A leap forward
While the hype around GenAI is a recent phenomenon, the technology is not new. We can trace its roots to the advances in machine learning that emerged in the 1950s, and accelerated in earnest in the 1990s and 2000s when appropriate hardware and digital data became widely available. Famously, this resulted in a defeat for chess grandmaster Garry Kasparov at the hands of IBM’s Deep Blue in 1997 and, more recently, when AlphaGo – built by Google’s DeepMind Technologies using artificial neural networks – beat Go world champion Ke Jie in 2017.
Most of us, however, only became aware of the power of GenAI in 2022, when the technology was put in the hands of consumers with the release of several text-to-image models. These were quickly followed by OpenAI’s ChatGPT-3, trained on conversational dialogue, which apparently had an answer to all of our questions. Venture capital poured into the space, with investors creating near-instant unicorns in Jasper and Stability AI – while GitHub’s Copilot, a tool that assists developers by converting natural language into executable code, saw widespread adoption.
The release of these technologies has been revolutionary. Within five days of its launch, ChatGPT had one million users, a feat that took Twitter 24 months and Netflix almost three and a half years to achieve. By the six-month mark, ChatGPT had reached 1.8 billion monthly users worldwide.
What separates GenAI from the AI technologies of the past is its unique ability to create. It can generate new content, including audio, art, and text, all by learning from a data set without explicit instructions. Moreover, GenAI has proven itself to be astoundingly intelligent. It has successfully qualified as a lawyer, scored 75% on the American Medical Exam, and passed the final exam at Wharton Business School.
We are only scratching the surface of what the technology can achieve. Today’s GenAI is the least advanced version we will ever see. It is constantly evolving and improving, with no risk of forgetting what it has learned.
Challenges to overcome
While its potential is enormous and the pace of development rapid, there are some challenges to overcome before GenAI will achieve widespread business adoption.
Data and infrastructure quality in most companies remains a barrier. In addition, businesses are grappling with issues relating to intellectual property infringement. A class action lawsuit brought against Microsoft, its subsidiary GitHub, and partner OpenAI alleges that GitHub’s coding assistant is responsible for software piracy, by training its model on open-source code and failing to comply with licence agreements.
Governments and agencies are working to impose limits on the technology’s capabilities. The EU has developed the framework for an EU AI Act that will govern AI and its uses, and is now negotiating the details with member states. The US has also acted, with President Biden issuing an Executive Order to ensure the “safe, secure, and trustworthy development and use of artificial intelligence”.
The winners so far
Despite the challenges, the commercial benefits of GenAI are becoming increasingly apparent, particularly for the large players. In 2023, Nvidia, the leading manufacturer of chips for AI, joined the elite group of companies with market caps of over $1 trillion. Microsoft’s CFO, Amy Hood, forecast that its suite of GenAI tools would achieve $10 billion of revenues faster than any other business in the company’s history. Google claimed that AI is helping its advertisers generate over 50% more reach for 60% of the cost – and also claimed that more than half of all funded GenAI start-ups are Google Cloud customers.
The benefits of GenAI are also becoming apparent across a broad array of industries. In healthcare, it is being used to create synthetic data for research. In the entertainment industry, it is generating special effects for movies and developing new video game experiences. And fashion professionals are using GenAI to predict new trends and create virtual designs.
Every successful business will be an AI company
Given these trends, we at Bowmark believe that GenAI will become an integral part of every successful company. In essence, all companies will need to become AI businesses in order to differentiate their products and services effectively and/or to optimise their economic models. Competitive differentiation through AI will generate new revenue opportunities, while process automation will help drive down costs, particularly for knowledge workers.
We have refined our investment strategy to capitalise on these opportunities, as well as to mitigate any potential risks from AI. Sustainable differentiation, proprietary data, cloud native infrastructure and technology partner alignment are just some of the areas gaining even more focus, both across our portfolio and in new investments. Equally important is company culture and the capability to evolve with, and embrace, the inevitable changes.
We are also working closely with the management teams of our portfolio companies to help them formulate and execute effective AI strategies that deliver sustainable competitive advantage. Bowmark’s Value Creation Team has established dedicated cross-portfolio working groups, is continuously sharing best practice, and has introduced industry experts to help our companies develop and implement AI use cases. In addition, we have invested in data infrastructure and recruited new talent to support GenAI initiatives.
Some of our portfolio companies have already made great strides in incorporating GenAI into their operations. IWSR is focused on opportunities to use AI to ingest qualitative data, generate written insights, and improve its predictive forecasting in the global beverage market. WSD, a provider of workflow automation software solutions, is set to launch a new AI-enabled product that will enhance efficiency and quality, ultimately driving revenue growth through product differentiation. Kubrick, a next-gen consultancy specialising in data, AI and cloud, has started delivering GenAI proof-of-concept and proof-of-value projects and is also providing the AI engineering and data governance skills needed by clients. Totalmobile, the field service management software provider, is embedding AI functionality into its scheduling, rostering, mobile workforce, and route planning tools.
GenAI will become one of the most important factors shaping business and the global economy. We are helping our portfolio companies to understand its applications, invest in the right areas and balance the inevitable conflicts between technology and humans. We are in no doubt that GenAI represents a very exciting opportunity for value creation and are working closely with our management teams to develop tangible GenAI strategies for the long-term.
