Investments in technology flocked to early-stage generative AI companies in the first half of 2023, led by Microsoft’s $10 billion investment in OpenAI (see Figure 1).

Figure 1

Artificial intelligence and machine learning solutions led venture and growth funding in the first half of 2023

With the potential for sweeping changes to the tech sector, investors are rightly afraid of the ways that generative AI technologies can affect current and future tech assets. The excitement raises a number of possibly overwhelming choices for investors: How will AI affect our portfolio companies? Which business models will change, and what new opportunities will present themselves? How do we adjust diligence criteria for future investments? Are there ways we should deploy generative AI to improve our own internal operations?

Broad impacts in software

As the nature of human-to-computer interactions evolves, customer expectations are growing. Generative AI–powered chat interfaces for applications and data simplify the user interface, increase the localization and personalization of content, and open new routes to market.

New products will emerge, many of which will automate and augment the work of people in specific roles across sectors (see Figure 2). Software developers, for example, will become more efficient as AI-coding assistants supplement their efforts. Workers in other roles, such as customer support, technical field services, and sales and marketing, could all be augmented by generative AI. Start-ups and other small companies with fewer resources may be able to deliver new products more rapidly when assisted by AI.

Figure 2

Generative AI will have differential impact, depending on the share of automatable and augmentative roles

Experimentation also becomes easier, and barriers to entry are lower given reduced development costs and democratized foundation models at enterprise companies’ disposal. With lower barriers to entry, cycle times come down, requiring incumbents to act quickly to capitalize on the advantages of differentiated data assets, entrenched customer access, and integration into user workflows.

Generative AI, however, introduces both opportunities and risks—for example, new AI features, such as a ChatUX, make it easier for users to engage with a company’s product, but risks also emerge as users seek out other AI-enabled applications that might better address specific use cases, potentially reducing market share.

Competitive landscape implications

Although most investors agree that AI will have a significant effect on the tech sector, the evolution of the competitive landscape remains to be seen.

Tools and enablers. Large language models (LLMs) and other foundation model providers are likely to consolidate, forming a few winners in each category. Open-source models are likely to be part of the evolving landscape, too. We also expect more consolidation for tools supporting generative AI—including cloud providers, system integrators, and specialized semiconductors—as a result of the rising R&D investment required to maintain the pace of innovation. Many of today's largest tech companies will benefit, and leaders, including cloud service providers, OpenAI, and Nvidia, are already seeing record-breaking growth.

For other generative AI tools and enablers (including data and systems and services that facilitate the use of AI), the story is more nuanced. With few incumbent or leading providers, a large number of early-stage companies are likely to arise to provide support for building LLM-based apps in categories such as data management, storage and process capabilities, and AI implementation services. These tools may eventually consolidate as larger platforms eventually provide these services in-house.

Software applications. Beyond tools and enablers, there will be winners and losers among software applications innovating on new and existing use cases. With broad and inexpensive access to democratized foundation models, a flurry of early-stage players will likely develop innovative use cases using existing foundation models. These will include vertical and horizontal applications for use cases that weren’t previously possible.

Among incumbents, software companies that learn to deploy generative AI technology in relevant markets are likely to emerge as winners. Unlike the transition to cloud, the benefits of AI can often be realized without investing in major overhauls of company platforms. On balance, this favors incumbent software providers that have access to data, customer relationships, and a track record of execution. Customer access and customer data protects incumbents from disruption by new competitors and start-ups, but sustained market leadership will depend on how incumbents adopt generative AI to make their products and operations better.

A healthcare IT company encountered this situation as it evaluated opportunities to use generative AI across its product suite. Larger competitors were quickly harnessing generative AI, potentially putting the healthcare IT company’s areas of differentiation at risk. Its position as a specialized provider could be threatened as customers begin to use generative AI tools and consider a broader set of vendors with features that would make their daily operations more productive. To counter those risks, the company set out to rapidly embed generative AI features that would enhance the customer experience and further differentiate its products.

How can funds avoid disruption risks?

Top funds aren’t waiting to see how generative AI changes this space. With shorter cycle times and lower barriers to entry, incumbent advantages will dissipate if they don’t act now.

In assessing whether a market will face significant change from generative AI, investors must consider both disruption potential and structural barriers in the market. Does generative AI have the potential to replace or augment human effort, improve product quality, or reduce costs? What are the structural barriers to entry? Are there legal restrictions or sensitive data involved?

In assessing the company’s ability to capitalize on these opportunities, investors must consider whether they own proprietary data that could enrich generative AI applications. Is the company’s pricing model set up to capture value from generative AI, or will it face pricing pressure? Do you have the talent to execute—and if not, where can you find it? What defensible moats, such as customer stickiness and brand awareness, can you lean on?

By understanding the overall potential for change in markets from generative AI and the ability of assets to navigate that change, top funds are biasing toward action to capitalize on the potential of their incumbent software assets.