In B2B transactions, the payer wants to delay payment to manage float, while the receiver wants funds immediately. This adversarial dynamic incentivizes the use of slow systems like paper checks, hindering modernization that benefits both parties in consumer payments.
Previously, disputing a small charge or arguing for a refund was not worth the time. Now, consumers and businesses can deploy AI agents to handle these negotiations endlessly and for free. This shift will force companies to re-evaluate policies around chargebacks and customer disputes.
Ramp began with corporate cards but expanded into bill pay, treasury, and procurement. These new, fast-growing business lines are projected to soon comprise the majority of its business, showcasing a successful multi-product cross-sell strategy from an initial wedge product.
With AI coding assistants, the barriers to shipping software are eroding. At Ramp, designers and customer support agents are now shipping code to production. This suggests a future where the traditional, siloed Engineering, Product, and Design (EPD) team structure becomes obsolete.
Strict rules can be penny-wise and pound-foolish (e.g., saving on a hotel but losing a deal). The ideal is a shared cultural understanding—a "moral code"—where employees act like owners. Technology can provide context and transparency to foster this culture at scale.
Instead of writing static code, developers may soon define a desired outcome for an LLM. As models improve, they could automatically rewrite the underlying implementation to be more efficient, creating a codebase that "self-heals" and improves over time without direct human intervention.
With an average U.S. business profit margin of 8%, the impact of cost savings is magnified. To net $1 in profit, a company needs to generate about $12 in revenue. Therefore, a tool that saves $1 directly boosts the bottom line by the same amount as a significant revenue increase.
Historically, payroll has dominated corporate expenses. As AI automates knowledge work previously done by humans, a significant portion of the budget will shift. Spend on SaaS, APIs, and model usage will grow from a small percentage to a major line item, displacing traditional labor costs.
Instead of rigid if-then rules, companies can use natural language for expense policies (e.g., "business class for flights over 5 hours"). AI agents interpret and apply these nuanced rules to over 100,000 daily expenses with 99% accuracy, freeing up managers' time.
While AI can easily replicate simple SaaS features (e.g., a server alert), it poses little threat to deeply embedded enterprise systems. The complexity, integrations, and "dark matter" of these platforms create a "hostage" dynamic where ripping them out is impractical, regardless of cloning capabilities.
In the 80s, credit was binary: a high score got a card, a low score got nothing. Capital One pioneered an "information-based strategy," using data to test and price risk for consumers just below the traditional cutoff, effectively creating the modern data-driven lending model.
AI can easily clone a product's user interface. However, a mature product's real defensibility lies in its "dark matter"—the vast, invisible knowledge of countless edge cases, regulatory nuances, and failure modes accumulated over years. This makes true replacement much harder than it appears.