# George Fraser: AI agents require centralized data for effectiveness, the rise of AI native companies threatens traditional software, and strategies to restrict data access are emerging | AI + a16z > Source: > Published: 2026-06-02 23:08:38+00:00 # George Fraser: AI agents require centralized data for effectiveness, the rise of AI native companies threatens traditional software, and strategies to restrict data access are emerging | AI + a16z AI agents need comprehensive data context to function effectively, which is driving changes in data management practices. The rise of AI native companies poses a significant threat to traditional e... ## Key takeaways - AI agents need comprehensive data context to function effectively, which is driving changes in data management practices. - The rise of AI native companies poses a significant threat to traditional enterprise software incumbents. - Data infrastructure is now being developed not only for business intelligence but also to support AI capabilities. - Centralized data is crucial for AI agents to operate effectively in business environments. - Some companies are attempting to restrict data access as a strategy to protect their interests against AI advancements. - SAP has introduced a new API policy that restricts AI agent access to its data, highlighting a trend among major vendors. - The ability of AI agents to access data directly could reduce the value of traditional SaaS applications. - There is a growing concern that AI agents will replace human users, diminishing the value of long-established business systems. - The fears surrounding closed APIs are not new and may be overstated, as historical patterns show. - Software costs are relatively minor compared to overall business expenses, making them a small part of company budgets. - The integration of AI into business processes requires modifications to existing data foundations. - AI-driven companies are rapidly catching up to established software firms, potentially outperforming them. - The shift towards AI capabilities in data infrastructure reflects a broader trend in the enterprise software industry. ## Guest intro George Fraser is cofounder and CEO of Fivetran, where he leads the company’s data integration platform for modern enterprise data infrastructure. He has built Fivetran into a leading player in data movement and infrastructure, making him a frequent voice on how businesses manage and access data in the age of AI. ## The role of AI agents in data management - AI agents require context to function effectively, necessitating centralized data management. - AI agents need context if you don’t do that then it’s sort of like using chatgpt from before chatgpt was connected to the internet — George Fraser - The shift in data management is driven by the needs of AI technology. - Companies are building data infrastructure not just for business intelligence but for AI capabilities. - For years companies built data infrastructure to answer questions about the business now they’re building it for ai — George Fraser - AI agents accessing data directly could diminish the value of traditional SaaS applications. - The concern is my saas app has less value as an interface because now the agents can access the data directly — George Fraser - Understanding the role of AI agents is crucial for adapting to changes in data management practices. ## The rise of AI native companies - AI native companies may surpass established incumbents in the enterprise software space. - The bigger threat is that ai native companies will just zoom and catch up to the established incumbents — George Fraser - The competitive landscape is shifting with the emergence of AI-driven firms. - Traditional software companies face disruption from AI advancements. - AI-driven companies are rapidly catching up to established software firms. - The potential for AI companies to outperform traditional markets is significant. - The rise of AI native companies reflects a broader trend in the software industry. - Understanding this trend is crucial for navigating the future of enterprise software. ## Centralized data and AI functionalities - AI agents require context from centralized data to function effectively in business. - AI agents need context and it turns out that the same data foundations that work well for business intelligence can work for ai agents — George Fraser - Centralized data is crucial for enabling AI functionalities in business applications. - The integration of AI into business processes requires modifications to existing data foundations. - Companies are adapting their data infrastructure to support AI capabilities. - Understanding the role of centralized data is critical for successful AI integration. - The shift towards centralized data reflects a broader trend in enterprise software. - Centralized data plays a key role in the effectiveness of AI agents in business. ## Companies’ strategies in response to AI - Some companies are reacting to AI by attempting to restrict data access. - We have seen some companies start to think that a great strategy for dealing with ai might be to lock it out — George Fraser - SAP has implemented a new API policy restricting AI agent access to its data. - SAP announced a new api policy that literally said all ai agent access was banned except in a way specifically approved by sap — George Fraser - The strategy of restricting data access reflects a broader trend among major vendors. - Companies are adopting new strategies to protect their interests against AI advancements. - Understanding these strategies is crucial for navigating the competitive landscape. - The trend of restricting data access highlights the challenges posed by AI. ## Impact of AI on traditional SaaS applications - AI agents accessing data directly could reduce the value of traditional SaaS applications. - The concern is my saas app has less value as an interface because now the agents can access the data directly — George Fraser - Companies are worried that their long-built systems will lose value as AI agents replace human users. - People are worried that their systems that they’ve spent many years building will simply be less valuable — George Fraser - The implications of AI agents on SaaS applications are significant. - Understanding these implications is crucial for adapting to changes in the software industry. - The impact of AI on traditional SaaS applications reflects a broader trend in enterprise software. - The transition from human to AI users poses challenges for existing software solutions. ## Concerns about closed APIs - The concerns about APIs being closed off are overstated and not new. - A lot of these threats are not new… the rhetoric was exactly the same… they reacted exactly we could never open up apis — George Fraser - Historical patterns show that fears surrounding closed APIs may be exaggerated. - Understanding the historical context of API usage is crucial for navigating the software industry. - The evolution of API concerns reflects broader trends in software development. - The relevance of API concerns in today’s software landscape is debated. - The concerns about closed APIs highlight the challenges posed by AI advancements. - Understanding these challenges is crucial for successful software development. ## Economic perspective on software costs - Software costs are relatively immaterial compared to overall business expenses. - If you look at the budgets of real companies… software compared to everything else a typical business spends money on is so cheap — George Fraser - Software expenditure is a small part of company budgets. - The economic perspective on software spending highlights its low impact on business costs. - Understanding the role of software costs in business budgeting is crucial for financial planning. - The relative insignificance of software costs reflects broader trends in enterprise software. - The economic perspective on software costs is important for navigating the software industry. - Understanding these trends is crucial for successful financial planning in business. ## Modifications to data foundations for AI - The integration of AI into business processes requires modifications to existing data foundations. - The same data foundations that work well for business intelligence can work for ai agents with some additions and some modifications — George Fraser - Companies are adapting their data infrastructure to support AI capabilities. - Understanding the modifications needed for AI integration is crucial for successful data management. - The shift towards AI capabilities in data infrastructure reflects broader trends in enterprise software. - The role of data foundations in supporting AI functionalities is significant. - Understanding these trends is crucial for successful AI integration in business. - The modifications to data foundations highlight the challenges posed by AI advancements. **Disclosure:** This article was edited by Editorial Team. 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