Seasoned cybersecurity revenue leader by trade. I'm a typical PNW nature enthusiast who enjoys cooking, paddleboarding with my wife, and chasing my daughter around. 

I'm on a mission to make the digital world a safer place by bringing the cyber ecosystem together. 

Join me?

Taylor Wells

CEO @ Hard Skill Exchange | The Stock Market of Skills

Julia is the CEO and Founder of Hard Skill Exchange, the innovation exchange for AI-native GTM.

A CMO by 30, she advanced through the Salesforce ecosystem and launched a “Product Hunt for GTM methodologies” as an intrapreneur, inspiring the creation of HSE.

Today, she leads B2B's foremost executive network where leading go-to-market CXOs, VCs, and analysts operationalize innovation, experiment at the edge of the unknown, and exchange the frameworks and architectures shaping the next era of the agentic enterprise.

She holds a master’s degree in linguistics.

Julia Nimchinski

Every company is becoming an operating system. Who’s writing yours?

7 existential questions (+jtbd) shaping the recursive enterprise:

1/ What does it take to operationalize the shift to context-graph systems of agents?

Neo4j
Apollo GraphQL
Airbyte 
Boomi 
Explorium

2/ What must be rebuilt in GTM methodologies, indicators, and operating frameworks for the AI era?

Provarity 
GTM Buddy
Pigment 
Nue.io
WRITER

3/ How should context, orchestration, and interface function as coordinated system layers?

Amoeba AI 
Retool 
Tines
Botpress 
Prismatic

4/ What architecture governs multi-agent systems, AI workers, accountability, and control boundaries?

Vivun 
1mind 
Aisera 
Scale AI 
Assembled 

5/ Does infrastructure sovereignty make on-premise the new cloud for AI systems?

CoreWeave 
Together AI 
Runpod 
SambaNova 
Hammerspace 

6/ How must products evolve as contextual systems interacting continuously with customers?

Gainsight 
Vidyard
Bland
EverWorker 
Parloa 

7/ What defensibility exists in the agentic age, and where do new network effects emerge?

ZoomInfo 
Alta | AI Revenue Workforce
Von
RevSure AI 
HG Insights

These questions sit at the core of the Agentic OS Summit (March 24–26), where B2B’s leading VCs, CXOs, analysts, and operators will examine how organizations are redesigning the operating layer of GTM.

Attend free/virtual: https://lnkd.in/dEdeb7Q6 

Hard Skill Exchange

#AgenticOS

Founder & CEO @ HSE | The Innovation Exchange for AI-native GTM

Operating systems are under direct AI disruption.

Rapid integration of AI into OS has fundamentally reshaped its core architecture, giving rise to context-graph–based systems of agents/systems of action. 

The best practices for operating this shift are still being invented in real time and the companies that codify them first will move fastest.

For the last generation of enterprise software, systems of record were the anchor tenant, the database plus workflows, everything orbited. The open question now is where that gravity shifts when agents become the interface and fewer humans log into the system at all.

This 'trillion-dollar opportunity,' introduces a distinct set of structural challenges:

1 / The Transition Challenge
What does it take to operationalize the shift to context graph–based systems of agents?

2 / Methodological Reconstruction
What must be rebuilt in the foundational methodologies and structural indicators that historically underpinned GTM, and what new value systems, operating frameworks, interfaces, and orchestration models must emerge?

3 / Architectural Realignment
What structural rethink is required to treat context, orchestration, and interface as coordinated system layers?

4 / The Human–Agent Equation
What is required to deploy multi-agent systems, super-agents, and AI workers under a formal agent management architecture that governs shared responsibility, control boundaries, performance accountability, and measurement?

5 / Infrastructure Sovereignty
Is on-prem becoming the new cloud?

6 / Product Transformation
What is required to reshape the product itself and reconfigure the bidirectional relationship between customer and product?

7 / Network Effects / Defensibility
What defensibility holds in the agentic age, and will it introduce additional network effects?

We’ll address this rigorously on March 24–26 at the Agentic OS Summit,  defining the structural entry points for AI within operating models to elevate product quality and compound the return on innovation.

Speaker preview: 
James Currier (General Partner, NFX), Meagen Eisenberg (CMO, Samsara), Conor Grennan (Chief AI Architect, NYU Stern, NY Times and #1 Int’l bestselling author, CEO, AI Mindset), parth ?.(Netrunner, Applied AI & Agent Systems, Office of Reid Hoffman, Technical Advisor, Blitzscaling Ventures), Godard Abel (Co-Founder & CEO, G2), Amanda Kahlow (CEO & Founder, 1mind), Daniel Davis (Co-Founder and Evangelist, TrustGraph), Mandy Cole 	(Partner, Stage 2 Capital), Scott Brinker (Martech Analyst & Advisor, ChiefMartech.com), Sangeeta Chakraborty (CRO, Amagi), James Roth (CRO, ZoomInfo), Udi Ledergor (Chief Evangelist, Gong), Mary Shea, PhD (Co-Founder & Chief Growth Officer, Meerkat), Pat Casey (Chief Technology Officer & EVP of DevOps, ServiceNow). 

Gainsight, Vivun, Vidyard, ZoomInfo, BuyerForesight, Alta | AI Revenue Workforce Hard Skill Exchange

Attend free/ virtual: https://lnkd.in/dEdeb7Q6

Steve Jobs used the term skeuomorphic to describe early interfaces that mimicked the physical world – file cabinets, notepads, folders.

Every platform shift starts this way.

Chris Dixon (Andreessen Horowitz) has noted that AI appears to be moving through the same skeuomorphic phase.

The first generation copies the previous system.

The next generation invents the native architecture.

Right now, enterprise AI is still largely applied in skeuomorphic form.

AI is being inserted into systems of record workflows – CRM screens, marketing dashboards, ticket queues.

AI is a system of action.

Always-on agents.
Parallel execution.
Processes firing simultaneously.
Machine-to-machine orchestration through protocols like MCP.

When that architecture becomes native, the implications for go-to-market reach the core of the company.

The operating layer changes.

GTM stops being a sequence of human workflows moving through software.
It becomes a network of agents executing, coordinating, and learning across the system.

The company itself begins to resemble an OS.
At that point, network effects can begin to form – each additional agent, workflow, and data stream increases the value of the system for the rest.

Humans move up the stack: strategy, judgment, system design.
Agents handle orchestration, execution, and iteration.

This transition will not be gradual.

Platform transitions rarely are.

The early internet looked like digital brochures.
Mobile first looked like desktop apps squeezed into smaller screens.
The first cars looked like horse carriages without horses.

We are at the equivalent stage for AI.

The skeuomorphic phase.

The companies that discover the native operating model first will define the next decade of go-to-market.

That is the conversation we are convening at the Agentic OS Summit March 24-26 with leaders like James Currier (NFX) and other shrewd operators designing the architecture of AI-native organizations.

Attend free/virtual: https://lnkd.in/dEdeb7Q6 

Gainsight Vivun Amoeba AI Vidyard ZoomInfo BuyerForesight Amzai Limited Alta | AI Revenue Workforce Von

Manny Medina (Co-Founder/ CEO, Paid): “There’s a direct correlation between the cost of an action and the value of the outcome.

If pricing or value capture isn’t aligned to that cost, margin compresses.

Take an outreach agent. It analyzes the full customer history and sends a re-engagement email for expansion or when someone changes jobs. With five years of tickets, wins, frustrations, and context in the prompt, that single email can cost $2 in input tokens.

If the recipient is now a VP at a target account, those $2 are trivial. One positive reply justifies the spend.

But sending the same depth of context to a low-value contact carries the same cost without the same upside.

Every AI action now has a unit cost. The system needs to evaluate expected value before execution.”

Full recording in comments.

Manny joins us live for a fireside chat at the Agentic OS Summit
March 25 · 9:30am PT
Attend free virtual: https://lnkd.in/dEdeb7Q6 

Hard Skill Exchange

What measurement / KPI / “vanity metric” do we need to stop using or at least stop pretending to believe in 2026? 

Michele Buckley Werner Schmidt Hard Skill Exchange

Von CEO on the biggest misconceptions about “LLM wrappers”:

“Everyone thinks you just take an LLM like Claude or ChatGPT, connect it to Salesforce with MCP, and boom, you’re done. But even for a basic use case, you need a semantic layer. The system has to understand what your business actually means.”

“If I ask: ‘What’s our win rate last quarter?’, it needs to know: is that Stage 2 onward or Stage 1 onward? Is it new business only, or new business plus strategic deals? And what exactly counts as ‘last quarter’?”

“To get to real agent systems, the stack becomes much bigger: an execution environment, pre-processed information about every deal and account, a semantic layer, and a vector database with retrieval. That’s where the agents actually live.”

– Sahil Aggarwal

Source: Hard Skill Exchange | link in comments

Gainsight Hook Von EverWorker Vivun

AI as a connection between architecture and OS.

We are observing accelerated differentiation in operating systems. They are multiplying; the number of variants is increasing, and customization is emerging.

Some operating systems are now being created by AI.

Others remain traditional and ritualized; rigid, powerful systems that introduce AI through integration layers. They embed AI, but the underlying operating logic remains unchanged.

Last week, we hosted AI Practice Sessions, where leading AI-native startups and operators presented concrete methods, workflows, and toolsets for embedding AI into their OS.  

Architecture was examined through three layers:

1/ Context
2/ Orchestration
3/ Interface

We presented multiple modalities of agent deployment:

1/ Multi-agents / “agent swarms” – distributed agents covering distinct functional domains.
2/ Super-agents – a single agent spanning multiple functions within a domain.
3/ AI employees / workers – agents positioned as structured functional replacements or assistance.

+ Practical applications of tools such as Claude, Clay, and Dust AI in GTM.

(more in the article)

Hard Skill Exchange

We are observing accelerated differentiation in operating systems. They are multiplying; the number of variants is increasing, and customization is em

Open article: AI as a connection between architecture and OS by Julia Nimchinski • 3 min read

AI as a connection between architecture and OS

Who built Reid AI? Yes, the digital twin of Reid Hoffman. 
parth ?. 

We'll find out what's in his Agentic OS on March 25 @ 11:30am PT

Attend free/ virtual: https://lnkd.in/dEdeb7Q6

“Claude Code is the only tool today that truly captures your imagination. 
Instead of starting with filters and working down to personas and messages, it lets you think backwards: what is the very best message you could send, and which companies are living in the very worst version of the problem you can serve?” Jordan Crawford

Full recording: https://lnkd.in/dBRK26aS 

Gainsight Hook Von EverWorker Vivun Hard Skill Exchange

“we need AI”
“we need to operationalize it”
“we’re not sure what to do with it yet, but we know we need it”

That cascade is the power of a story moving through the market.

Very few technologies have spread this way, where the narrative propagates faster than the implementation. The closest modern parallel might be Apple, when the idea of the personal computer or the smartphone spread before people fully understood how they would use it.

Enterprise deals move the same way: through stories that circulate internally long after the seller leaves the room. 

Doug Landis (Co-Founder/ CEO, StoryPath.ai) helps sellers build the story that survives after the meeting ends.

Full recording: https://lnkd.in/dGMsDQ7m 

Hard Skill Exchange

When you operationalize your own playbook: nine months in, blog views up ~50% every 30 days, AI referrals +230%, organic continuing its MoM ascent.

Ameya Deshmukh Gainsight Hook Von EverWorker Vivun Swan AI Hard Skill Exchange

Timothy Young (CEO, Jasper): “Most organizations today are simply applying models with basic prompts and hoping that alone will generate organizational value.

What actually matters is applying domain memory to the problem.

Project specifics, user preferences, learned information, feature lists, requirements.

When you pair that context with the model, you get reliable, repeatable outcomes.

But that only works if the system has guardrails and scaffolding around the model. Without that structure, it won’t operate consistently.”

Full recording: https://lnkd.in/dGMsDQ7m

"This is why rule-based automation breaks at scale.

You can easily have 50,000 potential signals across a customer base. No RevOps team is building/maintaining 50,000 static playbooks.

So we use plaibooks to orchestrate the actioning of those signals.

A plaibook scans full business context and determines the best next step based on what we know about the customer and what we know about your company.

E.g., a complex risk signal. There are four coordinated steps. AI generates them with full account context. It’s built specifically for the customer and the signal, far more precise than predefined rules.

When we need to introduce a new stakeholder, the system generates the email with full awareness of the account’s activity and recommended next steps.

For an upsell signal, it behaves more like an AI SDR, reaching out about an expansion opportunity or booking a discovery call.

Now we’re not just surfacing insight.

We’re converting signal into contextual action (emails, meeting invites, Slack messages, in-app prompts) tailored to the exact account state.

At scale, that’s the difference between automation and orchestration."

Natasha Evans – VP, Customer Growth, Hook
Sam Champion – Founding GTM, Hook

Full recording: https://lnkd.in/dBRK26aS

Gainsight Hook Von EverWorker Vivun Swan AI Hard Skill Exchange

"The edge is not real-time but relevant time." – Mark Stouse (Chairman, Proof Causal.ai)

Full report: https://lnkd.in/dhTM3f3d

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