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.

Each innovation wave redraws the architecture beneath it.
AI is doing so at unusual speed.

Whether we like it or not, AI has introduced a new set of structural challenges.
Over the past year, many of them became unavoidable.

In The Innovator’s Dilemma, Clay Christensen outlined three paths: innovation, market-creating innovation, and innovation that stalls and gets handed to marketing.
That was an early prototype of architecture.
Today, it’s clearly visible.

AI is entering how companies are managed and operated. It is taking on real functions: supporting teams, influencing execution, coordinating decisions, distributing models and responsibilities, and enabling personalization at scale. In parallel, organizations are experimenting with agent swarms, agentic distribution, agentic scaling, and AI-native GTM workflows.

Forward-looking projections remain mixed.

Mark Roberge outlines four phases of AI in GTM, placing most organizations at the first stage or just beginning to enter it. At the same time, some companies have already raised capital around agent-swarm architectures.

The outlook is contradictory. One camp expects a smooth transition. Another anticipates disruption.

Some companies will technically integrate AI into traditional systems, neutralizing any AI-native architecture in the process. Others, like Elon Musk, are breaking the system entirely. What’s emerging with X is an AI-native architecture that restructures relationships through transparency, reduced ambiguity, and explicit linkage between speech, identity, and context.

None of this is inherently negative. The open question is architectural: how these changes are designed and implemented across real tools, systems, and use cases.

That is the focus of our AI Practice Sessions on January 28.

We will examine architectures where AI initiatives fail, where they succeed, and why, giving leaders a clear basis to compare approaches and understand the risks they are choosing to take. Ten sessions, led by industry's leading operators and CXOs.

Preview:
• Architectural decisions that determine whether autonomous GTM creates or destroys value
• Moving agentic AI from pilots into production with governed, modular architectures
• AI-native retention architectures across customer success
• A practical framework connecting context, signals, reasoning, orchestration, and org reality
• Structurally scalable AI-native GTM campaigns
• Scaling agents across thousands of integrations without losing control
• Using graph-based RAG for reliable GTM execution beyond prompts
• Ambient AI and continuous, context-native execution
• Why full-funnel AI now requires centralized control and accountability
• Why a few deeply integrated “hero agents” outperform loose swarms

How are you designing AI-native architecture inside your company?
How are you deploying AI across GTM?

Attend free/ virtual: https://lnkd.in/dHEHQ3PG

Gainsight Hook Momentum.io

Julia Nimchinski

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

'A lot of us would be excited to see dashboards die. 
How do we actually make it happen?'
Michele Buckley (VP, AI GTM, Gartner)

Tooba Durraze, Ph.D. (Founder & CEO, Amoeba AI):
"It starts when we stop assigning human labels to AI, calling it a teammate or an analyst, and instead focus on its capabilities. If you think of AI as a living system, its job is simple: tell you what matters, when it matters, and what to do about it.

For anyone running a business, there is really one question: what drives growth, and what puts growth at risk?

Those answers cannot live in a static dashboard. Dashboards introduce bias. You are predefining what data matters and governing the business around that view, which leaves a lot on the table.

What leaders begin to realize is that there are important signals outside those predefined views, sometimes millions of dollars’ worth of anomalies, that you would never see by looking only at a fixed dashboard.

That leads to a different model: AI as the brain of the business. And if it is the brain, its job is to reduce cognitive load. Instead of you searching for answers, it pushes insights to you and says, “Pay attention to this. This is happening. This could put the business at risk.”

We are not fully there psychologically yet. People are still attached to dashboards and visuals. But as leaders see how much opportunity is hidden in data they never queried, or never thought to query, the shift starts to happen. That is when they realize how much more effectively they could be running the business."

Link to the full recording in the comments.

Hard Skill Exchange

"There’s a new kind of moat emerging.

We’ve made around 40 AI application-layer investments since 2022, and the ones that endure tend to answer the same question.

Can you become a business unit within the customer within a 3- to 5-year period? A high-performing business unit inside an enterprise is not something that gets replaced lightly.

The starting point is simpler. Can you become a high-performing employee? And then grow from there.

This is still early. The last investment we made involved a founder who left Microsoft only days ago, after we committed a month earlier. The pattern is forming in real time.

What we’re beginning to see is consistent. Teams that embed into workflows from day one, with a human in the loop and in non-critical processes, start to build trust. In year two, they generate data that improves those workflows. Over time, they earn more responsibility. By year three, they may begin providing decisioning.

That progression is what makes the business-unit moat hard to unwind. We’re still watching it play out, but the mechanism is becoming clearer."

– Kirby Winfield, Founding General Partner, Ascend

Link to the full recording in comments.

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Hard Skill Exchange #agenticsingularity

"2026 is the year we start organizing the Big Bang. 

Last year was largely about scrambling, throwing things at the wall, and seeing what sticks or delivers value. This is the year we organize.

Through that organization, you’ll start to see a real chasm emerge between the winners and the losers of this disruption. And I think mindset is what separates the two.

The winners will be those who see this transition as leveraging AI through a foundational rearchitecture of their business, teams, and tools. 

The losers are the ones who will keep buying tools and agents without a proper cultural change across the organization.

That’s the core mindset shift.

You have to stop thinking, “There’s cool tech, I’ll buy some of it, drop it into my business, and it will deliver a real delta.” You have to start realizing that this is genuinely disruptive. The depth of impact and disruption it can deliver is significant enough to justify rethinking many of the building blocks of your organization, your business, and your GTM."

– Santiago Suarez Ordoñez, Co-Founder, CEO, Momentum.io

Link to the full recording in comments.

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Hard Skill Exchange #agenticsingularity

Manny Medina: "I don’t even think you have to worry about margin once you identify where the value actually sits.

About a year ago, I pitched a customer who told me this would never work. Then last September, he called me and said, “Manny, I just added a zero to my ACV.” I asked him to explain.

He had sold his agentic solution to a company that decided to reduce its BPO spend by $2 million and reallocate that budget to his software. Until that moment, no one had told him there was a BPO budget. No one had told him it was $5 million. And no one had suggested he could compete for that pool of spend.

The shift happens when reps ask a different question: how is this problem handled today? If the problem matters, it is already being solved, and in most cases it is being solved with people.

When agents are positioned against labor budgets, the value ceiling changes materially. Conversations move from thousands to millions.

There is no playbook for this yet. The technology is evolving too quickly. That requires trust in the field. The most effective approach is to give capable reps minimal constraints and let them discover value where it already exists."

Link to the full recording in comments.

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Hard Skill Exchange #agenticsingularity

It used to be ‘+ AI.’ 
Now it’s ‘AI +.’

Raj Datta (Global GM & VP, ISV & AI Partnerships, IBM):

"We’re seeing a major transition from when AI was an afterthought. With much of the investment happening, AI used to be something you layered on top of an application. You’d build the application first, then say, “Okay, now we’ll put AI on top of it.”

What’s changing is that AI is becoming the foundation. You start with AI, and then the application and additional assets are built on top of that.

We saw immense scale this year. Everyone was talking about wanting to do AI, and the buzz was there. Agentic became the real buzzword this year. Next year, I think we’ll see that grow more than 10x, simply because people are now getting the capabilities to do it.

There are companies supporting this shift and enabling scale. That’s what we’ve been doing at IBM: providing a foundation that others can build on, and keeping it very open. We saw a lot of closed models slow companies down and get them into trouble.

Along with AI, I think we’ll also see a significant open-source movement emerge."

Link to the full recording in comments.

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Hard Skill Exchange #agenticsingularity

Sydney Sloan (CMO, G2) demystifies AEO:

“From April to August, there were seven vendors in the AEO category. By October, there were more than 117.

All trying to solve the same core problem: how do I show up?

The first step is understanding the questions being asked.

This is fundamentally different from SEO. With SEO, you could run a query and know whether you were at the top of the page, the middle, or page three. That model doesn’t apply to LLMs.

Put ten people in a room, ask different LLMs the same question, and you’ll get ten different answers.

That’s what AEO tools do. They aggregate hundreds of thousands of questions and surface the citation sources powering those answers. You can see whether your content is cited, which sources are referenced, and where you need to appear.

This is why G2 has a meaningful opportunity. LLMs cite G2 because the content is trusted. It’s verified, validated, written in human language, and structured around comparisons and pricing, exactly what these questions require.

The starting point is clear: understand the questions, identify the citation sources behind the answers, and ensure your responses are present there.”

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

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Hard Skill Exchange #agenticsingularity

“A lot of ROI calculations aren’t really business ROI.

It’s more like: we gave a developer a $30 license for Cursor, they became 30% more efficient, and we call that ROI. That’s a bit of a red herring.”

– Jason Napieralski (AI Business Transformation Strategist, Ex- AWS, Google, Oracle Executive) 

Timothy Young (CEO, Jasper): 
“What more progressive organizations are realizing is that it’s a business vs an employee transformation. 

To get there, they have to isolate real business processes and workflows, including ones that were never formalized, and apply AI there. That means treating AI as foundational infrastructure, not just a productivity tool.

What this exposes is a lack of process clarity and a weak understanding of what actually drives KPIs. Organizations are now being forced to define those processes explicitly so AI can generate ROI that shows up in the P&L.

That’s a journey most organizations are only just beginning.”

Link to the full recording in comments.

Hard Skill Exchange #AInativeGTM

Timothy Young (CEO, Jasper): "We sit down with organizations and ask: what has been impossible until now?

Imagine that you now have AI. You have something close to infinite junior labor, infinite junior intelligence, that is constantly getting smarter at a foundational level. Given that, what are the things you’ve always wanted to do in your business but couldn’t because of resource constraints, timing, or past leadership decisions?

We start there.

From that point, you co-create with teams. You design entirely new processes coming out of that work, rather than retrofitting old ones. You look at the problem from that angle first.

Then you take that group and you ring-fence it. You treat them as internal challengers to old models and older ways of working.

What we’ve seen is that this creates a real groundswell of excitement inside the company. It’s no longer about replacement or incremental improvement. It becomes about genuinely net-new innovation.

And where companies have pushed in that direction, they’re starting to see much stronger results, along with broader adoption and employee buy-in."

Link to the full recording in comments.

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Hard Skill Exchange #agenticsingularity

Amit Bendov (CEO and Co-Founder, Gong): "What’s better – predicting the future, or changing the future? 

Prediction is essential to autonomy. 
If you think about a self-driving car, it needs to know, “If I go this way, my ETA is 11:35, but I need to be there by 11:20.” 

Traditional systems are workflow systems. Everyone calls in their numbers on Friday, they get rolled up, and by Monday morning there’s a call. That still matters. Gong supports that, because programmatically you still need it.

But the more valuable piece for us was taking data from conversations and using AI to generate predictions. For example, it can tell you: your goal is $180 million, but based on what we’re seeing from customer data (not rep sentiment) your current trajectory looks closer to $166 million.

And then it can say: here are five things you should do right now to get back on track, or even exceed the goal.

We’re more interested in changing and creating the future. But prediction is key. Knowing early whether you’re on track or off track is incredibly valuable, and it’s what guides the AI."

We’ll go deeper into this in our AI Practice Sessions on Feb 12, focused on AI-native GTM architecture. 

Link to the full recording and event in the comments.

Hard Skill Exchange

Eli Schwartz: "SEO has to be part of the product. It has to reflect the product. It can’t exist as its own objective." 

Eli reframes SEO as answer-led, product-led discovery. 

“SEO” as manipulation of search engines is fading. Users now ask longer, more specific questions and expect coherent answers. The shift from ~4-word Google queries to ~23-word AI prompts changes discovery mechanics, but not user intent.

What has changed is the interface.

The unit of competition is whether your product actually solves the problem the user is trying to resolve, especially in the mid-funnel, where decisions are formed.

If you can’t articulate why someone should buy from you, that’s a business problem.

Mark Organ: "Distribution only works when it mirrors how buyers actually buy.

I hear about Marc Benioff, even given the size of Salesforce today, and how he still spends an enormous amount of time with customers. He has a very intentional way of choosing which customers to talk to. I think that’s a big reason Salesforce continues to stay on top and stay young, even as a large company."

Link to the full recording in comments.

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Hard Skill Exchange #agenticsingularity

Mark Roberge on the AI hype cycle and what 2026 looks like:

"I think we’re going to see a bust; the bubble will burst.

At the same time, the truly AI-native companies that end up taking over the tech sector will already be running. Many of them may still be seed or Series A companies.

The companies that raised over $100 million before achieving PMF will likely go the way of Groupon or WeWork. The companies that haven’t raised at that level yet, and won’t be able to once the bubble bursts, are the ones that will build real businesses. I think we’ll start to see real production efficiencies emerge across almost every department." 

Link to Mark’s new book and the full recording in the comments.

Hard Skill Exchange

2026 Q: Who in my network is deploying ambient AI in GTM?

(AI that operates continuously in the background, sensing context, learning behavioral patterns, and acting proactively without explicit commands.)

Beyond ClickUp, which vendors or internal systems are running this in production today?

Open article: A new era of humans, with AI Super Agents. by clickup.com

A new era of humans, with AI Super Agents.

"Companies form teams they now call AI architects. 

The scope of that role has expanded significantly. In areas like support, they now own the agent. They design journeys, run experiments, and iterate continuously. It’s a broader and more interesting role.

There’s a quantitative shift as well. Teams can run experiments, measure statistical significance, and improve how agents engage with customers.

This pattern will extend across many functions. Roles will evolve, but the experience improves. Customers don’t wait on hold. Consistency increases because the system runs 24/7 and avoids many of the random errors that happen during human onboarding."

– Pol Peiffer, Head of Agent Development, Sierra

Link to the full recording in comments.

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Hard Skill Exchange #agenticsingularity

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