Agentic AI Agents Big Tech Impact

Agentic AI agents are expected to sit between users and the web, automatically doing the “clicking and browsing,” which could pull power away from many consumer apps and sites and toward core AI and infrastructure platforms.

From apps to agents

• CNBC’s Dierdre Bosa contrasts chatbots (one question, one answer) with agents that run autonomously over vastly more tokens, continuously acting on the user’s behalf.
• Her example: instead of you visiting ten hotel sites, reading reviews, and booking, an agent already knows your preferences and books the right option without you ever hitting a travel website’s front door.

How this reshapes the internet

• Traffic and value may shift from front-end destinations (websites, branded apps, comparison portals) to an AI “intermediation layer” that decides which services to call under the hood.
• For users, this could feel like skipping search, navigation, and forms almost entirely, moving toward “tell the agent the outcome, it handles the web.”

Implications for big tech

• Cloud and chip leaders benefit because agents consume far more compute and API calls than simple chatbots, reinforcing demand for hyperscale infrastructure and advanced GPUs.
• Enterprise platforms that are easy for agents to plug into (good APIs, event-driven architecture, agent frameworks) may gain share, while UI-heavy incumbents that depend on eyeballs and ad impressions risk disintermediation.

Category	Why they could win or lose
Hyperscale clouds & GPUs	Agents run long, complex workflows, driving more compute, storage, and specialized chips.
Payment/infra rails (Stripe, Plaid, etc.)	Become the invisible plumbing agents call repeatedly to execute actions.
AI platforms & agent frameworks	Provide the orchestration, memory, and tools that make agents reliable at scale.
Ad/search-driven front ends	Risk losing traffic if agents bypass sites and ads to go directly to underlying services.
Vertical SaaS with strong APIs	Better positioned if they expose capabilities as services agents can compose.

 

Hyperscale clouds & GPUs

• Microsoft (MSFT) Agents mean more long‑running workflows on Azure (planning, booking, monitoring), so Azure AI and OpenAI‑style services should see higher GPU utilization and stickier enterprise spend. Revenue mix tilts further to high‑margin cloud/AI services, supporting continued capex and premium multiples, though there is risk of margin pressure if agent platforms demand better pricing.
• Alphabet/Google (GOOG) As agents handle search‑like tasks directly (trip planning, shopping, research), gross query volume hitting classic search and ads could flatten, but Google’s own agent layer (Gemini + extensions) can capture that traffic if they execute. In a successful transition, more spend shifts from keyword ads to “outcome‑based” or API‑based monetization, with upside in cloud and TPU usage but real risk to legacy ad units if migration is slow.

Payment/infra rails

• Block (SQ) If agents are constantly initiating small business payments, invoicing, and settlement, Block’s APIs benefit from higher transaction counts and deeper embedding into vertical workflows. The flip side is margin pressure as more of the value accrues to the agent platforms orchestrating payments, potentially forcing more competitive pricing on rails.
• Adyen (ADYEY) / PayPal (PYPL) Being the “default” payment rail for agents that book travel, subscriptions, or B2B services could raise volumes without consumer‑facing CAC. But if agents increasingly choose the cheapest or most reliable rail programmatically, it commoditizes some aspects of payments, favoring scale and reliability over brand.

AI platforms & agent frameworks

• Snowflake (SNOW) If enterprises build agentic workflows around a central data layer, Snowflake can become the “memory” and tooling hub for agents, driving more compute credits and marketplace usage. Risk is that some agent platforms abstract away the data layer, pushing more of the value to the orchestration layer rather than the warehouse itself.
• ServiceNow (NOW) Agents that automate tickets, approvals, and workflows inside enterprises can sit on top of ServiceNow’s platform, increasing seat penetration and module attach. Over time, though, some UI‑heavy components could matter less if agents interact mainly via APIs, pushing ServiceNow to double down on platform extensibility.

Ad/search‑driven front ends

• Meta Platforms (META) If users rely on agents to discover and buy products, fewer may scroll discovery‑oriented feeds, pressuring impression growth and some ad formats. Meta can offset this by making its own agents and messaging surfaces the place where commerce agents operate, but that requires shifting from ads‑only to more transaction and API revenue.
• Pinterest (PINS) A lot of its value is in inspiration and discovery—areas where agents can do the curation and selection without user browsing the app. Unless Pinterest becomes a first‑class surface or data provider to agents, its ad inventory could see slower growth as agents synthesize options for users elsewhere.

Vertical SaaS with strong APIs

• Shopify (SHOP) Merchant‑side agents (inventory, pricing, CX) and consumer‑side agents (shopping on behalf of users) can both route orders through Shopify if its APIs remain the default e‑commerce plumbing. That said, if agents aggregate across platforms and push for lowest‑cost fulfillment, Shopify’s take rate could face pressure in some segments.
• Toast (TOST) Restaurant‑oriented agents handling reservations, orders, and supply could integrate deeply with Toast if its APIs are robust, increasing attachment of modules and data‑driven upsells. However, front‑end ordering apps or loyalty experiences could become less important if agents bypass them, shifting value back to infra and back‑of‑house automation.