new era in human-computer interaction. Just as the graphical user interface transformed personal computing in the 1980s, LLMs with natural language interfaces will democratize access to AI and redefine how we work and interact with software.
Why now
It’s important to study past cycles of innovation to discern patterns and filter through the noise. Historically, we have witnessed that major economic shifts and market dislocations occur when there is an irresistible economic incentive (either in revenue expansion or cost reduction) or when new enabling technology improves product experiences by a factor of 10X. The fact that we haven’t yet witnessed a platform shift similar to the mobile and internet revolutions in AI suggests that the use cases have been somewhat limited in scope, not yet serving as platform enablers for disrupting entire sectors.
However, we are standing at an inflection point where the economics of generative AI are incredibly compelling. Looking back at the previous era, we can draw parallels to the current situation. The microchip era dramatically reduced the marginal cost of computing, reshaping iconic companies like IBM. The internet similarly drove distribution costs to near zero, giving rise to tech giants like Amazon and Google. More recently, the mobile era decreased the cost of access and connectivity, allowing users to connect at zero marginal cost, giving birth to companies like Facebook with new business models. And now AI and large language models have dramatically reduced the marginal costs of production, making us stand on the brink of another transformative moment, one that will result in massive productivity gain.
If we look at the average number of workers at an S&P 500 company needed to generate $1M, revenue has decreased by 75%; adjusting for inflation, this number could drastically improve over the next decade as companies start to do more with less.
As we navigate this inflection point, the excitement lies in the promise of generative AI evolving from novelty applications to truly transformative products, with the potential to disrupt industries on a broad scale. According to Goldman Sachs, Global GDP could grow by an extra 7% or $7 trillion and lift productivity growth by 1.5% over a 10-year period.
The value chain of Generative AI
To understand how the generative AI market is taking shape, we first need to define how its structure looks today. The AI stack can be divided into three layers:
The application layer
The application layer could be broken down into two primary categories:
- Horizontal Tooling: Aims to dominate workflows or other processes across various industries. Examples include Tome, which provides AI-driven presentation solutions.
- Vertical Turnkey Applications: Tailored for particular verticals or sectors, delivering comprehensive solutions within one specific industry. A notable example is Cradle, which offers AI-assisted tools for protein design.
Why the Application Layer Is the Most Exciting Area
The evolution of applications has unfolded in two distinctive waves. The first wave was characterized by initial hype and excitement, akin to a “me too” phenomenon, where everyone sought to partake in the burgeoning field. During this phase, numerous niche applications and autocomplete features emerged with no distinct competitive advantage. However, as:
- The quantity and quality of parameters these models are trained on increase,
- Models become more complex with user feedback as well as new techniques such as ‘reflexion’ and ‘grounding’ enhance AI’s capacity to provide more accurate predictions and responses using specific, contextually relevant information,
- Efficiency is improved in managing computing and storage costs,
the divide between customer expectations and Ai’s capabilities diminishes, facilitating domain-specific use cases exemplified by innovations like Harvey and Inceptive.
This leads to the second wave, promising to deliver deep value and enriched experiences to end customers by using foundation models as part of a more comprehensive solution rather than the entire solution. This phase represents a more mature and refined stage of generative AI, where the focus shifts from novelty to the substantial enhancement of user experiences and real-world applications.
During the initial wave, those who triumphed were the ones utilizing general-purpose LLMs for use cases where precision wasn’t crucial. However, as models progress, we anticipate the rise of novel applications demanding greater precision and complexity, thereby unlocking fresh opportunities in the market.
As models advance, we also see the emergence of new use cases that require higher precision and complexity such as the one in the top right corner:
Five Focus Areas within the App Layer:
1-Revolutionizing Healthcare
U.S spends a staggering $4 trillion annually on healthcare in the United States, with a substantial $1 trillion directed towards administrative costs and the remaining $3 trillion allocated to care delivery. LLMs can offer a transformative solution for reducing healthcare administrative expenses by automating tasks. It can streamline documentation, report generation, data summarization, and language translation. LLMs also enhance patient education, telehealth services, and workflow efficiency, freeing up time for healthcare professionals to focus on better patient care. Implemented by companies like Bayer Pharma, HCA Healthcare, and MEDITECH, showcases their ability to expedite clinical trials and improve documentation accuracy and
speed, thereby demonstrating their potential to streamline administrative processes and drive significant cost reductions in the healthcare sector.
Beyond the administrative tasks, we envision clinical agents that can do initial screening and suggest diagnoses to doctors. By efficiently managing administrative duties and conducting basic screening, these models can free up valuable time for healthcare professionals, enabling them to focus more on patient care and complex medical tasks, leading to increased productivity and improved care delivery. Other AI assistants for Healthcare workflow automation such as Navina and Synthpop.ai also display how administrative tasks can be automated.
2-Transforming software interaction
The current efficiency of human interaction with software remains suboptimal. We still have to navigate through multiple apps to plan for a vacation or book an appointment. However, LLMs with their ability to understand context, natural language, as well as their in-depth understanding of the world, presents an opportunity to redefine our daily interactions with software. Think of interactive AI, a new paradigm where AI systems can act on high-level goals, interact with other AIs, and potentially collaborate with humans to achieve tasks. This shift has the potential to reshape how we interact with technology and automate various processes, offering exciting investment opportunities. Interactive AI is capable of carrying out tasks beyond text-based conversations. These AI systems will have the ability to execute tasks set by users, utilizing other software and even involving human collaboration. This shift marks a profound transformation in technology, where AI will have a level of autonomy to take actions on behalf of users. It opens new possibilities for more dynamic and efficient interactions with technology. Companies like Inflection.ai are pioneering this interactive AI wave, and it represents an exciting frontier for investment and innovation.
3-Elevating Digital Production
Generative AI extends beyond LLMs and text, encompassing diverse models like diffusion models, that are text-to-image creators, enabling the creation of images, videos, and audio, proving to be a valuable asset for professionals involved in tasks such as marketing illustrations, video creation/editing, presentation design, interior design or architects, etc. Additionally, these tools hold promise for individuals who may have felt unskilled in creating images or music, echoing the way Canva once democratized graphic design for non-artists. Some aim to challenge established players like Adobe and Microsoft, while others focus on strengthening social media engagement and e-commerce capabilities through network effects and advertising. A good illustration of this is Runway ML, bringing a production studio to consumers’ fingertips. Other notable examples include Genmo for Short-form video, Soundraw.io for Music, Sloyd in 3D asset generation.
4-Boosting productivity
Utilizing LLMs and generative AI has the potential to transform personal efficiency by tackling daily challenges, ranging from booking a flight and a hotel, to recommending new destinations based on your life circumstances, and placing an order from Doordash without needing to open the app. The prospect of AI-powered personal assistants or coaches holds vast potential, envisioning an AI-driven companion capable of optimizing time and resources. One where a user can have a much more nuanced conversation. These AI advancements promise to significantly improve efficiency, decision-making, and workflow streamlining, impacting both personal and professional domains. From assisting in entertainment recommendations to automating tasks like sending emails and schedule management, helping you choose your next TV and many more everyday use cases to help you spend more time on creative tasks. A notable example includes Ninja.ai where its AI assistants aim to efficiently support professionals in reaching their objectives.
5-Democratizing Education
As we examine the landscape of student needs, we’ve pinpointed four crucial problem areas: Access, Content, Assessment, and Personalization. While companies are actively addressing the first two aspects, the needs in Assessment and Personalization remain underserved, presenting untapped opportunities for innovation. By unlocking the skills data needed to drive success and by a contextual understanding of a student’s needs to deliver personalized learning experiences through tailored content, adaptive learning paths, and automated grading, generative AI has the potential to redefine education.
The promises made by CEOs of leading companies such as Coursera, edX, Pearson, and Anthology reinforce the transformative impact anticipated in the Ed-tech space. Anthology’s collaboration with OpenAI to develop tools like a course-building aide and Pearson’s commitment to enhancing the Pearson+ app with ChatGPT-powered features, including automatic summarization of video content and AI-powered chatbots, showcase the tangible applications of generative AI in addressing educational challenges. Companies like edX and Coursera, with their ChatGPT plugins, underscore the industry’s commitment to providing learners with personalized assistance, learning materials, and career guidance through the integration of generative AI.
Conclusion
As we navigate this transformative phase, we’re left with more questions than answers. What will define the new competitive advantages, and can AI-native startups be able to win major segments of the market in this evolving landscape or will incumbents leverage their distribution network advantages to dominate the market? With increased adoption, who will own customers’ data and how do we address privacy concerns?
Despite these uncertainties, the transformative potential of generative AI in revolutionizing creativity, productivity, and software interaction is evident. We believe a future looms where every individual will have AI companions amplifying their capabilities and augmenting their intelligence. This integration of AI not only foresees heightened productivity and economic growth but also a renaissance in creativity and cost reduction. The trajectory from text-based conversational interfaces to emerging modalities like generative user interfaces and human-sounding voices signifies a paradigm shift in how we interact with digital information.
The strategic approach to investment in this realm demands a nuanced understanding of the technology’s short-term and long-term impacts. The looming challenge lies in striking a balance between recognizing the transformative power of generative AI and navigating the risks associated with its implementation, with an eye on where economic moats will be deepest and value pools will accrue in the years to come.
Our investment strategy is rooted in the belief that AI advancements will create new opportunities and disrupt existing sectors while forming new ones. However, we’re mindful of the patterns seen in technological revolutions, characterized by cycles of excitement, frenzied investment, asset bubbles, and market corrections. As we venture into this dynamic era, we remain vigilant, adaptable, and committed to harnessing generative AI’s potential while navigating its challenges with caution and foresight.
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