Everyone says they want growth. Very few organizations are actually built to deliver it.

In this episode of Signal & Noise, we sit down with George Musi—former executive across Publicis, WPP, IPG, and Horizon, now an advisor to Fortune 500 and high-growth companies—to unpack why growth consistently breaks inside large organizations.

This is not a conversation about frameworks or slideware.
It’s about what actually happens when strategy hits reality.

George brings an operator’s lens to one of the biggest disconnects in the industry right now: companies are overflowing with ideas, AI roadmaps, and transformation narratives—but still struggle to execute consistently. 

Why growth is structurally hard
Growth doesn’t fail because of a lack of strategy—it fails because of how organizations are wired. Incentives, silos, and decision-making systems break execution long before ideas do.

The HoldCo → Operating System shift (and what’s real vs narrative)
Every agency claims to be building an “operating system.”
George explains why most of these are still fragmented point solutions—and what a true system actually requires.

Why agencies are colliding with consultancies and platforms
As agencies move into data, tech, and integration, they’re stepping directly into the territory of firms like Accenture and Deloitte—while also competing with platforms like Google and The Trade Desk.

AI is not a tool problem—it’s an operating model problem
Most companies are layering AI on top of broken systems.
The result: more noise, not more output.
Real impact requires rebuilding how knowledge, workflows, and decisions actually operate.

The real future of the agency model
The FTE-based, labor-driven model is under pressure.
What replaces it? Outcome-based partnerships, deeper expertise, and a shift toward “growth architects” over execution vendors.

Why institutional knowledge is the ultimate advantage
Data is commoditized. Models are commoditized.
The real differentiator is the ability to capture, retain, and compound organizational knowledge over time—and most companies are terrible at it.

• Strategy isn’t the problem. Execution systems are.
• AI amplifies what already exists—it doesn’t fix it.
• The agency model is being reshaped from labor → leverage.
• The winners will be those who build systems of intelligence, not just tools.
• Growth will be owned by those who can connect strategy, operations, and execution into a single system.

George also joins Signal & Noise as part of our Exec Voices platform—bringing a no-BS perspective on growth, go-to-market, and what actually works inside complex organizations.

If you care about where agencies, consultancies, and platforms are heading—and what it really takes to scale—this is a must-listen.

Listen on Spotify | Watch on YouTube | Read more at signalandnoise.ai

What we cover:Key takeaways:

Most B2B companies don’t fail because the product is bad.
They fail because the system around it is broken.

In this episode of Signal & Noise, we sit down with Marc Sabatini, Co-Founder and Chief Commercial Officer at HighSignals, to break down what Brett has been calling the GTM Slop Problem—and why so many launches stall before the market even has a chance to decide.

Marc has spent 30+ years operating at the intersection of product, sales, marketing, and customer success—the exact place where strategy either turns into revenue… or quietly falls apart. 

This is not a theory episode.
This is how go-to-market actually works—or doesn’t—in the real world.

1. Why GTM breaks before the market even decides
The biggest failure point isn’t competition.
It’s internal:

• Misaligned teams
• Fuzzy narrative
• No shared system
• No clear ICP

As Marc puts it: “It’s not just slop from inexperience. It’s slop from lack of coordination.” 

2. The Six Dimensions of GTM (and where they fall apart)

We walk through the six dimensions every company thinks they have dialed in:

• Narrative (not messaging—commercial logic)
• ICP & targeting (focus vs. “sell to everyone”)
• Competitive readiness
• Offer & proof
• Field enablement
• Activation & measurement

The takeaway:
Most companies don’t have weak pieces.
They have weak connections between the pieces.

3. Problem Market Fit vs. Product Market Fit vs. Platform Market Fit

One of the most important frameworks in the episode:

• Problem Market Fit → You can sell, but it’s messy
• Product Market Fit → You can scale
• Platform Market Fit → You can compound

Most companies get stuck in the first phase—generating revenue without ever building a system that scales.

4. Why “great products win” is a myth

The uncomfortable truth:

The best product rarely wins.
The best go-to-market does.

Even strong products fail when:

• Narrative is unclear
• Proof is weak
• Sales is improvising
• Customer success is disconnected

Or simply:
Too much activity. Not enough coherence.

5. Partner-Based Buying is changing everything

Buyers aren’t buying alone anymore.

Agencies, SIs, cloud partners, and ecosystems are now:

• Influencing decisions
• Validating vendors
• Acting as gatekeepers

Which means:
You’re not just selling to the end buyer.
You’re selling to the entire buying system.

And that changes your proof stack completely.

6. Why most companies fail entering the US market

Marc breaks down a common pattern:

• Too broad ICP
• Weak local proof
• Misread buyer expectations
• Activity without traction

The result:
Burned time, burned budget, and burned field trust.

7. The real job of GTM: building a system, not running campaigns

This is the core idea of the episode:

GTM is not marketing.
GTM is not sales.
GTM is not a launch plan.

It’s a connected commercial system.

And when that system breaks:

• Messaging gets blamed
• Sales gets blamed
• Product gets blamed

But the real issue is lack of alignment and orchestration.

There’s more product being built right now than ever before.
AI has lowered the barrier to creation.

But it hasn’t solved:

• Positioning
• Differentiation
• Commercial execution

If anything, it’s made the problem worse.

More products.
More noise.
More GTM slop.

• Founders trying to turn product into revenue
• CROs, CMOs, and GTM leaders fixing broken systems
• Anyone launching B2B SaaS or AI products
• Operators tired of “more activity” being the answer

If there’s one idea to walk away with, it’s this:

A launch is not ready just because the product is ready.
It’s ready when the system around it is coherent.

Most companies never get there.

In Part 1, we talked about bots. In Part 2… we follow the money.

Brett and Rio sit down again with Dr. Augustine Fou, and this time the conversation goes deeper—and gets a lot more uncomfortable. Because the real issue isn’t just fraud. It’s the system that quietly profits from it.

We break down what actually happens between the moment a marketer places a bid and the moment a publisher gets paid. The gap is bigger than most people realize. In one example, a $1.50 CPM turns into just $0.15 for the publisher. In another, increasing your bid doesn’t get you better inventory—it just means someone in the middle keeps more of your money. And a lot of what gets reported as “performance” can be completely fabricated—clicks, conversions, even analytics data .

This episode is about that hidden layer most teams never see: take rates, pass-through opacity, spoofed supply, and the growing gap between what advertisers pay and what publishers actually receive.

Then we turn to what’s coming next: agentic AI.

Because if your inputs are broken, automation doesn’t fix it—it just scales the problem faster. The same systems that already struggle with transparency are now being handed more autonomy, more budget, and more control.

We get into why blended averages hide the truth, why the industry’s long-standing “1% fraud” narrative doesn’t hold up, and why so many optimization systems are quietly steering budgets toward cheaper, lower-quality inventory. We also unpack the difference between “attention” and actual human engagement—and why so much of what’s sold today is closer to performance theater than real outcomes.

By the end, this isn’t just a critique—it’s a playbook.

If you’re a marketer, this episode will change how you think about where your dollars are going, who’s extracting value in the supply chain, and what you can do—right now—to take back control.

Because the real question isn’t:“Is there fraud?”

It’s: “Who’s getting paid—and how much are they taking?”

Careers in AdTech don’t follow a straight line. They follow the market.

In this episode of Signal & Noise, we sit down with Evgeny Popov, a global media operator whose career cuts across publishers, agencies, AdTech platforms, and now television data—from News Corp to Lotame, Verve, and today at Samba.

This isn’t just a career story.
It’s a map of how the industry actually works.

Because Evgeny has seen every layer of the ecosystem up close—how publishers monetize, how agencies operate, how data platforms scale, and how companies position themselves for exits. 

And now he’s sitting at the center of the next major shift:
the transformation of television into a data-driven, measurable, and programmable channel.

1. From engineer to global AdTech operator
Evgeny didn’t start in media—he started in engineering. That technical foundation became a career advantage as the industry shifted toward data, automation, and programmatic systems. 

2. Building a career across every layer of the ecosystem
Publishers → agencies → DSPs → data platforms → CTV
This wasn’t random. It created a rare, full-stack perspective on how media actually functions—and where the leverage sits.

3. What actually drives successful exits in AdTech
Evgeny has been part of multiple acquisitions. The takeaway:
It’s not just about product. It’s about timing, signal, and positioning within the market.

4. Why relationships matter more than people admit
In an industry that constantly reinvents itself, networks compound.
Or as Evgeny puts it: “I collect good humans.” 

5. Inside Samba and the rise of ACR data
We break down how Automated Content Recognition (ACR) actually works—and why it’s becoming one of the most important data signals in the CTV ecosystem:

• What’s actually being watched (not just served)
• Cross-platform viewership across streaming, linear, and gaming
• A deterministic layer in an increasingly fragmented landscape

6. The real state of CTV (beyond the hype)
CTV is growing fast—but measurement is still broken.
Fragmentation, inconsistent currencies, and platform silos are holding the market back.

7. Television is becoming a data problem
Not a media problem.
Not a creative problem.

A data + identity + measurement problem.

And whoever solves that layer controls the future of TV advertising.

We’ve spent the last decade talking about:

• Programmatic
• Identity
• Data platforms

But television is where all of it converges.

And for the first time, we’re seeing:

• Deterministic signals (ACR)
• Cross-platform measurement
• Real competition for the “currency” layer

This isn’t just the evolution of TV.
It’s the reconstruction of the largest media channel in the world.

• AdTech operators thinking about where the market is heading
• Anyone working in CTV, streaming, or measurement
• Early-career folks trying to understand how to actually build a career in this industry
• People who want the real version of how this ecosystem works—not the slideware version

If you strip everything else away, this episode comes down to one idea:

The people who win in this industry aren’t the ones who stay in one lane.
They’re the ones who understand how the entire system connects.

Evgeny is one of those people.

In this episode of Signal & Noise, we sit down with Leighton Welch (CTO) and Sarah Martinez (CCO) from Tracer to unpack a fundamental shift happening across enterprise data, marketing, and advertising:

The stack is being rebuilt around data—not applications.

And at the center of that rebuild? The semantic layer.

This conversation goes beyond the usual “data unification” talking points. Instead, we break down the five structural shifts reshaping enterprise data strategy right now—and why they matter more than ever as AI moves from experimentation to execution.

Tracer provides a practical lens into this transformation. Not as another dashboard or point solution, but as a system designed to solve a harder problem:

How do you create shared, trusted business logic across fragmented systems so both humans and AI can act on the same truth?

1. Why enterprise data strategy is suddenly back on the front burner
AI didn’t create the data problem—but it exposed it. As Leighton puts it, AI is forcing organizations to prioritize initiatives they should have tackled a decade ago, from centralized data ownership to consistent definitions. 

2. The five shifts redefining the stack

• Semantic layers as the new business logic layer
• Warehouse-native architecture replacing SaaS silos
• Zero-copy activation reducing data duplication
• Governance becoming infrastructure—not compliance
• AI readiness as the new forcing function

3. The semantic layer as the control plane for AI
If AI is the operating layer, then definitions, context, and trust become the system of control. Without a shared “data dictionary,” agents don’t just fail—they amplify inconsistency.

4. Why monolithic SaaS is under pressure
Enterprises are moving away from copying data into every tool. The warehouse is becoming the system of record, and everything else is being forced to justify its existence.

5. The real problem: not data, but meaning
Most companies don’t lack data—they lack agreement on what that data actually means. Tracer’s approach focuses on turning raw data into reusable, governed business logic that can power decisions across teams.

6. AI readiness isn’t about models—it’s about foundations
Clean data, consistent taxonomy, shared definitions, and governance aren’t “nice to have.” They are prerequisites. Without them, AI becomes a very fast way to make very bad decisions.

For years, the industry debated identity vs. platforms, CDPs vs. composability, centralization vs. federation.

But that’s not the real shift.

The real shift is this: Data is no longer an input to the system.
It is the system.

And the companies that win won’t be the ones with the most data—they’ll be the ones with the most trusted, reusable, and operationalized context.

• CMOs and marketing leaders trying to make AI real
• Data and analytics teams dealing with fragmented stacks
• AdTech/MarTech operators navigating warehouse-native architectures
• Anyone tired of waiting 6 months for insights that are outdated on arrival

If you take one thing from this episode, it’s this:

AI doesn’t fix bad data. It exposes it.
And the semantic layer is how you fix it.

The CDP isn’t dead—but it’s definitely not what it used to be.

In this episode, Rio Longacre and Brett House sit down with Dale Renner, CEO of Redpoint Global—and one of the earliest architects of the space—to revisit the rise, fragmentation, and uncertain future of the CDP.

From helping shape the first-ever CDP deal to navigating today’s fractured landscape, Dale unpacks what actually happened—and where things went sideways .

They get into:

• Why the category splintered across identity, segmentation, and activation

• The real tension between marketing, IT, and data ownership—and why most CDPs failed to bridge it

• How “everyone became a CDP,” turning the category into a mile-wide, inch-deep mess

• Why many companies ended up with multiple CDPs—and still didn’t solve the problem

• The hidden truth: without high-quality data and identity, personalization is just noise

And most importantly—what comes next.

As AI reshapes the stack, Dale makes a clear bet:
The future isn’t vendor-built platforms or pre-packaged agents.

It’s data readiness, composable architectures, and company-owned agentic layers—built on top of systems that can actually handle identity, scale, and complexity.

Because in the end, this was never about CDPs. It was always about the data.

AdTech has built some of the most sophisticated technology in the digital economy — and some of the most forgettable marketing.In this episode of Signal & Noise, we sit down with Joe Zappa, Founder & CEO of Sharp Pen Media, to unpack a hard truth the industry rarely confronts:You can’t outsource belief.For decades, AdTech companies have treated marketing as something downstream — polish it, package it, delegate it. But Joe argues that’s exactly where things go wrong. When CEOs hide behind feature lists, jargon, and generic positioning, marketing becomes interchangeable. And in a crowded ecosystem where everyone claims better targeting, better measurement, and better AI, invisibility becomes the real risk.Joe makes the case that the CEO must be the chief evangelist — not a mascot, not a quote-approver, but an active communicator who can clearly articulate:

• What’s broken in the industry
• What they believe about the future
• Why their company exists
• And why anyone should care

We go deep on:

• Why AdTech messaging collapses into sameness
• The difference between product speak and narrative
• How to build a simple, usable messaging “Bible”
• Why controversy is often safer than invisibility
• The collapse of institutional gatekeepers — and what that means for founders
• How AI can amplify clarity — or create “slop cannons”
• Why humanities training may be more valuable than ever in the AI era

This conversation isn’t just about marketing tactics. It’s about leadership, conviction, and attention in a world where distribution is democratized and authenticity matters more than ever.If you’re a founder, CEO, CMO, product leader, or operator in AdTech, MarTech, or media — this episode will challenge how you think about voice, visibility, and responsibility. Because in 2026, you don’t win by having the best deck. You win by having the clearest belief.

Lucas Longacre officially joins Signal & Noise—and in his debut episode as an Executive Voices contributor, he starts exactly where great product work begins: in the mess.

In this conversation, Lucas sits down with Zach Grumet—product leader, operator, and one of the key figures behind Lucas’s own transition into product—to unpack what it actually means to “structure the ambiguity.”

This isn’t a conversation about frameworks or buzzwords. It’s about the real work:

• Why product managers don’t “ship features”—they own the mess

• How unclear problems, bad communication, and organizational friction kill good products

• The difference between product managers and actual product leaders

• Why most teams solution too early—and miss the problem entirely

• And how AI is quietly rewriting the rules of product development in real time

Along the way, they get into the uncomfortable truths: failed decisions, broken processes, internal chaos—and why those moments are where real product thinking is forged.

From reducing onboarding friction to predicting parking enforcement patterns (yes, really), Zach brings battle-tested lessons from FinTech, HealthTech, and SaaS—while Lucas connects it to a new reality where prototyping happens in hours, not quarters.

But beneath it all is a bigger question: If AI accelerates everything… what actually matters more?

This episode is about clarity in a world that’s only getting noisier. And it’s just the beginning.

Programmatic advertising has spent the last 15 years optimizing for one thing: speed.

But somewhere along the way, we lost something more important—control, transparency, and true decisioning intelligence.

In this episode of Signal & Noise, we sit down with Adam Heimlich, Founder & CEO of Chalice AI, to unpack what happens when an operator who spent decades inside the system decides to rebuild it from the ground up.

Adam’s perspective is different. He’s not theorizing about programmatic—he ran it. He understands the incentives, the inefficiencies, and the architectural limitations of RTB and DSP-driven buying. And now he’s building a new model.

We cover:

• Why RTB infrastructure is fundamentally constrained by legacy “pipes”- How Chalice is using containerized decisioning to move logic closer to the bidstream- What the Agentic RTB Framework (ARTF) actually is—and why it matters
• Why AI agents may reshape how media decisions are made (and who controls them)
• The shift from platform-centric buying → modular, agent-driven ecosystems- Why data is commoditized—but modeling and service are not
• The real implications of agentic trading and AI-native marketplaces

We also go beyond the tech:

• Adam’s transition from agency operator to founder- Building Chalice alongside his partner Ali Manning
• The reality of startup life vs. the mythology
• And yes… the philosophy behind AdTech shitposting

This is a conversation about more than bidding mechanics. It’s about whether the entire architecture of digital advertising is about to change—and who wins if it does.If you care about where programmatic is actually heading—not just what vendors are selling—this one is worth your time.

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What if dashboards are dying—and analytics is about to feel more human? In this episode of Signal & Noise, hosts Brett House and Rio Longacre sit down with Adam Greco, one of the most influential voices in digital and product analytics, to unpack a provocative idea: Vibe Analytics.Adam—former Omniture and Amplitude leader and current Product Evangelist at Hightouch—argues that the future of analytics won’t be defined by dashboards, SQL queries, or rigid reporting tools. Instead, it will be driven by natural language, AI-powered interfaces, and warehouse-native architectures that let teams ask questions the way humans think.Together, we explore:- What “Vibe Analytics” actually means—and why it’s more than a buzzwordWhether traditional analytics platforms like Adobe, Amplitude, and Tableau are at risk- How AI and conversational UX could democratize analytics (and de-specialize it)- What this shift means for analysts, marketers, CMOs, and data teams- Why warehouse-native stacks built on Snowflake and Databricks are foundational to what comes next- We also dig into real-world implications for measurement, attribution, personalization, and data collaboration—and debate whether this moment represents evolution… or extinction… for legacy analytics tech.If you work in analytics, marketing, product, or data—and you’ve ever felt constrained by dashboards—this is a conversation you don’t want to miss. Enjoy!