Playbook
Tasteful Skills
“Tasteful Skills” argues that the best agent skills are not documentation or best-practice lists.
Back to Podcast Digest
The Trillion Dollar Agentic Workflow Opportunity Is Here
TL;DR
This isn’t just an agents story — it’s a finance-and-deployment story — Nate B Jones argues the real shift is private equity, hyperscalers, and enterprises all converging on agentic workflow implementation, not just better models.
Private equity sees trillions in workflow automation because SaaS no longer “tastes like chicken” — PE firms with 2026-2028 exits are under pressure as legacy SaaS growth deteriorates, so they’re backing deployment plays like Anthropic’s reported $1.5 billion venture with Blackstone, Hellman & Friedman, and Goldman Sachs.
OpenAI and Anthropic are signaling that the bottleneck is no longer the model — both labs are moving down-stack into forward-deployed engineering and enterprise implementation, with OpenAI reportedly pursuing a deployment venture near $10 billion and its own Frontier Alliances post saying the bottleneck is how agents are built and operated inside companies.
The value is in finishing the workflow, not demoing intelligence — Nate says the “disproportionate value” comes from getting agents to 100% of an end-to-end workflow reliably, which he calls a new “2026 spring phenomenon” rather than the old co-pilot/chat pattern.
Generic AI wrappers are getting squeezed from four directions — frontier labs moving into product, consultancies like McKinsey, BCG, Accenture, and Capgemini moving into agentic delivery, systems of record like Salesforce/ServiceNow/Workday tightening their platforms, and PE becoming a powerful portfolio-wide distribution channel.
The implementation layer is the real moat — workflow design, permissions, evals, audit trails, rollback, and ongoing ownership determine enterprise value more than raw model access, which is why Nate says builders should “sit closer to the business object” rather than sell generic intelligence.
The Breakdown
The real story: finance, hyperscalers, and enterprises collide
Nate opens by reframing the whole “agents” conversation: this isn’t mainly about clever AI products, it’s about software finance breaking, hyperscalers learning what actually works, and enterprises suddenly wanting real deployment help. His hook is memorable and blunt — private equity used to say “SaaS companies all taste like chicken,” because they were easy to model and value, but AI has scrambled that playbook.
Why private equity suddenly cares so much
He says PE firms holding companies for 2026, 2027, and 2028 exits are now staring at SaaS assets that may be “on the rocks” if they can’t become AI-relevant. That’s why PE is pivoting hard into agentic workflows: not as a side bet, but as a survival and value-creation move, because they see trillions of dollars in workflows that can now be automated end to end.
Labs learned they need people in the trenches
Nate says hyperscalers have realized they can’t stay in “fancy brick-walled Silicon Valley conference rooms” and talk abstractly about implementation. Palantir’s forward-deployed engineer model looks right to him, and he says OpenAI and Anthropic have figured that out too — but because they’re still capital constrained despite huge fundraising, they need partners, which makes PE an ideal financing ally.
Enterprises finally got the difference between chat and agents
He describes a sharp behavioral shift since December: companies that were stuck in copilots and chat interfaces are now seeing enough examples to understand what agents can actually do. The exciting part for him is that full workflow completion — getting to a reliable 100% on real work — is now newly plausible at scale, which is why companies are effectively begging OpenAI, Anthropic, and consultancies to come sit down and help.
The money is moving into deployment, not just products
His proof point is the capital stack: Anthropic has announced a deployment company with Blackstone, Hellman & Friedman, and Goldman Sachs, reportedly backed by $1.5 billion, while OpenAI is chasing a similar opportunity at a valuation near $10 billion. That matters because, in his telling, the market is taking these implementation-heavy bets more seriously than many standalone AI products.
Four pressures are squeezing generic enterprise AI
Then he lays out the “squeeze” in four axes: labs are moving down-stack into applications and deployment, consultancies like McKinsey, BCG, Accenture, Capgemini, and PwC are moving up-stack into production agent delivery, systems of record like Salesforce, ServiceNow, Workday, and SAP are exposing frameworks that keep agents inside their platforms, and private equity is turning into a portfolio-wide distribution engine. His warning is direct: if you’re just shipping a generic wrapper with some data access, you’re getting boxed in from every direction.
What the implementation layer actually includes
He gets concrete here, defining the implementation layer as workflow design, data access rules, authority boundaries, evals, audit trails, recovery, and ongoing ownership. A model can sound confident off a six-month-old PDF or a live record, he says, but the implementation layer determines which one it sees — and that detail is what makes an enterprise system real instead of theatrical.
His strategy takeaway: sit closer to the business object
Nate’s practical advice is to anchor products around the objects and actions that define work: support cases, policies, entitlements, escalations; or sales objects across the full funnel. He closes by saying the old generic-SaaS mindset is dead, “the disproportionate value in agentic workflows is in customization,” and that the implementation layer war is wide open — encouraging news, in his view, for entrepreneurs willing to build deep into the fabric of real enterprise workflows.
Share
Keep Reading
Two Ways to Follow
The Weekly Echo. The inbox-shaped summary of what mattered.
New editorials announced here.
