AI Is Bringing Mashups Back · Mark Wolfe's Blog
A mashup<br>is a term that became popular in the 2010s for applications that combined two or more data sources into a single user interface. Recently, I have seen a steady stream of AI-generated side projects, small apps, slide decks, and useful tools showing up on social media, often built by people who have never tried this before. With the advent of ChatGPT<br>and Claude<br>, it has never been easier to integrate disparate data and build something useful.<br>These mashups are nothing new, but the barrier to entry is much lower, and the scale of what is possible has changed as AI models and tools improve.<br>I have also seen these mashups at work, which is fantastic because it gives more people the ability to build tools and explore problems without waiting for software engineering teams.<br>This is useful for a few reasons:<br>provides more value from internal data sources<br>includes visualisations that highlight trends or patterns<br>requires minimal programming skills<br>What is new about this?<br>The key differentiator is the scale and complexity of these mashups. With the help of tools like Claude Code<br>, Codex<br>, and Cursor<br>, people can build functional web or mobile applications.<br>This matters for a few reasons:<br>It is easier than ever to integrate disparate services using APIs.<br>Agents can respond to queries and follow up with users.<br>Integrating authentication or data storage is easier than ever.<br>The growth of agent skills<br>has unlocked even more capabilities, allowing people to package and share repeatable processes.<br>Overall, this has led to a Cambrian explosion of mashups, as a wave of new builders publish and share their projects.<br>How will this affect existing SaaS providers?<br>After chatting with my peers at events such as the AI Engineer conference in Melbourne<br>, it is clear that people with domain knowledge and a clear problem to solve are building mashups that automate day to day tasks and provide better operational reporting.<br>Businesses are building their own portals, combining feeds of data from multiple platforms with tools such as openclaw<br>to act as the nerve centre for their business. These portals combine APIs, Model Context Protocol (MCP)<br>servers, messaging, and agents to drive day to day operational tasks and provide integrated reporting that would be difficult for any one platform to deliver.<br>For example, a team might combine CRM data, email notifications from an online store, and supplier invoices into a small internal portal that shows business health and triggers follow-up actions through openclaw.<br>As a side effect of this, SaaS providers are seeing:<br>Less traffic to their user interfaces, with people using them mainly when configuring new features or diagnosing more complex issues.<br>Widespread use of MCP, which provides a more accessible interface for agents.<br>More API traffic from customers.<br>I think it is important to understand that the people building these mashups are often just looking to solve their own business problems in ways that are specific to their business.<br>How can platforms better support mashups?<br>If platforms want to support this new wave of mashups, then composability needs to be a priority.<br>Some areas to focus on are:<br>Stable documented APIs, with versioning, predictable schemas, useful errors, and clear deprecation windows.<br>Solid authentication for delegated access, including standardised OAuth/OIDC flows with scoped permissions that make sense to users and developers.<br>Fine-grained permissions, because a mashup might only need read access to one dataset, one workspace, or one resource type.<br>Security controls that protect customers as their mashups evolve, including audit logs, policy enforcement, token expiry, and clear visibility into connected tools.<br>Webhooks and event streams, with support for replay, signing, delivery logs, and retry behaviour so integrations do not need to rely on polling.<br>Reasonable rate limits, which should be visible, documented, and returned in headers.<br>Examples that compose multiple services and show real integration patterns: sync jobs, event-driven workflows, import/export, embedded views, and permission handling.<br>The most successful platforms will make integration easy by providing stable primitives, clear boundaries, and well-documented capabilities.<br>Summary<br>Mashups have seen a resurgence because AI tools make it much easier for anyone to combine data, APIs, and user interfaces without waiting for a full software delivery process.<br>Key points:<br>Mashups are not new, but the barrier to entry is much lower, and the scale of what is possible has changed as AI models and tools improve.<br>More people can now build useful internal tools from existing data sources.<br>SaaS providers should expect more API and MCP usage, and less reliance on their default user interfaces.<br>Good security controls help keep customers safe while these mashups evolve from experiments into operational tools.<br>Platforms that invest in composability, stable APIs,...