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

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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. For more information on how we create and review content, see our Editorial Policy.

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