AI to ROI

For three years, the AI infrastructure story has been about chips, power, and capital. In 2026, a fourth variable arrived that the hyperscalers were not prepared for: organized, well-funded, and increasingly successful local opposition to data center development.
Ray and Peter walk through the numbers behind the fight. Data Center Watch tracking shows at least $130 billion in US projects blocked or delayed in the first quarter of this year alone, with Carbon Direct putting the cumulative figure at $170 billion since 2024. Set against roughly $725 billion in projected 2026 capex across Alphabet, Amazon, Meta, and Microsoft, the question for operators is whether this is a genuine constraint or noise around the edges of an inevitable buildout.
The bigger shift is who carries the risk. With digital infrastructure funds raising $157.6 billion in 2025 according to PitchBook, and private capital taking positions like Blue Owl's 80 percent interest in Meta's $27 billion Hyperion campus, a permitting fight in a single county is now underwriting risk for pension funds and insurers nationwide. Delay no longer just moves a launch date. It extends the payback period and compresses return on invested capital.
In this episode:
The scale of the buildout: 12 gigawatts of national compute capacity today, with the industry targeting 60 gigawatts by the end of the decade

Who is writing the checks: OpenAI's $1.1 trillion in total infrastructure commitments, Anthropic's roughly $350 billion in compute commitments, and the rise of third-party capital as the load-bearing wall

Why projects slip: 75 percent of capacity under construction already pre-leased, 1.6 percent vacancy, five-year grid interconnection backlogs, and 3-5 year transformer lead times

What is driving residents into council meetings: $29.4 billion in added PJM customer costs, utility bills up as much as 267 percent in some markets, Google's water consumption up 34 percent year over year, and a Gallup finding that 71 percent of Americans oppose a data center in their own community, a higher share than opposes living near a nuclear plant

How communities win: 833 active opposition groups across 49 states, more than 300 municipal bans and moratoriums since 2023, and Monterey Park's permanent ban approved with 88.34 percent of the vote

Fifty different rulebooks: New York's statewide permitting pause, Ohio's 85 percent capacity payment requirement, Virginia's energy consumption tax, and West Virginia moving in the opposite direction

The three playbooks that get projects built: Meta's community investment model in Louisiana, the legal route in Michigan, and Microsoft's quieter university and water reuse partnership in Wisconsin

The operator takeaway:
The buildout is getting slower and more negotiated, not stopped. For anyone modeling enterprise AI unit economics, the bottleneck has moved from chips to concrete, copper, and zoning boards. Cost models built on an assumption of falling compute prices need a second look this quarter.
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On this week's AI to ROI podcast, Ray Rike is joined by Stephan Bajaio, co-founder and CEO of VibeLogic and a former co-founder of Conductor, the enterprise SEO platform he helped build over 14 years, culminating in a WeWork acquisition, a management buyback, and a valuation approaching half a billion dollars. With 25 years spanning e-commerce, Yahoo, Time Inc., and a stint as a software CMO, Stephan brings an operator's view of what has actually changed in the buyer journey and what has not.
The conversation centers on a hard number. When Stephan asked the marketing team at a $3 billion company what share of their web traffic was being driven by LLMs, the estimates ranged from 20 percent to 50 percent. The measured answer was 1 percent, with a high conversion rate on that small base. That gap between perceived and measured contribution is the core problem for any executive being asked to fund an AEO, GEO, or AIO program this year.
Stephan introduces Web Presence Intelligence, a supply-and-demand framing designed for executive conversations rather than channel specialists. Demand is what goes into the search bar or the prompt. Supply is everything that comes back, including publishers, Reddit threads, affiliates, partners, and competitors. The strategic question becomes whether you are influencing where your buyer's opinion is formed, before you decide where to place the bets.
Ray pushes back directly, arguing that understanding where models source citations should now outrank owned web properties. Stephan holds his position, and the exchange gets to the heart of the allocation decision facing CMOs and CFOs.
In this episode:
Web Presence Intelligence defined, and why supply and demand is the right executive language for a channel conversation

The four P's of owned content: point, paragraph, page, path, and the audit finding that most sites never actually name the problem they solve in the customer's own words

Why owned assets matter more when the interpretation layer keeps changing, illustrated by a professional who lost a decade of LinkedIn equity overnight

Attribution reframed as a consequence of measurement rather than a measurement itself, and how to work backward from the sale to the second and third best proxies

The baseline requirement, or what Stephan calls the before photo, and why AI deployed against a process you do not already understand produces outcomes you cannot judge

Control groups in practice, including a market share test on terminology that showed one enterprise software company exactly what its brand guidelines were costing it in visibility

Why the gold rush money went to the people selling pans, and where the actual near-term return sits: auditing your own sales calls, renewal conversations, and customer logs

From funnel to hourglass, and why channel ownership models are breaking down as generalists get more capable with AI

Augment, automate, or build net new: three different projects that require three different budgeting methods and three different attribution models

The operator takeaway: control what you can control, establish a baseline before you fund the initiative, and structure AI marketing investments so the return can be defended against assets you own rather than placements you do not own.
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AI spending continues to accelerate, but the ROI story has not kept pace. This week, Ray Rike and Peter Buchanan dig into the proposed fix that has the market talking: turning frontier AI labs into professional services firms through the forward-deployed engineer (FDE) model.
The role is not new. Palantir built the FDE function in 2005 to embed technical teams directly within customer environments, and by 2016, it had more FDEs than software engineers. What is new is the capital. Five ventures from the major AI labs and top three hyperscalers have committed more than $10 billion, betting that the bottleneck to enterprise AI value is not the model; it is getting that model wired into a customer's data, processes, workflows, and compliance requirements.
Ray and Peter connect this moment back to the ERP era, when SAP and Oracle needed four to five dollars of services for every dollar of software, and explain why the same people, process, and services reality is playing out again with agentic AI.
What the episode covers:
Why OpenAI's $4 billion DeployCo, with a guaranteed 17.5% investor return, is the most aggressive and most financially puzzling bet of the five
How Anthropic (Ode), AWS, Microsoft, and Google each took a different structural path, from joint ventures to capital-light partner ecosystem plays
Why the MIT 95% pilot failure stat and McKinsey's finding that two-thirds of organizations have not started scaling AI make this a real problem, not a fringe one
How incumbent consulting firms are playing every side at once to protect their AI practices
Alex Karp's argument that the AI industry broke its own business model, and the irony of him making it

What CFOs and GTM leaders should take away:
Ask any FDE partner for two or three production use cases with measurable outcomes before expanding scope
Start narrow with one win, not four or five simultaneous projects
Price for outcomes up front, before mid-project renegotiation
Model the ongoing maintenance cost, since 20 to 40% of the initial investment often goes to keeping it running, and an outside FDE team at $300 to $800 an hour is an expensive long-term maintenance line item
Weigh the neutrality trade-off honestly, since lab-backed ventures get you the deepest model roadmap access but also the deepest lock-in
Lawyer up on data privacy and IP, because these engagements can feed your proprietary workflows back into the next model release

Ray's read: independent consulting firms are the likely long-term winners, with hyperscalers close behind. The AI labs are, in Peter's words, still leaving the Shire without their full posse ready to go.
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Twelve months ago, US frontier models controlled roughly 70 percent of AI traffic. Today, Chinese open-weight providers, led by DeepSeek, Z.ai, Moonshot, and Minimax, account for 45 to 61 percent of top-tier model traffic on OpenRouter, with DeepSeek alone processing more tokens than Google, Anthropic, or OpenAI individually.
In this Big Story edition, Ray Rike and Peter Buchanan unpack how this shift happened, how US labs and regulators are responding, and three scenarios for how the closed versus open weight competition plays out for enterprise AI buyers.
Key topics discussed:
The pricing collapse driving enterprise migration. DeepSeek made a 75 percent price cut permanent in May, bringing its V4 Pro model to a fraction of a cent per million tokens versus $2.50 per million for GPT 5. Minimax delivers GPT 5.5 class coding performance at 5 to 10 percent of the cost. This is why Uber, Microsoft, and Walmart are now implementing formal usage governance on frontier models rather than treating cost control as temporary.

The Mythos and Fable shutdown as a trust event. The 18-day suspension of Anthropic's top models over export control concerns spooked global enterprise buyers who realized mission-critical workloads could be cut off without warning. This single event accelerated the adoption of open-weight alternatives and pushed allied governments to invest in sovereign AI capacity.

Distillation attacks and the IP leakage problem. Anthropic accused Alibaba's Qwen lab of running a large-scale adversarial distillation campaign, using tens of thousands of accounts and tens of millions of exchanges to extract agentic reasoning capability from Claude. This reframes the security conversation from model safety to unauthorized technology transfer, which is a distinct and arguably bigger risk for any enterprise relying on proprietary model capability as a moat.

Cybersecurity parity is closing faster than expected. Multiple Asian labs, including Z.ai's GLM 5.2, Beijing based 360 Security, and Japan's Sakana AI, now claim benchmark performance approaching Anthropic's Mythos model on vulnerability detection and both offensive and defensive cyber tasks, often at significantly lower compute cost. This weakens the safety and capability gap argument that has justified restricting access to frontier models.

Real deployments have moved from theory to production. Coinbase cut AI spend in half after migrating to Z.ai and Moonshot's Kimi models, even as token usage grew. Cursor shipped a coding tool built on Kimi, with a Grok-based version reportedly imminent. Andreessen Horowitz estimates 80 percent of its portfolio companies already use open-weight models in production AI products.

Three scenarios for how this settles, and why the decision belongs at the board level. The hosts outline bifurcation (premium closed models for regulated use cases, open weight for commodity workloads), export control entrenchment (Washington treats the Fable ban as a template rather than a one-off), and capability convergence (the rationale for unilateral bans erodes as the performance gap closes). All three are already visible simultaneously, which means enterprise AI architecture decisions, including primary and backup model orchestration, are becoming strategic decisions that belong with the CEO and board, not just the technical team.

Why does this podcast episode matter for enterprise executives selecting models, especially for agentic AI deployments?
The vendor you choose today may not be the vendor you can use tomorrow, for reasons that have nothing to do with model quality. Regulatory risk, geopolitical exposure, and pricing volatility are now first-order variables in model selection, alongside capability and cost. Any agentic AI architecture built on a single model provider carries concentration risk that didn't exist a year ago. Building orchestration flexibility with primary and backup models, and understanding the true economics behind token pricing, is quickly becoming a board-level governance question rather than a procurement detail.
For the metrics and framework to instrument and report ROI on your AI investment, get the Big Book of AI Metrics at benchmarkit.ai under Media.
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On June 12th, the US Department of Commerce ordered Anthropic to suspend global access to Fable 5 and Mythos 5, its most powerful frontier models, with no advance warning and criminal penalties attached.
In this week's Big Story episode, Ray Rike and Peter Buchanan walk through the first use of export controls to take a deployed frontier model offline, why it backfired for security rather than strengthening it, and what a coherent federal AI regulatory framework would actually need to look like.
The shutdown mechanics. Because Anthropic could not verify user nationality in real time, the directive knocked out access for every user globally, including Anthropic's own non US employees and more than 150 companies across 15 countries running critical infrastructure workloads.

Three reaction tracks. Industry, the developer community, and allied governments each responded differently. OpenAI pushed back on talent restrictions while its legal team blocked coordination with Anthropic on antitrust grounds, and a coalition of 150 cybersecurity leaders published an open letter asking not for less regulation but for a transparent, science based process.

The regulatory vacuum, in five forces. Ray and Peter unpack the drivers behind what they call a hair on fire crisis: capability jumps outpacing any statutory framework, a state level policy tsunami of over 1,500 AI bills across 45 states, data center community opposition, the unresolved Anthropic and DOD dispute, and a bipartisan Congressional letter questioning why comparable models were treated differently.

A six part framework. The episode lays out proposed solutions modeled on existing regulatory precedent: global market access agreements similar to military sales processes, mandatory pre release testing run by NIST modeled on FAA certification, clear federal versus state jurisdictional lines modeled on pharmaceutical regulation, expanded child safety authority, FERC fast tracking for data center grid access, and coordinated environmental standards.

What enterprise leaders should do now. Ray closes with practical guidance: review AI vendor contracts for shutdown protection since force majeure clauses were never written with export controls in mind, build fallback infrastructure for critical AI dependent workflows, delay rushing into brand new model releases, and automate tracking of the fast growing state regulatory landscape.

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