Unless you’ve been living under a rock for the past six months, you’ll have heard of generative AI – technology that enables computers to create synthetic data or digital content based on previously created data or content. The launch of ChatGPT in late 2022 lit a fire under this emerging space and seemingly overnight, hundreds of millions of people became inspired by the results of work that had already been going on for years within academic and commercial technology vendor research departments.
Earlier in June we spent two days touring around investment banks and hedge funds in London to talk to investors about generative AI and answer their questions.
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We had many great, in-depth discussions. Here are the questions that came up most frequently.
Where is the Value in Generative AI in the Short, Medium, and Long Term?
Today, most of the value is being captured by hardware vendors – most notably NVIDIA, which has seen its share price take off following a sharp upswing in its predicted revenues. As the market leading provider of GPUs with a strong enabling software story and emerging as-a-service play, too, NVIDIA is very well positioned to capitalise on the generative AI boom.
Of course, NVIDIA isn’t the only vendor that potentially stands to benefit; AMD and other semiconductor vendors (including start-ups like Graphcore, Cerebras & Moore Threads) are emerging as challengers, and generative AI platforms will drive storage and networking infrastructure investments too.
In the short to medium term, hyperscale public cloud providers can also expect to benefit significantly. With its early move investing in OpenAI and accelerated investments in generative AI across its software portfolio, Microsoft is in a particularly strong position; but AWS, Google, and Oracle are all also making significant moves in this space.
In the medium-term platform and application vendors also stand to benefit, although the value equation for them is less clear cut. There are significant question marks over which generative AI use cases can support direct monetization, and which will be important to implement from a defensive point of view. Many of the costs associated with managing generative AI models for scale, security, privacy and trust will also fall on their shoulders.
What Will Have to Be True to Make GenAI a Truly Broadly Adopted Technology?
Right now, we’re still in “year zero” for generative AI in a commercial context. There is still a lot of confusion around the technology and its applicability in practical real world use cases.
What is already clear, though, is that publicly shared foundation models delivered as a service (such as those hosted by OpenAI) will only be suitable for a subset of enterprise use cases. For many, enterprises will use fine-tuned, specialised domain-specific models that are made available directly to them on a private (or controlled) basis.
The current state-of-the-art in generative AI yields systems that are prone to accuracy problems, difficult to control and predict, and expensive to run. All of these issues need to be worked on.
Where Are the Implications for the Software Landscape?
Every software vendor that IDC is speaking to is updating or recreating their product roadmaps to incorporate their respective Generative AI strategies. Obviously, this will play out differently across infrastructure, platforms and applications – however there are certain common questions that are being asked:
- Should we develop our own large language models, or should partner with model providers like OpenAI, Anthropic, Cohere and AI21 and tune them for our software capabilities?
- How should we price our new Generative AI features?
- Should we include getting access to customer data to train models as part of a new set of licensing terms and conditions. What do we offer in return (if anything)?
- Do we need to evolve our support models to include service level agreements (SLAs) on accuracy on certain use cases that are being delivered?
Across all these questions, what is clear is that margin protection will be a major question for software vendors over time – especially those with questionable pricing power. In addition, there will be increased requirements for additional levels of support to deal with model, context and data drift. For the application players, there is an increasing likelihood that forms-based computing as a basis for applications will likely disappear over time and certain markets – for example, salesforce automation and human capital management could potentially be redrawn in the medium-term.
As part of these changes, what is becoming clear is that the application vendors that are cloud laggards will be AI laggards, and that platforms will continue to dominate the software landscape.
More importantly, incorporating trusted and responsible AI principles into both product development and customer engagement will move from being a differentiator in the short term to table stakes in the medium term.
What Are the Implications for Developers?
There’s been a significant amount of excitement about the ability of generative AI services (such as GitHub CoPilot, Replit Ghostwriter and Warp AI) to generate code, documentation, test scripts, and more.
Today’s state-of-the-art models are not going to put developers out of work. Rather, for some specific types of development work, and for some particular types of software asset being created, generative AI services are very likely to help developers accelerate their efforts to deliver working software, acting side-by-side with human developers in a “CoPilot” arrangement.
But it’s important to keep things in perspective: when we zoom out to consider the broader software delivery lifecycle, pro-innovation developers happy to experiment with new tools tend to bump into deployment, operations and support professionals who are much more risk averse.
What Are the Implications for Services Providers?
Lastly, many of the investment teams we spoke to were very interested in discussing how professional services (particularly IT services) firms might be impacted by generative AI. Will it bring them major new opportunities? Or will its ability to drive automation of knowledge work mean that it forces providers to cannibalise their own businesses?
Our early research shows that more than 65% of early adopters of generative AI capabilities agree or strongly agree that their need for external services providers will be reduced in the future
The potential impact of generative AI on project delivery is, in some ways, analogous to the potential impact of low- and no-code development tools; if providers can embrace these tools effectively and also deliver trusted solutions to clients, they may find fewer hours are required to deliver projects – but outcomes will be improved for everyone.
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The arrival of Generative AI technologies has created what we believe to be a seminal moment for the industry: it will be so impactful that it will influence everything that comes after it. However, we believe it is just the starting point. We think that Generative AI will trigger a transition to AI Everywhere – moving us from the use of narrow AI for specific use cases to widening AI for a range of use cases simultaneously.
This means that it will impact every element of the technology stack, and also drive a rethink of all horizontal and vertical use cases. However, given the questions around risk and governance, it will also require every organization to develop and incorporate an AI ethics & governance framework to deal with the risks mentioned earlier.
The investors that we spoke to in London agreed that the tech industry needs to take balanced approach to commercializing the opportunity, while also ensure that policies and regulations continue to protect consumers, enterprises and society as a whole.
