By: Yan-David “Yanda” Erlich, General Partner, B Capital and Raj Ganguly, Co-Founder and Co-CEO, B Capital

 

Last weekend, the Citrini Research “2028 Global Intelligence Crisis” memo went viral, racking up roughly 16 million views after Michael Burry amplified it. IBM dropped 13%, and we saw broad weakness across software, payments, and delivery stocks.¹ The market panicked.

The memo paints a vivid picture: AI replaces white-collar labor faster than the economy can absorb, consumer demand collapses, unemployment spikes past 10%, and the S&P draws down nearly 40%.² They call it “Ghost GDP,” where output shows up in profits but doesn’t circulate because displaced workers have lost their income.

It’s a clean left-tail story, and it’s wrong as a base case, but it’s directionally correct on the structural shift underneath, which is exactly where the investment opportunity sits.

 

What the Doomers Get Right

Strip away the compressed timeline and the stacked worst-case assumptions, and several of Citrini’s structural observations hold up.

White-collar work is the near-term fault line. A large share of knowledge work is read, write, decide, and coordinate. That’s exactly where current models are strongest. McKinsey estimates 60-70% of employee time is automatable by AI.³ The pressure will show up first in back office, operations, finance, support, sales enablement, and parts of legal and compliance. These aren’t theoretical targets; these are the functions where we see enterprise buyers already pulling budget.

AI is moving from tool to coworker. The real shift isn’t chatbots getting smarter. It’s AI gaining persistent memory, learning on the job, and planning autonomously. We went from “help me write this” (early ChatGPT, basic copilots) to “do this for me” (Codex, Claude Code, support bots) and are now entering “own this with me.” That last phase changes org design, not just task execution.

The distributional tension is real. If gains accrue primarily to capital while labor lags, demand weakens and politics get volatile. Even without collapse, wage, tax, and benefit debates will intensify. That affects regulation and procurement behavior. Investors who ignore this are underpricing political risk.

Take rates will face pressure. Agents routing around software is overstated near term, but the direction is correct. As discovery, evaluation, and execution become automated, friction-based pricing power erodes. The winners will be workflow-embedded products and infrastructure providers.

 

Where It Falls Apart

The Citrini scenario only works if every aggressive assumption resolves in the same direction simultaneously within 24 months. That’s not analysis, that’s a horror story presented as a base case.

Enterprise adoption doesn’t move at speed. Capability may be exponential, but deployment is not. Data restructuring, compliance, procurement cycles, and retraining are multi-year arcs. Only 5% of AI pilots currently achieve measurable P&L impact, per MIT research.⁴ 32% stall after pilot.⁵ METR’s own randomized controlled trial found experienced developers were 19% slower with AI tools, even as benchmarks showed superhuman coding performance, a stark reminder that lab capability and production value are different things.⁶ The bottleneck is not demand: 92% of Fortune 500 already use ChatGPT, 82% of executives plan AI agent integration within three years, and $252 billion went into corporate AI investment in 2024 alone.^{7, 8, 9} The bottleneck is the infrastructure to deploy and scale AI coworkers in production.

“Ghost GDP” confuses distribution with destruction. Labor savings don’t vanish; they reallocate through lower prices, capex, profits, dividends, and tax revenue. The issue is distribution and timing, not whether gains circulate. This is an important distinction for investors: the value gets created; the question is where it accrues.

Policy is treated as inert. Automatic stabilizers, monetary easing, fiscal transfers, mortgage forbearance, and credit restructurings historically interrupt demand spirals. The memo assumes none of these mechanisms activate. That’s not how economies function under stress.

Business execution is understated. Code is cheap, but trust, compliance, distribution, and operational execution are not. Agents may pressure take rates, but they don’t eliminate institutional infrastructure in two years.

 

The Narrative Shock Creates Real Opportunity

The market is conflating a long-term structural shift with a 24-month crisis scenario. That dislocation creates two types of opportunity: mispriced growth exposures in public markets and private-market picks-and-shovels that win regardless of macro path. The latter is where we’re focused.

 

1. AI Co-Worker Applications: Replacing Hours, Not Just Tasks

The highest-conviction opportunity is AI coworkers that land in the enterprise with measurable ROI inside 90 days. The investment filter is simple: can it replace measurable hours with compliance, auditability, and a clear feedback loop?

Software Engineering (~$370B addressable). Claude Code and Codex have commoditized code generation. The frontier has moved to enterprise context and verifiable domains where the AI can prove its work is correct: formal proofs, math/physics, security analysis, AI research itself. The moat here is organizational context, not raw coding ability.

Sales & GTM (~$245B addressable). The market is crowded with AI SDRs, and most of them are dead on arrival. The winners own the system of record, learn from outcomes, and close the loop on what converts. Data rights are the moat; if you can’t observe what works and improve autonomously, you’re a feature, not a company.

Finance & CFO Office (~$215B addressable). High-volume operational workflows with clear accountability: AR/AP, collections, procurement ops, FP&A, compliance reporting. These processes are rules-based but manual-intensive, making them ideal for AI coworkers. The companies we’re most excited about are the ones replacing FP&A analysts, not just augmenting them, where “better” is quantifiable and feedback is continuous.

When AI works in the enterprise, the returns are significant: $3.70 ROI per dollar on average, with top performers hitting $10.30.¹⁰ Gartner projects 33% of enterprise software will include agentic AI by 2028, with 15% of daily decisions made by agents.¹¹

 

2. The Infrastructure Layer: Where Durable Value Accrues

The application layer gets the headlines. The infrastructure layer gets the margins. This is where we believe the market is most under-invested.

Agentic Memory & Context. Models have memory, they don’t have your memory. The gap is organizational context: docs, code, tickets, CRM data, team terminology, approval workflows. This is what separates a stateless chatbot from a colleague. The defensibility compounds because memory improves as context accumulates, and switching costs grow over time as the AI learns your organization. Think of it as the unlock from agent to colleague.

Orchestration & Multi-Agent Coordination. Agents don’t yet collaborate well, with each other or with humans. The missing layer includes coordination protocols, escalation paths, and seamless handoffs between AI and human coworkers. The network effects here are powerful: value increases as more agents and humans use the same coordination layer. This is Slack for the human-AI workforce.

Production Observability. When agents run 24/7, ops teams need real-time visibility into what’s working and what’s not. Most existing tools focus on dev and debug, but the real pain is keeping agents reliable at scale. The first company to nail production-first observability for agentic systems owns the category. This is the Datadog opportunity for the agentic era.

Agentic Security. OpenClaw made this viscerally obvious (more on that below), but the principle applies broadly: agents with persistent access to enterprise systems represent a fundamentally new attack surface. Least-privilege access, skill sandboxing, action-level anomaly detection, and identity management for non-human actors. This category barely existed a year ago, and it will be table stakes for enterprise deployment within two years.

 

3. Why Now: Four Curves Crossed Simultaneously

This thesis isn’t speculative. It’s grounded in four technical inflection points that converged in 2024-25, and the data keeps accelerating.

Reasoning quality is on an exponential curve, and it’s steepening. METR’s time horizon research, which measures the length of tasks AI agents can reliably complete autonomously, shows capability doubling every ~7 months over six years.¹² Their updated TH1.1 methodology (January 2026) suggests recent progress is actually 20% faster than the historical trend, with post-2023 doubling at 131 days.¹³ Claude Opus 4.6 now clocks a 50%-time-horizon of roughly 14.5 hours, meaning it can autonomously complete tasks that would take a skilled human half a working day.¹⁴ If this trend continues for 2-4 more years, we’re looking at agents that can reliably execute week-long projects. MIT Technology Review called METR’s time horizon plot “the most misunderstood graph in AI.” The misunderstanding cuts both ways: doomers extrapolate it to imminent catastrophe, skeptics dismiss it as benchmark gaming. The investment-relevant reading is that autonomous task capability is compounding on a steep, consistent curve, and the gap between what agents can do on benchmarks and what they actually do in production is precisely the market we’re investing into.

That gap is real, by the way: METR’s own developer productivity RCT (July 2025) found that experienced open-source developers were 19% slower when using AI coding tools, despite believing they were 20% faster.¹⁵ Algorithmic benchmarks overstate real-world performance because they can’t capture code quality, context understanding, and integration complexity. This is the deployment gap, and this is the opportunity.

Tool use is standardizing, but not the way anyone expected. MCP (model context protocol) was supposed to be the universal connector between AI agents and external services, and it has real traction: 17,000+ servers on MCP.so, OAuth-native authentication, and adoption by OpenAI, Google, and Microsoft.¹⁶ But MCP isn’t the whole story anymore. Skills (reusable prompt-and-script bundles that encode domain knowledge into agent behavior) have exploded: 96,000+ on SkillsMP and 5,700+ on ClawHub – all built on the SKILL.md standard that emerged from coding agents like Claude Code and Codex CLI.¹⁷ Meanwhile, CLIs (command-line interfaces) are emerging as a surprisingly effective third pattern. Agents have been trained to be exceptionally good at using command-line tools, and CLIs handle authentication, structured output, and composability through patterns that have been battle-tested for decades. Karpathy called it publicly: build for agents by exposing functionality via CLI, publishing task-specific skills, and shipping MCP servers. The investment implication is that the “tool use” layer is not a single protocol but an ecosystem of complementary patterns. Skills encode knowledge, MCP provides authenticated access, and CLIs offer execution efficiency. The companies building the orchestration, discovery, and security layers across all three will own the integration tier of the agentic stack.

Context windows expanded to 2M+ tokens. Persistent memory across sessions is now possible. AI can remember what it learned yesterday.

Inference costs collapsed 200x. GPT-4 equivalent capability now costs $0.40 per million tokens versus $20 in 2022.¹⁸ DeepSeek pushed this even further, running 90% cheaper than Western providers.

These aren’t independent trends. They’re compounding. AI coworkers are now technically feasible, economically viable, and enterprise-ready. The question is no longer if but how fast enterprises can deploy them.

 

4. The OpenClaw Moment: What 157K Stars in 60 Days Tells Us About Where AI Is Headed

If you want a single case study that encapsulates the entire AI coworker opportunity and its risks, look at OpenClaw.

OpenClaw (formerly Moltbot, formerly Clawdbot) started as a weekend side project by developer Peter Steinberger: a personal AI assistant that runs locally on your machine and connects to your messaging apps, email, calendar, and file systems to act autonomously on your behalf. Not a chatbot, not a copilot, but an agent that does things for you across the tools you already use.

The reception was extraordinary. OpenClaw hit 100,000 GitHub stars faster than Linux, Kubernetes, or any project in GitHub history. It crossed 157,000 stars within 60 days.¹⁹ On January 30, 2026, alone, it gained 34,168 stars in 48 hours.²⁰ The project spawned Moltbook, an AI-only social network where only agents could post, which hit 1.5 million registered agents in five days and drew coverage from Fortune, CNBC, and TechCrunch. Y Combinator’s podcast team showed up in lobster costumes.²¹ “Claw” became Silicon Valley slang for locally-hosted AI agents.

This is demand signal, not hype signal. People don’t want another chatbot; they want AI that manages their inbox, controls their schedule, organizes their files, and executes multi-step workflows while they do something else. OpenClaw’s value proposition was blunt: “AI that actually does things, not just talks.” That resonated so strongly that OpenAI took notice. On February 14, Steinberger announced he was joining OpenAI, a move widely interpreted as OpenAI’s play to acquire agentic AI talent after their $3B bid for Windsurf (Codeium) fell through.²² The project transitioned to an independent open-source foundation under MIT license, mirroring the governance model of Linux and Kubernetes.

The enthusiasm is the bullish signal. Now here’s the cautionary one.

Within weeks of going viral, SecurityScorecard found over 40,000 exposed OpenClaw instances on the public internet, 63% of them vulnerable to remote code execution.²³ Researchers discovered 400+ malicious “skills” on ClawHub (OpenClaw’s marketplace) distributing infostealers, remote access trojans, and backdoors disguised as legitimate automation tools.²⁴ A critical one-click RCE vulnerability (CVE-2026-25253) meant attackers could compromise systems through a single link without the user installing anything. Skills execute with full agent and system permissions, with no sandboxing and no least-privilege access. Users were following YouTube tutorials that never mentioned security, deploying agents on cloud servers with authentication set to “none.”

The most alarming development: Hudson Rock documented the first observed case of an infostealer harvesting an entire AI agent configuration, not just browser passwords but the complete identity, permissions, and API keys of a personal AI agent. That’s a new attack surface that didn’t exist twelve months ago, and infostealers are now targeting AI personas as high-value assets.

This matters for our thesis on three levels.

First, the demand is real and it’s massive. 157K stars in 60 days, OpenAI acquiring the creator, and “claw” entering the tech lexicon as a verb are not indicators of a fad. Consumers and developers are telling us, loudly, that they want persistent AI agents with real autonomy over their digital lives. The enterprise version of this same demand is the AI coworker.

Second, agentic security is not a feature request, it’s a category. When agents have persistent access to email, calendars, financial accounts, and code repositories, the blast radius of a single compromise is an order of magnitude larger than a stolen password. Enterprise buyers will not deploy AI coworkers at scale without permissions frameworks, action-level anomaly detection, skill sandboxing, and audit trails. Every CISO who reads the OpenClaw postmortems becomes a buyer for agentic security tooling.

Third, OpenClaw draws a bright line between consumer-grade agent experiments and enterprise-grade AI coworkers. The difference is infrastructure: identity, access control, policy enforcement, observability, and governance. OpenClaw shipped the agent but not the infrastructure underneath it. That infrastructure layer is exactly where we’re investing.

 

5. The PE and Credit Warning

The Citrini memo should be a warning sign for PE-backed SaaS with weak differentiation and friction-based economics. If your portfolio company’s pricing power depends on being embedded in a workflow that an AI agent can route around, your margins are on borrowed time.

The selective opportunity in PE and credit: buy-and-build modernization plays where AI compresses COGS and SG&A, and distressed recurring revenue assets deeply embedded in workflows that agents will need to run through, not around. Avoid aspirational ARR quality and unsecured exposure if growth stalls.

 

6. Services as a Wedge

Implementation friction is real, which makes transformation services investable: mapping processes, instrumenting data, integrating systems, training organizations, and building governance layers. These capabilities scale if paired with product and repeatable playbooks. Enterprises want outcomes, not tools, and the companies that combine software and delivery to implement coworkers and redesign processes will compound.

 

What We’re Looking For

Our current investment filtering criteria across this thesis:

Team. AI technical depth plus domain expertise. Exceptional founders with strong founder-market fit over traction alone. In a market moving this fast, the team’s ability to navigate rapid shifts matters more than any current metric.

Data Moat Quality. As AI models commoditize, unique high-quality data with continuous feedback loops becomes the primary differentiator. If your data advantage can be replicated by a competitor with a bigger API budget, it’s not a moat.

Workflow Embedding Depth. Deep integration into daily workflows creates switching costs. Products that are indispensable to users’ daily work are defensible; products that sit on top of workflows are features waiting to be absorbed.

Progressive Defensibility. Technical moats alone don’t last in AI, and they may not even exist anymore. We’re looking for clear plans to layer in defenses over time: data accumulation, network effects, regulatory positioning, distribution lock-in.

Economic Value & Business Model Innovation. GTM and pricing that support AI economics for both the company and its customers. Sustainable unit economics are not optional.

We’re actively seeking Seed to Series C investments in AI coworker applications across engineering, sales, finance, and support, as well as enabling infrastructure in memory, orchestration, observability, and agentic security. Typical check sizes range from $5M to $50M.

 

Bottom Line

The Citrini piece is a useful stress test, not a prediction. The market appears to be treating a long-term structural shift as an imminent crisis, and that creates dislocation for investors willing to look past the noise.

The structural shift is real. METR’s data shows capability doubling every 4-7 months with no sign of deceleration. AI coworkers will change how enterprises operate, how work gets organized, and where value accrues. But it will follow enterprise timelines, not Twitter timelines.

If anything, the panic reinforces the thesis: AI coworkers and the enabling infrastructure are where durable value gets built. The 95% pilot failure rate isn’t evidence that AI doesn’t work — it’s the problem we’re investing to solve. OpenClaw showed us what happens when powerful agents ship without enterprise-grade infrastructure underneath them. The companies that bridge the gap from pilot to production, that give enterprises memory, orchestration, observability, security, and compliance, will capture outsized value in the decade ahead.

That’s where we’re putting capital, and that’s where we think you should be looking too.

—

Yan-David “Yanda” Erlich is a General Partner at B Capital, where he leads the firm’s AI co-worker and infrastructure investment thesis through Growth Fund IV and Ascent Fund III. Previously COO & CRO at Weights & Biases, GP at Coatue Ventures, and a 4x venture-backed founder. Reach him at yanda@b.capital.

Raj Ganguly is a Co-Founder and Co-CEO of B Capital. He is focused on connecting extraordinary entrepreneurs with the people, capital and support needed to drive exponential growth. In less than a decade and under Raj’s leadership, B Capital has grown into a global firm with 9 locations, 100+ employees and over $9B+ in assets under management.

 

 

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LEGAL DISCLAIMER
All information is as of 2.25.2026 and subject to change. This content is a high-level overview and for informational purposes only. Certain statements reflected herein reflect the subjective opinions and views of B Capital personnel. Such statements cannot be independently verified and are subject to change. The companies discussed herein are not portfolio companies of B Capital. It should not be assumed that any companies identified and discussed herein were or will be profitable. Past performance is not indicative of future results. The information herein does not constitute or form part of an offer to issue or sell, or a solicitation of an offer to subscribe or buy, any securities or other financial instruments, nor does it constitute a financial promotion, investment advice or an inducement or incitement to participate in any product, offering or investment. Much of the relevant information is derived directly from various sources which B Capital believes to be reliable, but without independent verification. This information is provided for reference only and the companies described herein may not be representative of all relevant companies or B Capital investments. You should not rely upon this information to form the definitive basis for any decision, contract, commitment or action.

 

SOURCE

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