Agentic AI Is All About Productivity, Says Blumberg Capital's Bruce Taragin — But We're Just Getting Started

What Bruce Taragin told the NYSE at Uncharted

Speaking at the Uncharted conference — an invite-only gathering of investors and founders hosted by UBS and entrepreneur Michael Loeb in New York — Blumberg Capital Managing Director Bruce Taragin sat down with the NYSE's Ashley Mastronardi to frame agentic AI in historical context (full interview on Yahoo Finance).

Taragin has backed companies through multiple technology shifts — from the internet and mobile to cloud computing. His portfolio at Blumberg Capital, an early-stage venture firm, includes names like Braze (customer engagement, IPO'd as BRZE), DoubleVerify (digital ad verification, DV), Nutanix (cloud infrastructure, NTNX), and BioCatch (behavioral biometrics). The common thread: every one of these companies rode a technology cycle that changed how businesses operate.

"Each major shift has created new ways for businesses to operate and grow," Taragin said, positioning agentic AI as the next in line. His central thesis: agentic AI is fundamentally about productivity. Not chatbots. Not content generation. Autonomous software agents that plan, execute multi-step tasks, and interact with tools — that's where the real enterprise value sits.

The quote that will stick: "If you think of it as a journey from the Washington Bridge in New York to the Golden Gate Bridge in San Francisco, we're still on the Washington Bridge." Translation: enjoy the demos, but don't expect to flip a switch and have AI running your operations tomorrow (Bruce Taragin on LinkedIn).

The state of agentic AI: mid-2026 reality check

Taragin's Washington Bridge metaphor lands at an interesting time. The same week, Deloitte US CEO Jason Girzadas published a piece arguing that "meeting the moment in agentic AI requires intention, focus, and organizational alignment" (WSJ/Deloitte CEO Perspectives). The subtext from both the venture capital and consulting sides: the technology is real, but the enterprise readiness gap is wide.

Let's look at what's actually shipping. Salesforce Agentforce is live, priced at roughly $2 per agent conversation (approximately €1.80). Microsoft Copilot Studio lets enterprises build autonomous agents at $200/month for 25,000 messages (about €180). OpenAI's Operator — an agent that controls a browser to book flights, fill forms, order groceries — is available to ChatGPT Pro subscribers at $200/month (€180/month). Anthropic's Claude Computer Use, which lets the model move a cursor and click, ships via API at standard token rates ($3/$15 per million input/output tokens for Sonnet, roughly €2.70/€13.50).

Google's Gemini agents and open-weight alternatives like LangChain/LangGraph and CrewAI round out the picture. But none of them are what you'd call "enterprise plug-and-play." Every deployment I've seen — including our own experiments running AI pipelines on local hardware — requires significant engineering around error handling, state management, and guardrails.

The European angle: EU AI Act meets autonomous agents

For European enterprises, the timeline has an extra layer. The EU AI Act enters its high-risk enforcement phase in August 2026 — literally weeks away. Fines reach up to €30 million or 6% of global annual turnover, whichever is higher. And here's the catch: the Act wasn't written with autonomous AI agents in mind, but autonomy is exactly what pushes risk classification upward (RatedWithAI analysis).

An agent that autonomously screens job applications? That's high-risk under the AI Act's employment provisions. An agent that decides credit approvals? Also high-risk. Even lower-risk deployments trigger transparency obligations under Article 50 — users must know they're interacting with an AI system, and AI-generated content must be labeled.

The compliance cost isn't trivial either. For a mid-size enterprise deploying an agentic architecture, early estimates from firms specializing in AI Act readiness put the initial compliance investment at $8–15 million (€7.2–13.5 million), covering system inventory, gap analysis, architectural remediation, documentation, and conformity assessment (StackAhead AI).

This creates a structural gap between the US and EU markets. Taragin's Blumberg Capital portfolio is US-heavy — most agentic AI startups are shipping first in the US where the regulatory burden is lighter. European companies deploying these tools will need to layer their own compliance stack on top.

The practical consequence for European businesses: you can't just sign up for Salesforce Agentforce or OpenAI Operator and call it a day. You'll need to document which AI systems are in use, classify their risk level, implement human oversight mechanisms, and maintain technical documentation — all before the August deadline.

What enterprises should actually do

Taragin's Washington Bridge framing is useful here. It's not a reason to wait — it's a reason to start small and build internal capability. Here's what that looks like in practice:

Start with internal, non-customer-facing use cases. Code review agents, documentation generators, data pipeline monitors — these carry lower regulatory risk and let your team learn the operational patterns. We run several AI agents internally at ai-jarvis.eu for article research and content pipelines, and the biggest lesson we've learned is that error handling matters more than prompt engineering.

Pick platforms with EU data residency. Microsoft's Copilot Studio offers EU data regions. Salesforce has EU instances for Agentforce. If you're going the API route with Anthropic or OpenAI, check where inference runs — both now offer European data processing via their respective cloud partnerships.

Budget for compliance from day one. The $8–15 million figure isn't a one-time cost if you do it right. Build AI Act compliance into your agentic architecture — audit logging, human-in-the-loop override mechanisms, and decision traceability — and it becomes a competitive advantage rather than a bolt-on expense.

Keep an eye on the open-weight ecosystem. Models like Mistral Large (Paris-based, EU-sovereign) and Llama 4 running on European cloud infrastructure give you full control over data flows — a non-trivial advantage when GDPR regulators come asking questions.

Taragin closed his Uncharted talk by referencing founders like DoubleVerify's Oren Netzer — entrepreneurs who built category-defining companies by spotting the productivity shift early and executing before the market caught up. The message is clear: the opportunity is real, the timeline is years not months, and the companies that invest in genuine capability — not just press releases — will be the ones crossing the Golden Gate Bridge first.