Claude is not a GTM silver bullet.

Social feeds are cluttered with claims that switching on Claude is a magic button that automatically sets meetings and generates millions in pipeline every week.

Claude is great, but that premise is ridiculous. And at least in my experience, its output is only marginally better than other LLMs.

Where it actually stands out is how seamlessly it plugs into your existing systems. If implemented well, it removes a significant amount of time spent on drudgery and makes your workflows far more efficient.

When that work disappears, you get your time back to focus on the parts that actually move deals forward. Conversations. Running more experiments. Building stronger narratives.

I worry people get turned off because the promises are an overreach. The real value is more subtle, which also makes it less exciting to talk about.

My view, and this applies to any LLM, is that if you focus on testing it across a few parts of your workflow each day, instead of expecting immediate results, you end up with much better outcomes over time.

So I wanted to outline a simple before and after that shows where I’ve actually found Claude to be useful.

For context, my company sells into the private equity vertical. We regularly run campaigns where we go to a PE firm’s website, scrape their portfolio companies, and build prospect lists with messaging tied to relevant portcos we’re already working with.

What this used to look like (8–12 hours)

In 2022, this was the workflow:

1. Find portfolio companies manually
   Go to the website, find the portfolio page, copying each company’s name into Excel. Here is Vista Equity’s portfolio page (brutal).
   Time: 1–2 hours

2. Research and tier companies
   Go to each portco’s website, add notes about the value prop (in my own words), decide what matters, tier accounts (basically a finger in the air exercise) and then prioritize what companies to go after.
   Time: 2–3 hours

3. Clean and structure the data
   Make it uploadable to LinkedIn Sales Navigator, needed to Google how best to optimize formatting for this.
   Time: ~20 minutes

4. Find contacts in each company
   Sales Navigator, company by company, role by role. Save to a list. By far the worst part.
   Time: 3–4 hours

5. Build lists + prep for outreach
   Export the list to a contact info scraper (Seamless, LeadIQ), clean again, organize in a sales execution platform like Outreach.
   Time: ~1 hour

6. Write messaging for that portfolio
   No real way to automate this, and at this point I’d be so burnt out that my “personalization” was half-assed at best.
   Time: 1–2 hours

All in, you’re looking at 8–12 hours before a single email or call is made.

What this looks like now (30–60 minutes)

Now the workflow starts with Claude (see image for exact convo I had).

1. Scrape portfolio + return CSV
   One prompt asking Claude to go to Vista’s website, scrape the companies, and format the output to be optimized for Clay
   Time: ~5 minutes

2. Structure for Clay + define enrichment
   Ask Claude what instructions to provide Clay’s copilot (Sculptor) to find the target contacts at each account (it knows my ICP).
   Time: 5–10 minutes

3. Upload to Clay + run enrichment
   Copy/paste the prompt you were provided into Clay’s copilot and tell it to run the enrichments. In a few minutes I have all of the executives emails, numbers, LinkedIn profiles, etc.
   Time: 10–20 minutes

4. Generate first-pass messaging
   Claude drafts a concise, tailored set of messages using what it knows about our work with the portfolio (has the context because it connects with my systems), and/or other valuable tidbits it finds in it’s web research. Not a magical message but a hell of a lot better than what I can come up w/ after 4 hours of list-building.
   Time: 10–15 minutes

5. Quick refinement + launch
   Tighten messaging, QA list, push to Lemlist (Sales Execution platform) to launch
   Time: 10–15 minutes

All in, 30 minutes to an hour. I can now run 2-3 of these per week. Before I’d have to bucket my time over the course of several weeks to launch only one.

Conversion isn’t much better, and it’s getting worse as inboxes get more saturated. Ironically, that’s happening because these tools are making it so easy to send volume.

There’s still opportunity here, however, and it’s worth doing as a complement to higher-converting channels. But only if you minimize the time and effort, so the work actually produces a positive ROI.

For the skeptical…

The posts claiming you can go from zero to 100 by clicking a magic button are mostly just engagement bait. And if you engage, they usually try to sell you something.

It’s one thing if this is just my algorithm, but I do think these exaggerated claims are keeping people on the sidelines.

Either they don’t try at all, or they try it once, don’t see immediate results, and assume it doesn’t work.

But if they take the time to learn how to actually incorporate it into their workflow, and focus on improving process efficiency rather than expecting immediate business outcomes, they’ll start to see real value.

For anyone feeling overwhelmed, start small. Find one thing in your daily workflow that’s genuinely annoying, or something you suspect AI could eliminate entirely, and spend 30 minutes to an hour trying to automate it.

It will take more time upfront as you experiment and figure out what’s actually possible. But once you start seeing what works for you and what doesn’t, it becomes very hard to go back to doing things the manual, more labor-intensive way.

SaaS stocks took a beating once Claude CoWork emerged as an existential threat.

The module’s been around for a while, but the release of an AI-powered legal tool triggered a sharp reaction. Investors saw it as a threat to companies like LegalZoom, sparking a selloff across the vertical.

From there it was an easy leap to a broader conclusion: if Claude can do this for legal workflows, what else can it replace? ServiceNow, Salesforce, HubSpot, etc. were all suddenly vulnerable.

What’s wild is that most of these companies are strong. Revenue and margin growth is intact. They consistently beat earnings. And in most cases, product usage looks healthier than it did a year ago.

But the selloff seems less about fundamentals and more about a growing unease about how work gets done. As LLMs get better at coordinating tasks across systems, why do workers need different applications?

AI replacing SaaS outright is not going to happen. The interface just becomes less important while the underlying system of record actually becomes MORE valuable. Why?

The LLM layer depends on these systems as a source of proprietary context to execute tasks, answer questions, and generate strategy. That data is far more valuable than anything an LLM can pull from public sources.

In Defense of SaaS

A VP of Sales who vibe-coded a CRM over the weekend is not replacing a production-grade, compliance-ready Salesforce deployment at any sizable company (despite what the market seems to think).

Salesforce and HubSpot did not become foundational platforms by accident. They have spent decades investing in infrastructure, security, permissioning, compliance, and performance under real-world load.

These systems support edge cases and failure modes that most organizations never see, but rely on every day.

Internal tools built quickly with AI can be useful, especially for narrow workflows or small teams. They are not substitutes for systems that need to operate reliably across thousands of users, business units, and regulatory environments.

The real moat is the data. Years of structured customer history, workflow logic, reporting integrity, and permissioned access create context that cannot be recreated over a weekend. When LLMs begin orchestrating work on top of these systems, that proprietary data becomes more valuable, not less.

SaaS companies that embrace this role and make integration seamless could end up in a stronger position. The UI may fade but the data layer will not. In an LLM-driven world, being the trusted source of high-integrity enterprise context is a durable advantage.

We will probably see business models evolve alongside the interface. Instead of relying primarily on traditional B2B seat-based sales, these companies may lean further into OEM-style partnerships, embedding directly into LLM ecosystems where revenue flows through usage, API access, or structured data licensing.

If LLMs become the primary control plane for enterprise work, being the trusted, high-integrity data engine underneath that layer could prove more scalable and economically attractive (significantly lower S&M spend).

Clay as an Early Signal

(FWIW - I know I talk about this company a lot. I don’t know why as I honestly haven’t truly embedded them into my processes. I think they’re just a buzzy name and are attacking the market the right way…)

Clay offers a useful window into how this transition is unfolding. On the surface it seems like yet another contact database, but its actual value comes from aggregation rather than destination usage.

I used to jump from Sales Navigator to find prospects on LinkedIn, port them into Seamless.ai to get their email/phone number, then port them to Outreach to execute on calls + emails. These workflows were very inefficient and led to a lot of distraction.

Clay pulls together multiple data providers, enrichment tools, and logic layers into a single workflow. While the execution layer is still in beta, this aggregation has already resulted in significant tool consolidation and significant productivity gains.

Clay also shares a percentage of the consumption fee with its underlying data providers each time their data is used (see image above). Those providers increasingly benefit from distribution without having to invest heavily in building or scaling their own direct sales motions.

Importantly, this also works in the inverse as well.

Clay recognizes this higher-order value of aggregation and has been intentional about integrating into LLMs early, treating them as the higher-order aggregation layer where work increasingly originates. They understand that the long-term battle is not about owning the UI, but about being easy to operate through whatever interface GTM operators prefer.

Wouldn’t be surprised if, long-term, their largest chunk of revenue came from the LLMs leveraging their data as an OEM.

The LLM as Control Plane

With deeper integrations and model context protocols, tools like Claude are starting to function less like applications and more like control planes. The model becomes the place where intent is expressed and work is coordinated.

In my own workflow, this is no longer abstract. Claude is connected to my email. It has access to Clay. It can work with Figma, Canva, and Microsoft Office. I can draft and analyze emails, build prospect lists, create decks, and model scenarios without bouncing between systems.

This is not yet fully automated or frictionless. I have tried to push parts of it further than the technology reliably supports today e.g. having outbound emails automatically written and placed into my draft folder for review.

It technically worked, but the latency and need for edits made it slower than reviewing drafts directly in the chat interface and sending them manually.

That said, the pace of advancement has been insane. Six months ago, Claude didn’t even have memory. The idea that it could execute most tasks in seconds with high-quality output and become the only interface someone logs into daily no longer feels far off.

Not quite there yet, but by EOY this is the likely reality.

Where the Jobs-to-Be-Done Argument Lands

Most people don’t want to “use a CRM.” They want outcomes:

• Context preserved

• Follow-ups handled

• Deals tracked

• Reporting available when it matters

For years, that required living inside a dedicated system. As LLMs get better at coordinating intent and execution, that expectation changes. The CRM doesn’t disappear. It becomes infrastructure instead of destination.

What remains durable:

• Proprietary data accumulated over years

• Security, compliance, and operational depth

• Structured systems of record that are hard to replicate

What shifts:

• The user no longer lives in the tool

• Work begins in an intelligent interface

• Software operates in the background

The companies that make themselves easy to operate through LLM-driven workflows will continue to grow. The economics may evolve, especially if LLMs become the primary distribution layer, but that reflects a shift in interface, not a loss of value.

From the operator side, it’s already visible:

• Fewer logins

• Less tab switching

• More time expressing intent and reviewing outcomes

Before diving into this one I recommend watching the (very slick, very brief) video below:

45 second Clay x GPT integration preview

TLDR - you can now live inside GPT and run outbound end to end. Find ideal prospects. Draft personalized emails. Send them. All with a few clicks, without really touching Clay itself.

The promise is huge because my biggest knock against Clay is that it’s powerful, but not built for AEs. To unlock real value you needed a sales/marketing ops person with a systems engineer skillset to configure it effectively.

I like to think I’m fairly advanced when it comes to building top-of-funnel systems but every time I used Clay previously I could never get it to stick.

If Clay can actually make the product both powerful and easy to use, the ICP shifts from “GTM engineers” to any salesperson who wants to run thoughtful, personalized outreach with a few prompts and a couple of clicks. A much, much larger TAM.

That is if this actually works.

I spent a full day pressure-testing this so you don’t have to. What follows is a step-by-step teardown of the experience; from zero → real prospects → personalized outbound.

(quick note: Clay announced an integration with Claude after this was written. I will plan on building more advanced workflows there over the coming months and will document that for a future article. As you’ll see below, GPTs reasoning capabilities are stagnating, and Claude has proven to be much more capable in getting actual work done.)

Step 1 — Connecting Clay and GPT

This part was easy.

I connected Clay to GPT using the official integration. No friction. No errors. Everything worked as expected.

You just go to the Apps section of your GPT instance, search for Clay, click the icon and login using your Clay credentials. Took like 30 seconds. Off to the races…

Step 2 — Prompting My Way Into a Wall

The next several hours were spent doing what I (wrongfully) inferred from the demo: prompting GPT to build things in Clay.

I tried asking GPT to create a table. Then to add companies. Then to add columns. Then to find prospects. Then to structure outreach.

GPT understood every request. It could reason through the steps. It could explain what should happen. But it could not actually do anything.

No tables were created. No rows were added. No columns were written. Nothing persisted inside Clay.

I tried rephrasing prompts. Narrowing scope. Being more explicit. Nada.

*live look at where things went off the rails about an hour in/right before my crash out. Basically GPT went on a diatribe about how you can only leverage GPT inside of Clay, not the other way around, which turned out to be technically true, but not helpful when analyzing the full context around what I was trying to accomplish:

Eventually it became clear that GPT had no write access at all. Worse, its sycophancy meant it never once pushed back or suggested a different approach based on the actual jobs-to-be-done.

Taken literally, this led me to conclude Clay’s marketing was sham. All because the system was unwilling to tell me I was approaching the workflow incorrectly.

The entire “operate Clay from GPT” premise collapsed (temporarily) here. I was ready to give up, and initially just ran the rest of the experiment through Clay itself (will detail a very good experience with their AI Co-pilot Sculptor in subsequent sections).

Thankfully, and mostly to avoid looking like an absolute idiot knowing I was going to be writing about this experiment, I tried it again and realized there was a real distinction between manipulating Clay from GPT and using Clay as a data source to find prospects and send emails from within GPT.

In short, I overcomplicated things from the start and went down a GPT rabbit hole filled with confident-sounding hallucinations and half-truths.

Step 3 — The Aha Moment

I hung up the cleats on this experiment for a couple of weeks, convinced that it was vaporware. I even wrote the first draft of this with a very, very, critical tone.

But then I received a marketing email urging what I’m assuming is a massive distribution list to try it, with a few simple prompts to get started.

I had to try it again. There was no way they could send this without it working, right?

It worked. Everything as advertised. I felt like an idiot. Here is my conversation with it for anyone who wants to give it a spin. TLDR - I gave it 3 successive, simple, prompts:

1. @clay can you find the senior most customer support leaders at intuit

2. Can you run an enrichment to find all of their emails

3. Can you write tailored emails using recent company news, mapped to what you know about FPS’s value prop

Boom. If you have your email connected through MCP you can just click a button and send the email (top right of the image). For me, I wanted to make a few adjustments so copy/pasted into an email draft and sent when ready.

My biggest advice is not to approach this like it’s 2015. That means no spending a full day prospecting dozens of accounts, dumping 150+ leads into automated sequences, then letting emails run in the background while you occasionally call to “check if they saw it.”

This workflow is built for smaller, focused batches. Pick one or two accounts per day, have GPT identify the right targets, draft tailored emails, and send them. It takes ~15 minutes a day and doesn’t overwhelm your systems or your prospects.

This also allows you to more dynamically A/B test variables like messaging and prospecting targets based on reply rates, meeting conversion, etc.

Also will help you manage credits more effectively.

Bonus — Sculptor is Really Good

During the original failed experiment, I gave up on the idea that GPT was going to be the control plane and shifted back into Clay itself for the sake of completing the jobs to be done i.e. find targets and send tailored emails.

Since I last used Clay they implemented Sculptor, their AI Copilot, which is awesome.

Whatever sits underneath it, whether it’s GPT or something adjacent, it makes Clay meaningfully more usable. You can prompt what you want and it will not only give you step-by-step guidance on how to build, but actually execute the process, too.

Table creation is faster. Structuring workflows is easier. The product feels less brittle and less hostile to first-time setup.

What used to take hours (and required a sales/marketing ops person with an engineering skillset) now takes minutes and is genuinely usable for everyday sales reps.

*One note, I will say the email writing is not great compared to the output that GPT produces. Their GPT is not tuned to your preferences, your GPT is, use accordingly.

Conclusion — The Real Takeaway

I initially overcomplicated this experiment by approaching Clay like a system GPT should be able to operate autonomously. That framing was wrong, and it distracted from where the real value actually is.

Once I reset the mental model and used the product as intended, the experiment became much more successful. What actually matters for GTM leaders and reps:

If you haven’t used Clay yet, start with the GPT integration

• This is the fastest way to experience Clay’s value for the first time.

• Use natural language to:

  • Identify target accounts

  • Find senior decision-makers

  • Enrich contacts with emails

  • Draft personalized outbound using real company context

• You do not need to build tables, logic, or workflows to get meaningful output.

• The time-to-value here is minutes, not hours or days.

Treat GPT as the front door, not the control plane

• GPT does not replace Clay’s underlying platform.

• It lowers the barrier so reps can get useful work done without touching ops-heavy setup.

• This is the right way to “get your toes wet” without overcommitting.

If you want to explore Clay’s native platform, use Sculptor

• Sculptor shortcuts much of the historical complexity.

• Instead of building enrichment logic and workflows from scratch, you describe what you want and the system executes.

• This makes Clay meaningfully more usable for non-technical users.

The broader takeaway:

Clay is moving in the right direction as a company, and these improvements materially expand the product beyond a pure ops-driven ICP. By reducing setup friction and meeting reps where they already work, Clay has a real opportunity to unlock far broader adoption and capture a much larger share of the GTM TAM, not just technically inclined teams.

PS - Proof I Tried Really Hard to Cover my Bases when GPT was being sycophantic and unhelpful during my crashout…

It’s become increasingly hard to justify the boiler-room SDR model.

The CAC payback on this approach has been weak for a while and hard to believe so many companies still operate as if it’s 2015.

As we head into 2026, agentic systems offer a path that costs little, generates more qualified leads, and converts more effectively with just one or two humans in the loop.

The Conversation

I recently chatted with a marketing ops wizard at a Series B deep tech company who has implemented a truly modern GTM motion.

It was clear she spent a lot of time thinking about how demand actually shows up, how it matures over time, and where human effort genuinely adds value. And despite the company having raised more than $35M, her directive was the opposite of what you might expect: build the leanest system possible for capturing demand so that more dollars could be allocated to the marketing efforts that actually move the needle.

Founder-led content. PR. Thought leadership. Education. Differentiated signal in the market.

Ten years ago, this same problem would have been “solved” with millions of dollars poured into an army of SDRs, layers of specialized marketers, and a sprawling collection of subscription SaaS tools.

Instead, she walked me through a workflow built on just two platforms: RB2B and Clay. Depending on website traffic and usage, the entire system costs roughly $5,000 to $10,000 per month.

No massive team behind it. No bloated SaaS stack costing $250K+ a year just to irritate prospects who aren’t in market. Just her, one SDR and a tightly designed system optimized around a single idea: capture as much real demand as possible, and make sure it’s handled correctly based on where the buyer actually is.

The SDR acts less like a hunter and more like an air traffic controller. His sole focus is high-quality conversations with leads to understand where those buyers actually are in their journey, not force a meeting. Then they would decide who warranted a pass to an AE vs. who belonged in longer-term nurturing through Hubspot.

The goal of nurturing wasn’t just to “stay in touch.” It was to ensure that when those buyers were ready, they were already indoctrinated in the company’s way of thinking and less likely to consider a competitor.

There was also no intention of hiring another SDR until it was absolutely necessary. Between being disciplined about tooling costs and explicitly optimizing for LTV:CAC, they’d embedded a culture of lean systems thinking from the start. The SDR was incentivized to incrementally improve the qualification process without sacrificing quality or follow-up SLAs.

They would need to be absolutely drowning before they’d consider adding headcount.

The result wasn’t just more leads. It was a system that captured demand without waste, without noise, and without adding people.

The System

Besides the cost advantages, the system is designed to capture meaningfully more value.

What matters isn’t just that it’s cheaper. It’s that it sees more demand, earlier, and handles it more intelligently.

Step 1: Market Education Campaigns

At a surface level, this looks similar in relative cost and output to a best-in-class SaaS marketing motion from 2015. In fact, the 2025 version may be more expensive simply because there is more capital flowing into content creation and distribution.

The difference is what that spend is designed to do.

Instead of trying to manufacture demand or push buyers into a sales process, the objective is to educate the market by establishing a clear, differentiated point of view. Buyers self-select into your orbit because the content is genuinely useful, regardless of whether they are actively evaluating vendors.

The tactical details matter less than the principle: everything should ultimately lead back to owned channels. If the founder is active on LinkedIn, their profile should clearly route interested readers back to the website. The website is not just a destination, it’s the intake layer for the entire system.

System view:

Market education
→ Pull-based traffic
→ Buyers arrive earlier in their journey
→ Website becomes the demand intake point

This is the first economic shift: you’re no longer paying to force motion, you’re paying to surface latent demand.

Step 2: Capture the Lurkers

In 2015, only buyers who explicitly opted in ever entered the system. Download a whitepaper. Register for a webinar. Ask to speak with sales. These people understood what was coming next and, by definition, were already deep into consideration.

Everything else was invisible.

Some teams tried to solve this with chat tools like Drift, often spending $30K+ per year to engage buyers who were interested but unwilling to fill out a form. The justification was that contact forms created too much friction. In practice, this was often overstated. Realistically, only about 10% of website visitors were actually in-market at any given time.

With RB2B or similar de-anonymization tools, that constraint disappears. You can identify the vast majority of website visitors based on IP data, capturing the other 90% who previously slipped through the cracks.

System view:

Website traffic
→ Known accounts instead of anonymous visits
→ 100% of demand becomes visible instead of 10%

This is where the economics quietly change. You don’t need more traffic. You simply stop losing most of the demand you already paid to generate.

Step 3: The Follow-Up

Next was an automation layer that turned visibility into action.

All identified visitors were routed via Zapier into a Clay table, where each account was enriched automatically. From there, every contact received a soft, automated email from the SDR asking how they could be helpful.

On its face, this is invasive. That’s why the tone matters. These buyers did not explicitly opt in, and most are still early in their journey. Aggressive follow-up would destroy trust and compress the system in the wrong direction.

Instead, the default behavior is a slow, ongoing nurture. Buyers continue to get educated. Their understanding of the problem (and the company’s framing of it) compounds over time. When they finally reach consideration, they are already aligned and far less likely to evaluate competitors seriously.

As a sales guy, I was skeptical that defaulting to a slow-drip nurture (instead of persistent meeting requests) wouldn’t create a leaky funnel. But her response was simple: lead volume is more than sufficient.

While there is no pre-system baseline to compare against, the marketing ops lead shared that roughly 15–20% more website visitors were already in consideration but never opted in, despite multiple, static CTAs. When these buyers received the soft SDR outreach, many responded quickly, requesting demos or trials.

System view:

Lurker capture
→ Earlier timing
→ Softer outreach
→ Higher-quality conversations
→ Improved conversion
→ Better LTV:CAC

The SDR compensation model reinforces this behavior. Instead of optimizing for meetings booked, the SDR earns a higher base with bonuses tied to overall company revenue and qualified leads created. This removes incentives to force conversations and rewards correct routing: AE handoff when appropriate, nurture when it’s not.

They report directly into marketing and spend meaningful time improving the system itself, refining messaging at each stage and engineering incremental efficiency gains.

As buyers become increasingly resistant to overt sales motions, making push-heavy systems less economical, this GTM design highlights a broader shift. The gains don’t come from working harder or adding headcount. They come from seeing more demand, earlier, and applying human judgment only where it actually changes outcomes.

That distinction becomes even clearer when you compare this system to how the old models were built.

How the old model actually functioned

The legacy GTM system was built around a single assumption: humans were required to surface demand.

From the start, everything was organized around headcount. Large SDR teams segmented by territory. Some handling inbound. Others managing chat tools. Most focused on outbound, attempting to create demand through volume and repetition.

Marketing drove traffic, but the website itself was a blunt filter. If a buyer didn’t fill out a form, start a chat, or respond to outreach, they effectively didn’t exist.

In practice, only a small fraction of real demand ever entered the system.

System view:

Website traffic
→ Form fills and chats
→ ~10% of demand becomes visible
→ Humans chase signals manually

The remaining majority of buyers read content, evaluated options, and left quietly. There was no visibility, no follow-up, and no learning loop.

Instead of addressing that blind spot, the response was almost always the same:

→ More SDRs
→ More tools
→ More activity

This approach worked when capital was cheap and buyers needed help navigating immature categories. But it was an expensive way to compensate for a system that simply couldn’t see most of the demand it was paying to generate.

The economics finally make sense

Once you compare the two systems side by side, the difference becomes hard to ignore.

A 2015-style GTM model might look like this:

• Twenty lower-skilled SDRs at roughly $80K all-in

• Around $1M annually in headcount

• Another $230K or more in tooling

• All focused on actively working a small fraction of actual demand

In the newer model:

• Market Education spend increases to get their ICP into their orbit, earlier

• AI-driven capture and enrichment might cost ~$8K per month

• One higher-skill SDR costs ~$120K annually

• Systems touch the vast majority of incoming demand automatically

But the real advantage isn’t just lower cost.

When you move from seeing ten percent of demand to seeing most of it, opportunity volume increases over time. Conversion improves because timing improves. Long-term revenue grows because you enter the buyer’s journey earlier and stay present longer.

You don’t need aggressive assumptions for this to matter. Even small improvements compound when the top of the funnel stops leaking.

This is why the best teams aren’t optimizing for activity anymore. They’re optimizing for unit economics.

What best-in-class GTM teams are really doing now

AI isn’t replacing people. It’s replacing the assumption that people are required to move information through the system.

Once that assumption breaks, headcount-heavy SDR orgs start to look like an inefficient allocation of capital rather than a growth strategy.

The teams that get this right early aren’t just leaner. They’re calmer. Their reps are more effective. Their AEs spend time where it matters. Their buyers feel less pressured and more understood.

And they build all of this without continuously adding headcount.

Prior to this year I used ChatGPT sparingly. Maybe twice a month to help me polish emails? But never really integrating it into my daily workflows.

In 2025, however, the doomer narrative around AI leading to mass unemployment became compelling. As career insurance, I went “all in” and bought the $20/month GPT plan to see how much efficiency there is to gain in the GTM space.

The goal was simple: become fluent enough with AI to understand where a sales guy can still create value in a future where machines make perfect decisions, execute instantly, operate around the clock, and never need healthcare.

The good news is after a full year of using it every single day across sales, GTM strategy, RFPs, writing, analysis, content creation, and personal tasks, the reality is far more straightforward:

AI massively enhances your existing skill set, but it does not replace the underlying expertise or judgment required to do the work well.

It accelerates productivity, it improves output quality and it eliminates a huge amount of drudge work. What it does not do is operate autonomously.

Yes, these tools will enable leaner and more productive teams, but there will always be a human in the loop. It behaves like a useful idiot: extremely capable when guided well and almost useless when not.

The year-long experiment showed how powerful this technology is once you know where it truly adds value. When it cuts time AND raises quality it feels like cheating. It changes how you operate.

That realization pushed me to expand beyond GPT and become fluent across other models too. And now, heading into 2026, I want to crystallize the workflows that delivered real value and start cutting the ones that did not.

Why I Use Multiple Models

I see some of these companies touting benchmarks for how good the model is, but haven’t found them to be terribly accurate. Gemini 3.0 is technically the “best” model for some reason, but I continue to find it the least useful LLM between the 3 majors. I experimented with Perplexity and Grok, too, but never really found them to add anything I was missing. Point being - just experiment until you find what works…

Brief breakdown:

• ChatGPT: best writer, best tone, best thinking partner

• Claude: best integrated knowledge worker because it can access my Google Workspace

• Gemini: Is helpful in Google Sheets when I need to implement complex formulas, but is weakest at writing. Don’t understand the hype…

• Lovable (Honorable Mention): best example of “AI enhances expertise, but cannot replace fundamentals”

No model does everything well, so the real advantage comes from knowing which to use and when.

ChatGPT: The Best Writer, The Most Human, The Most Reliable Partner

If I had to keep only one tool, it would be ChatGPT. Nothing else comes close on tone, clarity, or the ability to turn raw thought into polished output.

Where ChatGPT excels

• Email polish & Exec Communication - the best by far

• High-quality RFP and proposal creation - don’t try it in one shot though, make sure you provide plenty of context and build very polished answers question by question

• Marketing Assets - Can create Newsletter Articles, Case Studies and Executive Summaries with pretty much one prompt with enough context

• Slide copy (not slide design)

• Long-form thinking and ideation - helps me work through deal dynamics, best next actions for follow up, etc. if you assign it to be your CRO

And one of its biggest advantages is something people don’t talk about enough:

Voice input: the most underrated unlock of ChatGPT

Voice input lets you get every thought out in real time without the self-editing that naturally happens when you type. It is faster, more honest, and far more complete.

This is especially valuable for thorny emails, strategic thinking, or anything that requires nuance. Rather than staring at a blank screen trying to find the perfect opening line, I talk through the situation out loud. All the context, all the caveats, all the emotion. ChatGPT then turns that into a clear and professional message that gets the tone right.

The same applies to longer writing. The backbone of this article came from walking around with my phone, talking through ideas as they came. When something is fresh in your mind, you can capture it immediately instead of hoping you remember it later.

Voice is also a massive differentiator for GPT over Claude. Claude can handle structured prompts extremely well, but it does not have the same native ability to take jumbled thoughts and build a coherent and well structured output. GPT bridges the messy gap between raw thinking and polished communication in a way that other models currently cannot.

Typing feels efficient until you try voice. Then you realize how much friction you have been tolerating. When AI hardware matures, I believe voice will be the central experience because it unlocks the true speed and fidelity of thought.

Where ChatGPT still struggles

• It cannot design beautiful slides (copy is great, formatting is not)

• Image generation is fine for charts, not great for anything else

• It is siloed from my work systems and the copy/pasting process is becoming more inefficient, especially as I increase my Claude usage

Net: ChatGPT remains the single best model for thinking, writing, and communicating. If it ever gets deep native integrations into enterprise workflows the way Claude is currently winning, it will dominate everything.

Claude: Slightly Less Natural Than ChatGPT, But A Far Better Operator

Claude’s writing quality is excellent. It is slightly more structured than ChatGPT, but very strong. However, its real value comes from one thing:

MCPs give it access to my entire Google Workspace.

I’ve tuned it so it only focuses on work tasks, and it behaves like a true execution engine. GPT has released this recently, but its not as advanced.

What this enables

1. Sales call follow-up
Every transcript gets emailed to me. I tell Claude:
“Go into the transcript from today’s call and draft a comprehensive follow-up.”
It pulls the email, drafts the follow-up, and gets me 95% of the way there in 10 seconds.

2. Proposal creation
“Based on the scope we discussed in this transcript, and referencing the proposals we’ve built, draft a full project plan and SOW.”
It pulls everything needed and produces a coherent structure.

3. Re-engaging old opportunities
“Review all communication with this person and draft a tailored end-of-year reconnection email.”
This increased my output 2–3x without sacrificing personalization.

I want to truly emphasize how transformational this is. Before Claude, I took notes by hand on every sales call, constantly worried I would miss something important. That distracted me from listening deeply, asking better questions, and driving the conversation forward. Then I would spend 30 to 60 minutes after each call reviewing those notes and manually crafting a follow-up email that was never as strong as it could have been.

Scoping calls were even more painful. I was juggling both note-taking and trying to synthesize complex scope details in real time. Producing a project plan or proposal afterward often consumed an entire day of focused work.

Now that friction is gone. Claude captures the conversation, pulls relevant context from past work, and produces better output than I ever could manually. A follow-up email that used to take an hour is done in less than a minute. A proposal that used to take a full day gets drafted to 80 percent completeness in one or two hours.

This represents a 3 to 5 times increase in speed with a meaningful improvement in quality. It has completely reshaped how I spend my time and what I am able to accomplish in a given week.

Strengths

• Exceptional at context-specific work

• Best and most flexible integrated workflow of the three

• Great professional tone

• Pulls from real assets, not just a chat window

Weaknesses

• Slightly more formal than ChatGPT

• Still cannot design slides

• Still needs refinement passes to sound more human

Net: ChatGPT is the better writer, but Claude is the better worker. It is the only model that can actually shorten multi-hour workflows because it can retrieve and combine work assets on command. Honestly, I’m likely to double down here as I expect it to only get better on it’s current shortcomings.

Gemini: Overhyped

I wanted Gemini to be great and supposedly Gemini 3.0 is best-in-class on benchmarks. We have an enterprise license at work and it would save me a bunch of dough if it was better. But in practice:

Writing quality is brutal and continues to be brutal…

• Emails sound robotic

• Slide copy is mediocre

• RFP sections feel flat and lack nuance

• Refining drafts inside Gmail makes things worse, and has horrible UX issues

• I often paste Gemini drafts into ChatGPT or Claude and immediately get better results

Where Gemini is actually strong

1. Google Sheets
If I need a complex formula or multi-step logic, Gemini is extremely good at generating and applying it directly if I’m building a complex ROI model with a sensitivity analysis or something like that. Hoping they focus on replicating this value in Slides, Gmail and Docs soon…

2. Website prep via Chrome sidebar
For intro calls, I can open a company’s site and ask with their little Gemini widget on the top of the page:
“I’m speaking to the CX leader, summarize the business model, likely tools, org structure, and draft FPS demand hypotheses.”
Because it knows my workspace and context, this is genuinely helpful.

Future category

Nano Banana image gen might turn into something, but I haven’t tested it enough.

Net: Good for Sheets and browsing. Not viable for writing or any task where tone matters.

Side note - These “industry benchmarks” that claim Gemini and Grok are the best platforms, how do they arrive at that? Am I using them wrong or are these companies paying to get ranked this highly (wouldn’t be surprised) because in my experience they are so much worse than Claude and GPT…

Honorable Mention: Lovable, and why AI (at present) is just a force multiplier

Lovable is an impressive tool and encourage anyone to play with it. You can produce extremely polished, production-level UX without touching code. It excels at creating the front-end of a website or App and giving you a clean design direction.

But without backend or architecture knowledge, or even any coding experience in general, you hit a wall almost immediately. It has integrations with other back-end platforms, but its super hacky and for someone without coding experience is limiting.

This experience showed me that you cannot magically create software without understanding how software works.

This is the clearest example I’ve experienced of the broader reality:

AI is not replacing expertise. AI is amplifying expertise.

Which means:

• If you know what you’re doing, AI makes you 2–5x faster.

• If you don’t, AI magnifies your blind spots.

• The output is only as good as your inputs.

• There is no such thing as autonomous AI execution today.

This is why the “AI will replace every job” narrative falls apart when you actually use these tools in real workflows.

What This Year of Daily AI Usage Actually Proved

Great output still requires refinement and a shit-ton of context.

AI has removed a tremendous amount of friction from my day but it has not removed workflows or processes. It has simply increased my leverage.

2026 will be shaped more by integrations than raw model quality. The people who learn to orchestrate tools, documents, systems, and workflows through AI will have an outsized advantage. And I think Claude’s head start here will prove fruitful as the overall efficiency gains will come from significantly increased enterprise adoption.

Teams will run leaner.
Output per person will increase.
Strategy and judgment will matter more, not less.

The disruption will mirror every previous technological shift: uncomfortable at first, but ultimately beneficial.

Timely recent interview with the Managing Partner of Sequoia, for anyone looking to dive deeper into the topic: Sequoia’s Roelof Botha: Why Venture Capital is Broken & How Great Companies Are Built

I got sucked into the allure of VC after getting invited to participate in a fund.

Venture has this aura of being an exclusive club where outsized returns are the norm. The only real barrier is getting in.

In the public markets anyone can buy stocks from their phone. The returns are steady, but the story you hear is that the real wealth is created long before a company goes public.

And that is true for the top tier funds because they participate in those early winners. But the way it’s told, you’d think all venture funds have equal access. They don’t. Before I spoke with people who actually knew the space, I assumed even the average fund was outperforming the market.

That assumption was wrong. And the data clearly tells a different story. The average venture fund does worse than a basic S&P 500 index fund. Even after ten years, and with all the illiquidity and risk, you’re likely better off in the market (see graph at the top).

In reality, the top decile funds capture almost all the gains, the median fund delivers modest returns while the bottom half destroys capital. And the reason is straightforward:

• The best companies are obvious early.

• They choose the top and most experienced investors.

• Those investors keep winning allocations, which compounds their advantage and locks out everyone else.

Mid-tier funds are left competing for what’s left. They might back good companies, but rarely the grand slams that drive venture’s return profile.

The AI Era and Fund Inequality

In previous tech waves (social, SaaS, mobile) value creation was spread across hundreds of companies.

AI feels different. Most of the seismic value will concentrate in a few large language model providers like OpenAI, Anthropic, and Perplexity, along with the hyperscalers and chip companies that power them, such as Google, Microsoft, and NVIDIA.

The capital and data required to build these models create a massive moat and a high barrier to entry for anyone trying to start now (e.g. Meta). As the models become more tailored to individual users, the lock-in becomes even stronger.

There is also far less value at the application layer. If you build a wrapper around one of these models, the value still sits with the model itself. And as these tools become ubiquitous, most people will realize it is easier and cheaper to accomplish what they need directly through the LLM rather than through a thin layer on top.

Instead of hundreds of unicorns, we may see only a few dozen companies that matter.

Early-Stage VC Is Being Disrupted

Before AI, getting an idea off the ground required real capital. You needed a small team of engineers to build a first version, bring it to market, and iterate until you found product-market fit.

At that stage, investors were betting on the founder’s ability to create new demand, not just capture existing demand. That’s where outsized returns come from, but it takes time, capital, and a willingness to accept that most of these bets fail.

AI changes that. The tools now exist for early experimentation to happen fast and cheap. What once took quarters or even years can now be done in a weekend by a non-technical founder.

Someone can spot a problem, build a solution, and test it with real users in days.

The tradeoff is that it’s easier to copy. Fewer moats exist. But if you can capture a small slice of a market, bootstrapped and with high margins, you can build a solid six- or seven-figure business that gives you freedom and control.

Because that outcome is now more achievable, and the odds of creating generational wealth are smaller, more founders will choose it. They’ll focus on building sustainable, cash-flowing businesses to maximize both profit and time without having to deal with all the nonsense that comes with taking on outside capital.

At the other extreme, the only true grand slams will be the foundational LLM providers. The platforms everything else runs on. There will be some winners at the application layer, but far less capital will be required to reach them.

That means there are now fewer scraps for the average venture fund to fight over. The concentration of value will continue to move toward the few firms already backing the biggest players. The gap between the haves and have-nots in venture will become even wider.

Not sure if you could tell already, but I decided not to invest.

Will put it out here early in the article - I think Calendly is a super valuable tool. It’s just not a tool that justifies a $3B+ market cap.

I’m going to pick on them because they came up in conversation, but there are plenty of companies (ones that do have some value) that just raised way too much over the past few years and will never grow into their valuation.

I was chatting with a buddy who was VP of Sales at a large SaaS, and then moved to become Head of North America at a PE-backed company.

Back in 2020, at the peak of the pandemic’s shift to remote work, he rolled out Calendly licenses for his entire sales team. It was a no-brainer: the product worked. It solved an annoying, universal problem. His reps stopped wasting time on back-and-forth emails, customers loved it, and it just made everyone’s lives easier.

Fast forward a few years. He takes on a new leadership role at a new company. First item on his GTM wishlist? Calendly, again. This time, he’s ready to move fast. He knows the product. He knows the value. He just wants to buy.

But that’s not how it went.

Instead of a quick purchase, he’s forced through the standard enterprise gauntlet:
First, a BDR to “qualify” him. Then, an AE to “educate” him — on a product he already knew inside and out. By the end of the process, he was so turned off by the experience that he gave the deal to a competitor.

“I just wanted to buy the licenses. But they made me feel like I was shopping for an ERP system.”

From Product-Led Magic to VC-Driven Bloat

Calendly’s rise was one of the most celebrated growth stories of the last decade.

Founded in 2013, it was a breakout hit because it solved a specific, painful problem with remarkable simplicity. It didn’t need a sales team; it spread like wildfire through product-led growth (PLG). People signed up, loved it, and invited others.

In 2021, Calendly raised a $350 million Series B at a $3 billion valuation — one of the largest SaaS deals of the ZIRP era. It was a classic VC success story: big market, viral adoption, massive TAM expansion ahead.

But raising that much money rewires everything.

Suddenly, you’re no longer just building a great product — you’re expected to build an enterprise. You start hiring AEs, BDRs, SDRs, CROs. You need to “go upmarket” to justify the headcount. You’re not content with serving individuals and small teams; you need to sell to Fortune 500s.

And then you hit the wall.

The Copycat Risk — And Why the Moat Was Always Thin

Calendly’s core problem today isn’t just bloat — it’s that the product was never defensible in the way venture capital assumed.

The truth is: scheduling tools are easy to copy. Google Calendar, Microsoft Outlook, HubSpot, Zoom, and Salesforce all offer embedded scheduling links now. Startups like SavvyCal and Motion are eating into the market by offering similar features with slightly better UX or positioning.

Calendly wasn’t built on breakthrough technology; it was built on speed-to-market during a liquidity bubble. Once capital becomes expensive and competition commoditizes the category, that advantage evaporates.

The SaaS Efficiency Gap: Calendly as Case Study

The chart from Kyle Poyar’s “Growth Unhinged” newsletter tells the broader story here.

Early-stage SaaS companies (<$1M ARR) are seeing +75% growth in ARR per employee year-over-year. But mature companies (>$50M ARR) are facing negative returns — -34% ARR per employee — as they grapple with bloated teams, slowing growth, and markets that no longer justify their valuations.

Calendly is squarely in that camp.

Public estimates suggest it’s somewhere between $100–150M ARR. It’s hired and fired multiple CROs in the past few years. It’s lost the PLG magic that made it special, and now it’s stuck trying to build an enterprise sales engine for a product people fundamentally want to buy on their own.

The Bigger Takeaway: When SaaS Peaks at $50M

Calendly’s story isn’t unique.

We’re entering a new era where many SaaS companies — flush with cash raised during ZIRP — have hit the ceiling. They’ve maxed out their core product’s market. They don’t have a second act. They’re stuck at $50–100M ARR, bloated with GTM hires, pressured by investors, and facing copycat competitors who can match them feature-for-feature, but run leaner and cheaper.

In this world, the old VC playbook starts to break down.

You can’t brute-force your way to the next growth stage with headcount. You can’t justify a $3B valuation on a product that’s easily bundled by Google or Microsoft. And you can’t force customers who love your product into an enterprise sales process that they never asked for.

Where Does Calendly Go From Here?

There are only a few roads left:

• A slow march toward profitability, shrinking the GTM org back to what the product actually needs.

• A markdown sale to private equity, where it becomes a quiet cash-generating utility.

• A pivot, or expansion, into an adjacent product line (but nothing obvious has emerged yet).

For founders and operators, it’s a clear lesson:

Simplicity scales — but only so far. Capital is not a moat. And when you overbuild the machine, it can eat the very product that made you successful.

Calendly was built to save us from calendar ping-pong.

Instead, it got trapped in a game of venture-funded ping-pong, chasing growth at all costs — and now, like many SaaS companies of its era, it’s struggling to find what’s next.

Apple was built on the marriage of world-class hardware and a relentless focus on user experience. A formula that’s starting to look dated.

Siri, after 15+ years, still sucks. Apple Intelligence—despite the hype—is a cautious rollout, leaning on privacy theater and on-device optimization rather than open-ended intelligence.

And now, OpenAI is working with the Jony Ive to build an entirely new category of AI-first consumer devices.

For the first time in decades, Apple’s core moat—tight integration of hardware, software, and design—is being seriously challenged.

While we shouldn’t plan for their funeral yet, it’s hard to imagine they’ll be able to win (or even medal) in AI.

So, what if Apple stopped trying to catch up in AI... and started powering it instead?

Apple Is Behind—and Playing Scared

Apple’s approach to AI has been slow, risk-averse, and incremental. While OpenAI, Anthropic, Google, and Meta push the boundaries of reasoning, generation, and multi-modal understanding, Apple is still showcasing email summaries and smarter autocorrect.

It’s not that Apple lacks the talent. It’s that the culture—post-Jobs, post-Ive—is driven by optimization, not reinvention. Tim Cook’s Apple is brilliant at scaling existing products, squeezing margin, and expanding services revenue. But the boldness that led to the iPhone, iPad, and AirPods has given way to the most profitable stagnation in the history of capitalism.

AI Demands a New Infrastructure Layer

Training and deploying foundation models is brutally expensive. OpenAI will be spending billions annually on compute. Even inference at scale—once models are trained—requires massive energy and real-time performance across distributed data centers.

This is not just a software race anymore. It's an infrastructure war.

That’s where Apple, oddly enough, has a structural advantage:

• Over $60B in annual free cash flow

• Decades of experience in custom silicon (Apple Silicon, Neural Engine)

• A growing global network of private data centers

• A proven track record of optimizing performance per watt

While companies like OpenAI and Anthropic are begging for capital, chips, and energy, Apple is sitting on an empire of untapped industrial potential.

The Fork in the Road: Compete or Power the Ecosystem?

Apple has a choice:

1. Try to compete head-on in foundational AI.
   They could acquire a company like Perplexity and bring on a visionary AI-native CEO. They could try to rewire their culture around open-ended intelligence, creative risk-taking, and exploration.

   But that would require a level of humility, urgency, and cultural upheaval that seems unlikely under a leadership team that averages around 60 years old. The DNA just isn’t there anymore.

2. Or: Become the industrial backbone of the AI era.
   Apple could lean into its strengths—chip design, energy efficiency, supply chain mastery, and hardware-software integration—and become the Intel + AWS of the next computing epoch. Not by building servers for rent, but by:

   • Designing and manufacturing AI-optimized chips for other labs

   • Owning the most energy-efficient, secure, and privacy-respecting data centers

   • Building tools for developers that abstract away infrastructure complexity the same way the iPhone abstracted away PC complexity

This would be a dramatic pivot. But it could also be a way to build a new golden goose before OpenAI kills the iPhone.

It’s ironic that Apple, the company that defined personal computing, could become the company that powers impersonal computation. But they may have no choice.

AI is not just a software feature. It needs almost unimaginable energy, infrastructure, and new tools to be fully realized.

Apple doesn’t have to beat OpenAI to win - they just have to enable them.

In the wake of ZIRP and the rise of AI, the cost of starting a business has collapsed—leaving the venture-backed model looking increasingly outdated.

Have written about this previously: #25 - Goodbye J-Curve

So I thought I’d highlight the kinds of companies that will define the next wave of company building: bootstrapped businesses.

The ones that quietly compound, focus on customers over headlines, and build real leverage without burning cash.

Mailchimp is one of the best examples. Grown methodically, obsessed with the customer experience, and never distracted by investor hype.

This philosophy used to be the exception.

Ben Chestnut, Mailchimp’s founder, was recently on the Grit podcast (link here). Worth the listen, though Joubin (the host) is pretty brutal.

Here are a few key lessons from that conversation—for anyone thinking about how to build in this new era, without raising a dime.

When Intuit bought Mailchimp for $12 billion, people were shocked.

How could an Atlanta-based, bootstrapped email company with no enterprise sales team be worth that much?

Because Mailchimp wasn’t just selling email software. It was selling trust, empowerment, and brand identity to millions of small businesses. And it did so in a way that defied nearly every Silicon Valley norm.

1. They Chose Profit Over Pace

Key Detail:
Mailchimp never raised venture capital. This gave them absolute control over product, pace, and profit. While other SaaS startups were chasing breakneck growth to justify inflated valuations, Mailchimp was focused on building a business that actually made money.

Implications of Being Bootstrapped:

• Disciplined hiring: They didn’t overhire during growth spikes—because they couldn't. Every hire had to be accretive.

• No burn multiple obsession: Growth was never funded by future promises; it had to be sustainable from customer cash.

• Long-term orientation: Without a board pushing for a liquidity event, Mailchimp could invest in customer experience, not short-term metrics.

“VC money makes you skip steps. We wanted to build muscle, not fat.” – Ben Chestnut

Takeaway:
Being under-capitalized can be a feature, not a bug. It forces constraint-driven innovation—and builds real business fundamentals.

2. They Went Where No One Else Would

Key Detail:
While SaaS chased enterprise logos, Mailchimp served the ignored long tail—freelancers, side-hustlers, and tiny businesses who needed a simple, affordable marketing tool.

Why This Worked:

• Massive TAM: There are 30+ million small businesses in the U.S. alone. Individually low-value, but collectively enormous.

• Zero competition: These customers weren’t being served by traditional enterprise-focused vendors.

• High loyalty, low churn: Micro-businesses that found value early stuck around for years.

Brand Alignment:
Mailchimp’s quirky tone and design felt like it was made for small, creative businesses—not imposed from the top down.

Takeaway:
Big markets are often hiding in plain sight. The "unsexy" customer segments can create sticky revenue streams if you meet their needs and get deeply ingrained in their operational infrastructure.

3. They Mastered Self-Serve Before PLG

Key Detail:
Mailchimp had no outbound sales org. Instead, it built a product that sold itself through usability, delight, and clear value.

Their Product Strategy:

• Simple onboarding: You could launch your first campaign in minutes without support.

• Free-to-paid funnel: A generous free plan drove massive top-of-funnel, converting to paid as users grew.

• Feature discovery: Intuitive product design encouraged exploration and expansion.

Mailchimp executed all the hallmarks of product-led growth—land-and-expand, virality, usage-based monetization—without ever branding it as such.

Takeaway:
Before you hire your first AE, make sure your product can convert with minimal friction.

4. They Built a Brand, Not Just a Tool

Key Detail:
Mailchimp didn’t blend in with its competitors. It stood out—with absurdist illustrations, offbeat humor, and a voice that felt like a creative partner, not a corporate vendor.

How Brand Became a Growth Engine:

• Mailchimp Presents: Their content studio featured a docuseries and podcasts—not ads. It was built to earn attention, not demand it.

• Memorable UI: Freddie the chimp mascot and playful UX with him heavily featured gave the brand an identity.

Why This Mattered:
In a sea of feature-parity SaaS tools, the brand built recall, trust, and loyalty. It created a defensible moat beyond just functionality.

The Mailchimp Playbook, Applied

Profit isn’t just a financial outcome—it’s a strategic advantage. When you grow through revenue instead of raising capital, you maintain control, focus, and clarity. You don’t need to chase the next round or pivot for someone else’s metrics. You can stay aligned with your customers, build at your own pace, and make decisions that serve the long game.

When Apple launched AirPods in 2016, critics called them overpriced and unnecessary.

By 2019, however, AirPods had become one of the most successful consumer hardware products in history — outselling not only every other pair of wireless headphones but generating more revenue than Airbnb and Shopify combined.

So how did a pair of earbuds go from joke to juggernaut? And what can SaaS companies learn from that trajectory?

1. Frictionless UX Wins

AirPods were the rare product that “just worked.” Open the case near an iPhone, and a pop-up appeared. Tap once, and you were paired. No app download. No tutorial. No guesswork.

That level of seamless onboarding is the holy grail in SaaS. The most successful software products eliminate friction between signup and value.

• Zoom grew explosively because it required no setup — click a link, you’re in.

• Notion and Figma gained traction by making powerful capabilities feel intuitive from the first interaction.

This goes beyond good design. It’s about collapsing the time between interest and impact. When users don’t need to ask “how do I use this?” they stick around longer and evangelize faster.

Key Takeaway: The faster a user gets to “aha,” the more likely they are to become a promoter.

2. Usage Is the Best Marketing

Apple never had to ask users to show off their AirPods — they were visible by default. You’d see them on the subway, in airports, at coffee shops. And with every sighting, a bit more social proof accumulated.

This same pattern exists in the best PLG (product-led growth) SaaS tools.

• Slack invites bring new users into an ecosystem without a sales pitch.

• Calendly links make a scheduling tool visible in every outbound email.

• Loom videos start replacing walls of text in onboarding, sales, and support — spreading the product passively with each use.

By building distribution into the usage, these tools expand virally without massive spend.

Key Takeaway: Make your users your growth engine — not just your customers.

3. Ecosystems Create Moats

AirPods technically work with Android devices, but you lose most of the magic: auto-switching, “Hey Siri,” battery sync across devices, and instant pairing. The product is great — but it’s amazing inside the Apple ecosystem.

That’s a play SaaS companies have run for years.

• Salesforce locks in customers through custom workflows, integrations, and data centralization.

• Microsoft 365 wins because it bundles together everyday workflows and builds network effects across teams.

• AWS creates stickiness by offering breadth and depth that make migration painful.

The moat isn’t just about product features — it’s about how deeply embedded the product becomes in a company’s workflows.

Key Takeaway: When your product becomes infrastructure, customers stop shopping for alternatives.

AirPods weren’t just a hardware win — they were a masterclass in product strategy. They nailed what every SaaS company is chasing: fast onboarding, organic distribution, and ecosystem lock-in.

In 2025, when standing out is the only way to convert meetings, bots that can’t understand nuance—or worse, deliver painfully obvious “personalized” messages—just burn through good leads with nothing to show for it.

That said, expertly bundling human and virtual agents is becoming a necessity to drive productivity and reduce costs. Many of the best orgs are already doing this.

I’ve spoken at length about Clay. I like the platform and I’m not paid to say that.

But it’s not a silver bullet. It’s powerful but still very hard to use effectively. And depending on your specific needs, there may be more intuitive tools out there.

Still, Clay is pushing an important shift in how we think about go-to-market strategy—especially with its advocacy for a new kind of role: GTM Engineering.

Replacing your SDR function with GTM Engineering will be the smarter path to sustainable, profitable growth.

What is GTM Engineering?

GTM (Go-to-Market) Engineering is an emerging discipline at the intersection of growth, operations, and automation. Its purpose is to build scalable systems that:

• Identify, enrich, and qualify leads

• Personalize outreach using real-time data

• Automate routing, follow-ups, and CRM workflows

• Continuously test, learn, and optimize outbound motions

Rather than scaling headcount to build pipeline—where outdated systems dilute ROI—GTM Engineering challenges reps to treat pipeline like a product: engineered for efficiency, repeatability, and scale. When built right, this “product” generates pipeline growth without adding incremental cost, allowing reps to drive more revenue from the same inputs.

Why GTM Engineering is Replacing SDRs

1. Efficiency > Headcount
Traditional SDR teams operate on high-volume, low-yield tactics. But tools like Clay, Apollo, and Clearbit can now automate much of that work—faster, cheaper, and often with better results.

2. Personalization at Scale
No SDR can handwrite 200 thoughtful emails a day. GTM Engineers build systems that respond to live signals—like job changes, funding rounds, or hiring sprees—and automatically tailor outreach.

But a human must own the build-test-iterate loop. No set-it-and-forget-it here.

3. Systematic Pipeline Creation
Instead of hiring more SDRs, GTM Engineers create workflows that:

• Pull in enriched, intent-based leads

• Trigger personalized messaging

• Push sequences into Outreach or HubSpot

• Automatically log interactions into the CRM
  The result: a scalable, always-on pipeline engine.

What a GTM Engineer Actually Does

A GTM Engineer is part technologist, part operator, part growth hacker. Responsibilities typically include:

• Using low-code tools (Clay, Zapier, Retool) to build automation

• Integrating data sources (LinkedIn, Crunchbase, job boards) to enrich targeting

• Running experiments to test messaging and timing

• Setting up trigger-based sequences (e.g. “Company X raised $20M → send this sequence”)

• Translating GTM strategy into systems that scale

Where It’s Already Happening

• Early-stage SaaS: Founders build automation instead of hiring reps to generate initial market feedback, and land their first few customers.

• Bootstrapped startups: Stay lean to stay profitable.

• Series B+ orgs: Replacing SDR headcount with GTM Engineers for predictable, cost-efficient growth as markets shift towards valuing profitability and EBITDA vs. Revenue growth.

What This Means for GTM Teams

The old-school pod model (AE + SDR + CSM) is giving way to a more modular, systems-driven approach:

• AEs focus on closing

• GTM Engineers own pipeline creation

• RevOps ensures optimization and insight

• AI copilots support every role along the way

Where to Start

• Audit your SDR function—what’s truly manual vs. what can be automated

• Pilot tools like Clay, OpenAI, and Clearbit to build systems

• Hire or upskill a GTM Engineer before adding more headcount

This isn’t about killing the SDR role—it’s about evolving it.

While fully automated “soup-to-nuts” AI SDRs may someday show promise, I remain skeptical. The moment one tactic gains traction, it’s copied en masse—flooding inboxes and turning once-effective outreach into noise. The only sustainable path to scalable pipeline growth is blending human strategy with automated systems—using them to test, learn, and iterate continuously. Humans guide the “why”; automation powers the “how.”

Most aren’t old enough to remember (including myself), but the door-to-door salesman was THE dominant force in sales.

Think vacuum cleaners, encyclopedias, or early telecom services. Without modern infrastructure like TV, phones, email, or LinkedIn, a well-dressed guy knocking on your door was often the only way a company could create awareness and drive revenue.

But that era is long gone.

As consumer buyer journeys evolved, the door-to-door model collapsed. Sure, management tried to keep the engine running — encouraging reps to “knock on more doors” or “try new pitches” — and those tactics might’ve worked on the margins for a while. But ultimately, structural shifts in the market made the model unsustainable.

Some still argue that cold outreach isn't dead. My response: how many door-to-door salesmen have you seen in the past five years?

If that motion continued working, it would still exist. But it doesn’t — because the economics no longer made sense. And the same thing is happening to inside sales teams today.

Let’s break down the parallels:

Then: The Decline of Door-to-Door

• Information Asymmetry Disappeared
  Before, buyers had limited access to product knowledge. Salespeople filled that gap. But once advertising, catalogs, and mass media arrived, buyers could learn on their own — making reps less essential.

• More Efficient Channels Emerged
  TV ads, direct mail, and later e-commerce enabled companies to reach broader audiences at lower cost. Door-to-door suddenly seemed inefficient, slow, and intrusive.

• Market Saturation and Skepticism
  As more salespeople hit the same neighborhoods, buyers grew weary. Trust eroded. Homeowners stopped opening their doors. The channel became blocked — not through regulation, but through irrelevance.

Now: The Same Shift in Inside Sales

• Information Asymmetry Is Gone
  In SaaS especially, buyers don’t need SDRs to educate them. They read reviews, check G2/Reddit/LinkedIn, lurk in Slack communities, and often arrive to the first sales call more informed than the rep.

• More Efficient Channels are Emerging
  Product-led growth (PLG), self-serve buying, and AI-powered chat let buyers explore solutions on their terms. These motions are to SaaS what TV and e-commerce were to consumer goods — more scalable, more efficient, and aligned with how buyers actually want to buy.

• Volume-Based Outreach Is Saturated
  Cold emails and LinkedIn DMs had their moment — when they were novel. But now, everyone’s inbox is flooded. Just like homeowners tuning out door-to-door pitches, today’s buyers ignore outreach unless it's hyper-relevant and timely.

The companies winning today aren’t relying on brute-force outbound. They’re investing in:

• Hyper-personalized engagement

• Embedded product value

• Inbound demand generation

• Community-led growth

Just like the door-to-door model gave way to better systems, the old playbook for inside sales is being replaced. Not because people got lazy — but because buying habits evolve and the ROI on previous best practices erode.

Even as the economy rebounds, layoffs in the tech sector—especially in sales—will continue.

While returning to growth mode is a top priority, the era of "growth at all costs" is officially over. Companies have already learned to do more with less, and AI will ensure this trend continues.

The market is responding accordingly. As M&A and IPO activity picks up, acquirers (both strategic and financial sponsors) are laser-focused on profitability.

Deals based on revenue multiples will likely be a thing of the past as fast-growing but unprofitable companies no longer command premium acquisition prices.

This shift marks a fundamental change in how tech businesses operate. Below I outline the timeline around this shift.

Yesterday: Growth at All Costs

Companies spent as much as they raised, hiring sales and marketing talent without a justification for ROI. In a competitive market, whatever company grew the fastest would win, so VCs pumped significant $$ into growth without abandon.

Once their company won, they’d achieve economies of scale and naturally, profitability would follow.

What actually happened was that when capital was cheap and abundant, the tech ecosystem thrived on a cycle of mutual back-scratching.

• VCs pumped money into startups.

• Startups sold products to each other.

• Strategic acquirers & PE firms—often with deep VC ties—bought fast-growing but unprofitable companies, betting they could streamline operations post-acquisition.

In the era of Yesterday, sales and marketing teams operated with little financial scrutiny. As long as revenue grew, no one cared about efficiency. Startups routinely burned millions on headcount and expensive tools, assuming future funding rounds would cover the costs.

A typical Seed-stage company raising $10M at $1-5M in revenue would spend aggressively on product development while keeping GTM somewhat efficient—at least until a Series A round, when rapid hiring would inevitably push the GTM function into an unprofitable state.

Profitability was a distant concern.

Today: The Efficiency Era

Then, the music stopped.

Rising interest rates forced the industry to pivot from revenue growth to cash flow and profitability. In 2022, the market corrected hard: tech layoffs surged, budgets shrank, and GTM teams were forced to prove their financial ROI.

Surprisingly, many sales leaders now say that streamlining their teams and tech stacks has actually improved efficiency, productivity, and revenue growth.

With fewer resources, sales teams became scrappier, more focused, and ultimately more effective. Meanwhile, CROs are now judged as much on P&L as they are on revenue growth, ensuring there will be no return to "growth at all costs."

Sales tech is adapting to this new reality. Solutions like Clay consolidate multiple prospecting tools into one, allowing reps to work more efficiently and purchase software autonomously—often without CFO approval or company-wide adoption.

Tomorrow: The Rise of Automated GTM

A future without traditional sales teams is becoming increasingly plausible.

SDRs will no longer be in-house employees responsible for lead generation, prospect list building, and executing multi-step outreach sequences. This model is proving highly inefficient—entry-level reps are spread thin across multiple functions, unable to develop deep expertise in any one area. And with traditional activity-based metrics failing to yield predictable results, the cost burden of both tools and personnel remains on the company.

Instead, outsourced agencies with shared-services models will take over. These firms will structure SDR roles around specialized functions—research, list building, personalized email copywriting, or cold calling—maximizing efficiency through domain expertise while leveraging best-in-class technology at scale.

The same trend is emerging in marketing, with AI-powered agencies handling content and demand generation.

For bootstrapped founders prioritizing profitability, outsourcing entire GTM functions to AI-driven firms could become the norm.

• Outbound prospecting could be fully outsourced, delivering highly qualified leads at scale.

• Sales execution could be streamlined, with founders focused on closing deals rather than managing teams.

• Marketing, sales, and even product development could be outsourced end-to-end.

While founders will still be responsible for identifying problems, building MVPs, and iterating toward product-market fit, scaling may look entirely different. AI-powered systems and external agencies could handle everything—without the overhead of a traditional GTM team.

The best part?

If founder/CEOs build businesses generating a few million bucks a year at really high-margins, there’s no pressure to grow if they can pocket most of it. Expect a significant reduction in unicorns over the next decade in favor of highly-profitable, digital, small businesses.

I had to get a new iPhone last week.

My iPhone 7’s battery life was non-existent and the general UX was becoming unusable. Despite my love for the home button I had to bite the bullet and upgrade.

The Apple Store in midtown Manhattan was riddled with employees in matching red shirts. And I soon learned that these “Geniuses” were actually sales reps in disguise - within 30 seconds of walking in I was accosted.

The rep was great. Super cool, not pushy, asked a lot of questions, and knew a ton about the trade offs for each product based on what I wanted. He showed me some awesome functionality with Apple Intelligence and won me over with an incredible demonstration of automated email drafting - something that I could very much see myself becoming dependent on.

I bought the newest iPhone 16 for ~$1000. Significantly more expensive than the 13, 14, or 15 models. But they don’t support Apple Intelligence. Something I now find myself valuing significantly.

Without the rep giving me FOMO for advanced AI functionality, I would’ve bought an older (much cheaper) model solely based on better battery life.

Vaporware

When I got home I was annoyed. I opened the phone and (obviously) went to test out Apple Intelligence. When nothing he demoed to me was available I realized I was sold on product roadmap.

While I’m not returning the phone (too much hassle, don’t want to sync to another device, etc.) it did get me thinking about how Apple will continue growing in the coming years.

They’re whole schtick is focused on their products being simple, intuitive and with every last detail (both inside and out) designed to perfection.

Now they have sales reps hawking half-baked products because new iPhone releases no longer sell like Air Jordans.

When you need to hire a fleet of sales people to move inventory - because the product’s innovations can no longer sell themselves - you question whether something more existential is happening at the most valuable company on the planet.

Will their bull run continue despite not having a real breakthrough innovation since the iPhone?

The Golden Goose

While it’s no secret they’ve lost a level of innovation post-Jobs, it’s clear that Tim Cook as his successor was the right move.

An expert in supply chain management and operational efficiency, he took a very valuable company to extraordinary heights. This largely through production optimization, negotiating incredible deals with suppliers (primarily Foxconn, and AppStore developers) and financial engineering practices that gave Apple the largest cash pile in the history of the world.

Essentially, he took one of the fastest growing and most innovative companies of all time, and made it the most profitable on the backs of Apple’s existing ecosystem.

Since taking the helm, however, there hasn’t been a ‘real’ breakthrough product.

Cook’s success has been focused on milking all of the remaining value out of the existing portfolio. Take a look at Apple's revenue breakdown by product line for the most recent quarter (Q3 2024):

1. iPhone: $39.30 billion, representing approximately 45.8% of total revenue. This is slightly down year-over-year by 0.94%.

2. Services: $24.21 billion (28.2% of revenue), showing significant growth of 14.14% year-over-year.

3. Wearables, Home, and Accessories: $8.10 billion (9.4% of revenue), with a small decline of 2.26% year-over-year.

4. iPad: $7.16 billion (8.4% of revenue), which increased by 23.63% year-over-year, partly due to the introduction of new models.

5. Mac: $7.01 billion (8.2% of revenue), reflecting modest growth of 2.46% year-over-year.

In total, Apple reported $85.78 billion in revenue for Q3 2024, marking a 4.87% increase compared to the same quarter last year. Not bad, but it’s clear growth has slowed significantly.

The bright spot, Services, include things like the AppStore, Apple TV, Apple Music, Apple Care etc. This growth is extremely high-margin relative to its hardware counterparts and very much aligns with Tim Cook’s maniacal focus on the business fundamentals.

However, none of these revenue generators represent anything beyond extracting marginal value within the existing Apple ecosystem. The massive financial success of blockbuster products like Airpods and the Appstore is totally dependent on people owning an iPhone.

As a corollary, Cook shut down the Apple Car project, and is very close to shutting down their Apple Vision (VR headset) product too. These projects represent opportunities for major disruption that could carry the next generation of growth. But they’ve been unable to execute.

This means that if a major disruption to the iPhone occurs, just like they did to Blackberry, the rest of their portfolio is in trouble.

Meta’s Quest, and even Neuralink, could wreak havoc if they catch on in a big way as their “jobs to be done” could replace the need for phones, computers, keyboards, etc. - gutting Apple’s golden goose.

What now?

So, is this newly minted sales force a sign of “all things must go” to generate as much cash as possible ahead of a downfall? Perhaps.

But perhaps Cook’s calculus is a bet on his same tried-and-true playbook. And a bet on a potential wave of hardware disruption being way farther in the future than most of us think.

Apple Intelligence is, right now, nothing more than vaporware. But Apple still absolutely dominates the hardware space - from a revenue, profit and per unit device perspective - they still run laps around the next closest competitor in the phone, computer and tablet market.

What’s more is they finalized a partnership with OpenAI, the company most likely to dominate the next wave of technological transformation. As long as Cook can execute on ensuring that partnership powers the next wave of devices, they are well-positioned to fend off significant disruption.

If this holds true, Apple just needs to execute on two things:

1. Ensure they get as many people to upgrade to devices that are AI-enabled. Hence, the sudden influx of sales reps heavily incentivized to get folks locked into the new, AI-enabled devices.

2. Ensure that, ahead of the iPhone 17’s release next year, Apple Intelligence advances to the point of making it a “must have” device to maintain their relative premium in the market. Ideally getting the functionality to the point where those reps slinging 16’s are unnecessary.

Tim Cook clearly realizes that he is not a “breakthrough product” guy.

He is just a master at business model innovation → ensuring that Apple’s existing portfolio advances enough to support the next 10 years of profitable growth. Largely on the backs of other people’s inventions to minimize operating costs.

Continuing to execute against this will ensure Apple keeps generating more cash than the GDP of many developed nations.

The market cap of PAN (currently ~$130B in Nov. 2024) has increased 6.5x since Arora took the reigns as CEO. The Cyber market has increased significantly, but PAN is outpacing their competitors by a significant margin…

CISOs (Chief Information Security Officers) weren’t really a thing until enterprise org’s embraced the cloud.

The productivity & efficiency gains associated with migrating company data from on-premise servers to the public cloud were enormous. Storage became essentially limitless and the costs (at scale) reduced significantly.

Teams could easily access information, build “things” more efficiently, work more collaboratively and, in aggregate, accelerate the pace of innovation to remain competitive in the marketplace.

Within a few short years, migrating to the cloud became an absolute necessity - businesses couldn’t keep up without it.

The Rise of the CISO

In parallel, hacking became much, much easier. Nefarious actors no longer had to be in close physical proximity to a company’s server racks to steal sensitive information they could exploit for $$$.

So, while enterprise companies invested heavily in the cloud to remain competitive, they needed to build out a net-new office squarely focused on protecting their data, sensitive customer information and IP.

What makes a Chief Information Security Officer (“CISO”) tick?

These were previously VPs of Information Security that rolled up to the CIO. The CIO handled everything related to IT - like issuing employees new computers.

As digital security quickly became a company’s highest priority, enterprises split that function out from under the CIO, and hired an expert to assemble a team focused on building a digital Fort Knox.

Given their importance to cloud migration efforts, CISOs became some of the highest paid executives with the most discretionary spend. They invested in (lots of) tools, processes and workflows to ensure their company data was as protected as possible.

In response to this shift, VCs funneled tons of dough to cyber startups vying for the opportunity to be one piece of a CISO’s puzzle. This was a gold rush and many people were looking to cash in.

Several of the early players quickly established themselves as a necessity for every program. Developing core competencies focused on shoring up specific, yet major, vulnerabilities.

However, the fast followers, protecting more niche areas of the value-chain experienced difficulties scaling. Why?

CISOs don’t like sales people.

As the volume of sales outreach increased in proportion to the significant rise in Cyber companies (often with great technology), all members of security offices simply tuned out.

Nikesh Arora and Palo Alto Network’s Transformation

Let’s be clear, there are several cybersecurity companies with eye-popping market valuations. For example, Crowdstrike, even after the July incident, has a market cap of ~$83B dollars (as of Nov. 2024).

But the reason PAN is worth ~$130B (significantly outpacing any of it’s competitors) is not just because of the rise of cloud, it’s because of two business model innovations implemented by Nikesh Arora - CEO since 2018.

1. M&A Strategy:

When companies reach a certain stage of maturity, it’s typical to acquire next-gen technology to jumpstart growth and maintain relevance.

At the time, PAN was largely a hardware security company and desperately needed to shift towards SaaS. If not, they’d be rendered obsolete.

When defining the focus of their M&A strategy, Arora settled on paying a premium to acquire market leaders and great technology in more niche markets.

This based on the discovery that after ~$50M in ARR, these companies saw declining growth. There was a disconnect, as these were the best technologies in fast-moving market segments.

Why? Remember…CISOs were starting to really hate sales people.

As those great technologies got to a certain stage, VCs pushed them to invest heavily in sales & marketing to scale. Annoyed with the state of their inboxes, CISOs shifted away from evaluating markets for the best technology, to asking incumbent vendors to provide solutions.

If these vendors had products they were often sub-par technologies. But it eliminated the time and effort associated with a prolonged market evaluation. CISOs were dissatisfied with this reality but it checked-the-box…

Paying a premium for the market leader turned out to be a great long-term strategy. PAN could now capitalize on being the existing vendor that consistently provides the best technology solution.

2. GTM Transformation

In 2018, PAN’s GTM org was built around product-specific siloes. Sales reps sold one product, understood everything about it, but knew nothing about the rest of the portfolio.

With the evolution of the CISO’s buyer journey - shifting from searching for the best technology, to asking existing vendors to provide sub-par solutions - Arora refashioned the organization to become more account focused. Incentivizing reps to sell the entire portfolio into fewer accounts/verticals.

Essentially, they needed to become a CISO’s most trusted advisor. A deep understanding of their unique business needs and ever evolving priorities, to continuously deliver the right solutions.

PAN executed against their goal of acquiring the best technology in emerging markets. Thus, CISOs never needed to go to any of their other vendors - they just called up their new best friend who quickly configured new solutions into their existing security stack.

As a result, PAN revenue at existing accounts skyrocketed. What’s more, this transformation reduced S&M costs significantly; instead of building a whole sales development machine around each individual product, the function now operated more like a shared service focused on building a cohesive story around the high-level business impact of the whole portfolio.

Conclusion

As soon as these innovations went into motion, a flywheel started, compounding on itself such that no competitor could really catch up.

Founders of great technology wanted to get acquired by PAN to satisfy their board after a prolonged stall. Remember, PAN pays a premium + growth re-accelerates after plugging into their distribution machine.

The reps with the deepest rolodex of CISOs wanted to work for PAN because they could confidently sell the best technology with incredible pricing power. CISOs were willing to pay a significant premium (and had the coffers to do so) by getting the best quickly without evaluating the market vs. getting competitive bids to increase negotiating leverage.

PAN was by no means the only large cybersecurity company with this strategy. The difference in market cap, however, is a direct result of Arora building the strategy ahead of the curve, and most importantly, his ability to execute.

During my senior year of college I stumbled across Marc Andreesen’s seminal piece Why Software is Eating the World.

And after reading and digesting his thoughts on where the world was going, I wanted nothing more than to break into tech.

In 2014, opportunities outside of Silicon Valley for non-computer engineers were scarce. But there was an emerging VC industry in Boston - with several firms focused on investing in growth at companies that had established product-market-fit.

Investing in growth meant investing in significant sales/marketing horsepower. This shift opened up entry-level opportunities at dozens of Boston-based technology companies for Sales Development Reps (“SDRs”).

I landed one.

My daily existence became cold-calling and cold-emailing (day-in and day-out) at significantly lower entry-level pay than my friends going into Commercial Real Estate and Investment Banking.

Over the next few years, however, these jobs went from being a relative afterthought to the most sought after entry-level position for recent college grads. Pay became comparable, and even more lucrative, than the jobs in finance and real estate. And it was also a lot more fun.

The Black Duck Experience

For four years, I’d hop in my car in South Boston and drive 35 minutes north to HQ in Burlington MA.

I’d enter the front door, turn right, grab a quick coffee in the kitchen and continuing walking into an expanse filled with long-tables. Over the next few hours the place would fill out with ~50 inside sales reps.

Music was playing through a central speaker system and SDRs (equipped with those weird looking headsets that have a mouthpiece) were building lists and hammering calls.

This was the stereotypical “bullpen” that characterized fast-growing VC-backed software companies in the 2010s. While our revenue grew 4x in just a few short years leading to an incredible exit ( >$500mm), I’d be hard-pressed to think that the P&L for the inside sales org was anything but red.

In some ways we got very lucky. As an earlier player in the cybersecurity SaaS market we were early adopters of platforms like Salesloft, Zoominfo and Drift, which allowed us to reach decision makers when phone and email channels weren’t so clogged.

Each team could predictably set up dozens of meetings per week through high-volume, generic value prop, outbound prospecting. Now, they’d be lucky to set up a couple meetings per month.

The sheer number of early-stage tech companies exploded during the “Golden Age of SaaS.” And the vast majority of these companies invested in, and abided by this playbook.

Between the saturation of email/phone channels + a non-favorable interest-rate environment over the past few years, best practices have changed as tech companies (and especially GTM orgs) need to run a lot more efficiently.

Put simply, in 2024, the economics on the old playbook no longer works.

Will this SDR role be retired?

Absolutely not.

But teams will be smaller, scrappier and need to prove profitable (not just meeting revenue targets) in order to sustain long-term.

Here is how SDR teams will evolve for the balance of the 2020s:

1. Reduction in Headcount: As AI advances, it will become more capable of handling repetitive and time-consuming tasks, such as data entry, lead qualification, and initial outreach. Automating this low-value work, will open up a significant amount of capacity on a per rep basis. Thus, fewer reps are required.

2. Shift in Focus: SDRs will spend less time on basic outreach and more time on high-value activities. This might include strategic account planning, personalized outreach, and relationship building. The role will shift from being task-oriented to more strategic and consultative.

3. Higher Skill Requirements: The role of an SDR will require a higher level of expertise in using advanced sales tools and technologies. Skills in data analysis, CRM software, and understanding complex sales processes will become a prerequisite for landing one of these coveted roles - not learned on the job.

4. Outsourcing: Off-shore talent, in the right markets, has proven to be as performant as their internal counterparts at a fraction of the cost. Finding a third-party firm operating in a low-cost market that can efficiently stand-up and manage a team to take on all of the high-volume outbound work, will be the norm. These agents will not replace SDRs, instead, they will augment a smaller, more strategic team of internal SDRs that will become future closers.

5. Integration with Other Roles: The distinction between SDRs and other roles, such as marketing and customer success, will blur. The combination of PLG + automation will make customer acquisition a lot more efficient. These roles will focus less on cold outreach to push people into the funnel and more about content creation and other marketing activities to pull people in, and customer enablement to help prospects after they’ve signed up for a trial. And/or, if they’ve already bought the solution, ensure they realize value to ensure retention and expand usage.

6. Continued Importance of Human Touch: Despite advancements in technology, the human element in sales remains crucial. High-touch interactions, building trust, and understanding complex customer needs cannot be automated. Therefore, while the number of SDRs might decrease, their role in the sales process will be far more focused on understanding challenges unique to specific companies while effectively communicating the right solution that properly addresses these problems. No more pitching.

Overall, the trend suggests a potential decrease in the number of traditional SDR roles, but those that remain will be more specialized, skilled, and integrated into broader sales and marketing strategies. These more highly skilled SDRs will become masters in leveraging technology to drive more strategic and personalized sales efforts.

RIP inside sales bullpens.

SaaS businesses, profitable or not, are acquired based on a multiple of Revenue.

Profitable service businesses are acquired based on a multiple of EBITDA.

Over the past decade or so, raw dollar amounts for SaaS acquisitions significantly outpaced their often much more profitable service counterparts.

Software companies grow faster with significantly greater operational efficiency - thus, their projected future cashflows will be exponentially higher.

Until 2023, the moat for SaaS companies was their ability to raise capital to hire an engineering team, build a product and test the market. If the company finds something that works they raise more and build a GTM organization to scale.

In essence, even if a bootstrapped services business solved the exact same problem as a SaaS product (perfect substitutes), the software is infinitely scalable once the product development infrastructure is built.

The Rise of AI

Over the next few years, however, the ability to build software will commoditize and the “VC moat” SaaS companies enjoyed will evaporate.

With an ability to prompt engineer code using sophisticated LLM models services companies can build software solutions without engineers or upfront capital.

For buyers this will be great - more competition will lead to more optionality at more favorable price points.

For suppliers this decreased barrier to entry means more competition and less opportunity for market capture. Greater operational discipline will be required to generate profits with an expected lower growth ceiling.

This will result in a really robust ecosystem of digitally-enabled small businesses, which will be awesome for the economy in general.

Acquisition multiples will compress.

The Value of IP

Seinfeld is one of the most popular TV shows of all time.

While Larry David and Jerry Seinfeld were both paid handsomely when the show was running in the 90s, they are billionaires because they own the IP.

The majority of that wealth was created after the season finale as networks (e.g. TBS) and streaming platforms (e.g. Netflix) paid - and continue to pay - top dollar for syndication rights. The best part? No additional operational cost/resources were required from Jerry and Larry to realize that exponential growth in value.

IP, like SaaS, is an infinitely scalable business with an incredible and defensible moat rooted in brand value.

Building the Founder Brand

As the world of SaaS and plug-and-play solutions become more and more commoditized, how can B2B companies leverage content to build a more scalable business?

1. Invest heavily in the founder brand very, very early. Even before a product is built (Adam Robinson did this with RB2B).

2. Use the founder as the lightning rod and distribute the thought leadership content to all platforms that the target buying audience lives (typically LinkedIn).

3. Stay consistent with content and volume, and reap the benefits of the inbound flywheel.

This GTM motion is no longer a secret. Many companies already realizing significant upticks in the revenue of their business as a result of this unique motion.

The Next Chapter

Over time this motion will see diminishing returns. After enough saturation, buyers will tune-out knowing that this is just another GTM motion.

Instead of shelving this motion and moving on to the next “innovative GTM thing” that will inevitably emerge, companies can focus on monetizing the content beyond its value as a pure marketing motion.

The free-market thrives on a group of like buyers needing a solution to their problem, regardless of what form the solution takes (e.g. services, software or IP).

What if the content can be repackaged as a series of assets (e.g. video training courses, in-depth newsletter, quarterly research findings, etc.) and sold at a nominal cost?

If the founder has developed an audience of raving fans, a healthy percentage of that audience may buy their software and/or services solutions - which would be the ideal outcome.

However, there may be a percentage of that audience where those solutions may not be a fit - but a lighter weight version of the value in content form can still help them overcome challenges.

As this motion matures we will see a proliferation of B2B content creators with business models that look and feel very similar to today’s current podcaster, TikToker, Youtuber, etc. monetization strategies.

The best part? Once this content is created and repackaged, it is infinitely scalable - similar to SaaS, and with potential recurring revenue models.

“Solopreneurs” like Justin Welch makes all of his income from courses simply repackaged from the content he creates.

Conclusion

A focus on developing content around thought leadership will help grow core businesses, while the value of that IP can also unlock a much more scalable business opportunity over the long-term.

Services companies will likely never trade on a multiple of revenue. But if they can layer in a scalable, profitable and interesting revenue stream that can significantly improve EBITDA they can drive a larger acquisition price.

When AWS launched in 2006 they came to market with a consumption based licensing model.

They ushered in the cloud era and had a first mover advantage with the pay-for-use model. It proved extremely lucrative.

In the early days of evaluating AWS technical teams did not have a full understanding of the amount of data they would need to store in the cloud. Only paying for what they used was a revenue model they could get on board with.

However, as companies produced exponentially more data runaway costs became significant.

AWS enjoyed explosive growth as a result - it’s market cap would likely be over $1T if it were a standalone company. And while customers see immense value in AWS, a whole market of solutions have exploded to help AWS users better manage their monthly compute bills.

In the immediate years that followed software vendors working with tech teams had a tough time selling consumption-based solutions.

Contracts based on headcount (i.e. per seat revenue models) became the de facto revenue model of the SaaS era. Companies could easily forecast headcount, and thus, forecast yearly spend with any given solution.

Industry Standard (2010-2022)

The trade-off for customers opting for per seat licensing models was a commitment to long-term (typically annual) contracts based on current user headcount. If that headcount grew, so did incremental revenue.

Both sides were fine with this.

Customer’s expected usage based on headcount growth was easy to forecast, calculate and budget.

Vendors were happy because the long-term contracts helped ensure they could accurately forecast revenue growth for quarterly board meetings.

And when the flood of money came roaring in during COVID, both sides were elated. More money meant more growth. And more growth meant more headcount. And more headcount meant more licenses.

A virtuous circle of sorts.

However, when interest rates rose and growth came to a halt, the proverbial music stopped.

The Great Leaning Out (2022 - Now)

While we’ve not seen a recession (yet), this slowdown hit tech hard.

A disproportionate amount of COVID money found its way to the tech sector leading to massive bets in highly speculative industries without regard for business fundamentals.

Once the tide went out markets began caring a lot more about profits, cashflow and three-letter acronyms like LTV, CAC, and NRR. Big visions and cool ideas in slide decks were worthless.

Headcount is always the first thing to address during these busts.

Companies implemented mass layoffs to reduces burn rates, increases cash and extend runway. The headcount reduction across the industry had a long-tail affect on the per license business model.

Reduced headcount reduced need for licenses. Once annual contracts expired customers needed to renegotiate terms, or in many cases, churn those products out of their operations completely.

This set in motion a vicious cycle of more layoffs as revenues dried up.

As a result, the next wave of emerging tools are moving away from the per license revenue model and moving towards a more manageable consumption based revenue model.

What’s changed since AWS?

First, in this current macro, cost-conscious companies are unwilling to commit to long-term annual contracts unless they’ve already realized significant value.

Second, PLG is now industry standard. If there is no try before you buy (or try cheap before you buy big) option there is no transaction, period.

End-users expect they can use the tool, easily implement it into their workflow and quickly determine if the productivity gains justify a buy decision. That trial better give them a really good understanding of their consumption needs.

These forces require solution providers to significantly reduce Sales & Marketing investment to gain market share. That capital is now allocated to building an extremely valuable product with gated functionality optimized for pricing.

While the revenue is less predictable, designing an operating model based on user-feedback loops increase product stickiness which unlocks incremental value, leading to organic and sustainable revenue expansion over the long-term.

Once this feedback loop is stabilized sales reps will be required to manage complex enterprise deals, but they will join much later in the company maturity life cycle.

In short, usage-based pricing is back with little-to-no trade-off from the customer. Vendors must counter this by minimizing S&M costs and overinvesting in building an extremely valuable product for the end-user.

It appears Google has abandoned their efforts to acquire Hubspot for Wiz.

It’s hard to believe that a major acquisition is not a distraction from Gemini’s early stumbles and recent headlines about employees too focused on social issues.

These two companies are wildly different - Cloud Security vs. B2B Martech - and paints a picture of a company not exactly sure of their long-term business strategy in the face of massive market shifts vis-à-vis AI.

Could this be a mid-life crisis?

In any case, this isn’t an article about whether Google will buy a Red Corvette or Porsche 911. Nor am I in a position to wax poetically on Google’s M&A strategy.

But it is fun to think about what the world of GTM would look like following a Hubspot acquisition.

It certainly would have put them in an enviable position to dominate the B2B market by building a closed-loop ecosystem.

Let’s take a look at a few of the components:

GSuite (Pre-top of Funnel)

In my opinion, Google Suite is the best workplace productivity solution of all time. Their solutions are easy-to-use and configure well into user workflows - propelling them well ahead of the bulky Microsoft Office Suite that is more prevalent in the enterprise.

While the product quality of Google Docs, Sheets, Slides, et al my be marginally inferior to their Microsoft counterparts in a vacuum, they are cloud-native and integrate with each other (and Gmail) seamlessly which significantly increases the efficiency of teams getting projects completed.

What’s more, because multiple people can work in one version of a project in real-time they’ve all but stamped out issues related to version control. Microsoft can do this with Sharepoint, but it’s not very good.

What would this mean for Hubspot and GTM teams?

For starters, integrations between GSuite and Hubspot’s CRM & Marketing solutions will allow content marketers to both efficiently increase the production of content marketing material AND distribute it through Hubspot to nurture pre-funnel leads, in-funnel prospects and customers.

Enhanced integrations will enable content and demand gen marketing teams to run leaner and generate significant productivity gains.

Google Paid Ads, SEO and Website Analytics (Top of Funnel)

Admittedly, I don’t have a wealth of knowledge on these solutions as an end-user.

However, their primary objective is to place company content in the Google Search ecosystem to increase brand awareness, measure impact, and ultimately reach in-market target buyers

For instance, if someone in Kansas Googles “I need an autobody shop in my area” the supplier who meets the company/location criteria AND pays Google the most will be at the top of the search list.

The buyer will then visit the website and will either make an appointment, or if not, banner ads of that autobody shop will pop up wherever else they decide to go on the internet.

If they visit the website enough, the autobody shop may be able to capture their information and proactively send them outreach to further increase their odds of moving into the funnel.

These Google Solutions are already rich with APIs that allow for some level of automation to port them into their CRM system.

If Google owned Hubspot, however, significant investments will be made into providing more out-of-the-box, automated workflows for nurturing prospects and customers alike to increase customer acquisition, repeat buyer and retention rates.

The Virtuous Circle (HubSpot as the True Hub)

The real value is in being able to manage all of these workflows and automations in one single pane of glass without the need for a tech expert to implement and manage it.

A lean marketing team can:

1. Ideate in Google Docs, Slides and Sheets

2. Refine those ideas into marketing assets with Hubspot design templates

3. Push those marketing assets out into the Google Search ecosystem to drive pre-funnel lead generation AND to the prospects and customers that exist into the CRM.

4. All of this can be measured and analyzed within a combination of Google/Hubspot analytical machine to maximize ROI on marketing spend.

If marketers can do this in one system, with greater plug-and-play functionality, a team of 50 marketers could be reduced to a team of 5, with more targeted, strategic output based on data-driven insights.

Hubspot will also have the benefit of leveraging the best engineers in the world who can not only enhance the existing products set and build tighter integrations with easier to implement automated workflows, but can also layer the power of Gemini to turbocharge actionable insights and provide recommendations on what to do next in order to achieve significant revenue growth.

If Google was wondering how to more effectively displace Microsoft Office (especially as OpenAI becomes a more strategic component of those solutions) at the enterprise level, this acquisition would be a game-changer.

Microsoft Dynamics (their CRM) is a joke, and there is no real relationship with Salesforce. A Google + Hubspot solution would be well positioned to dominate this market.

In the day and age of tool overload, the companies that win will be the companies that ensure customers use as few tools as possible to achieve their business goals.

Clay raised $62M at a $500M valuation. The second shoe to drop following Apollo crossing unicorn status in August.

The sales platforms powering the “Golden Age of SaaS” are now under even more pressure to change their business models to align with macro shifts.

Gong, Outreach, Clari, etc. all sit above $1B valuations in the private markets. Their value is tied to improving sales productivity for large and expensive sales teams through mechanisms that no longer accelerate pipeline/revenue growth.

Currently they rely on locking customers into massively expensive annual contracts, based on a per-seat licensing model.

Growth has surely slowed to a crawl, and churn has skyrocketed as GTM organizations operate with significantly greater efficiency.

Emerging platforms like Clay are more flexible and designed to drive value based on usage, not seat count.

As the new generation of platforms gain greater adoption, improve their capabilities and become the new standard, the “old guard” will be lucky to sell to PE at their current valuations.

Clay is great. I’ve implemented into my workflow and it’s saved me upwards of 6 hours/week and is more effective in driving meeting conversion. However, they still need to build out more functionality for me to feel comfortable fully replacing my tech stack that includes Outreach.

With this new round, here are some thoughts on where they should and shouldn’t invest (this is just me spitballing so take it for what it’s worth):

Sales Execution

Clay can do everything up to the point of outreach, which makes a sales execution platform like Salesloft, Outreach or Apollo.io still necessary to actually reach out to the folks you’ve prospected.

Given how mature the space is, it could be in their interest to build this feature set into the existing platform and compete directly with the incumbents.

However, the cost/benefit analysis may not make sense if it pulls them too far away from their core.

A better bet may be forming a very, very tight integration with Apollo in an effort to stamp out Salesloft and Outreach.

If they can accomplish this with an economic relationship (a back-and-forth pay for premium) and a UX that allows customers to leverage each solution’s capabilities from one platform, that combination will be the standard GTM tech stack for sales teams moving forward.

A monster merger in the next few years is fun to think about too.

Integrations/Workflows/Partnerships

See Apollo.io suggestion above.

They have already done a phenomenal job of integrating with dozens of sales data and sales execution tool in the sales tech ecosystem. This gives them a unique competitive advantage in data accuracy AND introduces insane customization into their offering. Double down here.

Maybe create a marketplace where buyers can buy the rest of their tool set through the Clay platform and take a fee from the platform providers? Even better, offer more robust features from their partners, inside the Clay platform so that reps don’t have to operate anywhere else.

As it stands today, the per user pricing model is pretty cheap. Which is great as they enter hypergrowth mode at incredibly low CAC (they don’t have a sales team). However, I can’t imagine their top-line is overly impressive.

If they can become the one platform every sales rep uses, addressing all of their needs, and can monetize it effectively, they will easily become the winner in the space.

AI Email Copywriting

No one has put out a good product in this category. Clay has a distinct advantage because of all the datasets they are leveraging to power the rest of their functionality.

If they can develop great copy, that looks and sounds authentic, and scrapes information from far more than just a prospect’s LinkedIn profile (e.g. incorporate highlights from a 10-K) there is opportunity to win here as well.

Performance-Based Pricing

They do not lock you into annual contracts. You pay a low-price entry fee, month-to-month which provides users a certain amount of credits. You can pay for more credits if you are realizing value, but if not, you can cancel anytime.

Ensuring that revenue is tied to value, and building their operational infrastructure around this, is a win-win for customers and the business. If they can add additional revenue streams at extremely high-margins through their existing technology partnerships, this can be a major driver for long-term growth.

No Sales People

They have a great PLG and word-of-mouth machine that has led to explosive growth. Maintain this until you really need high-end sales people to drive massive enterprise deals with a lot of complexity.

Customer Success

One knock against them is that it is really hard to get up and running.

Their value proposition lies in their ability to build very customized, and accurate target company and prospect lists based on your unique business objectives. This significantly increases your ability to reach the right people with the right message, quickly.

The downside is that it is still really difficult to customize to your specs when you’re first starting out and there is very little in the general FAQs to help.

Additionally, it’s hard to get a CSM if you are only paying ~$80/month - nor is it worth it for them to build out a super large team, as most of the adoption issues are pretty easy hurdles for users to overcome.

Building a robust library where you can easily query and access very customized solutions would go a long way in initial customer adoption, ensuring users realize value quickly and then incentivizing them to do more. It is also infinitely scalable and requires minimal overhead.

This will ultimately maximize LTV.

Conclusion

When companies were incentivized to “grow at all costs,” LTV:CAC was an elusive metric.

If Clay can remain lean, invest heavily in product, partnerships and high-quality implementation/CS (without having to spend significant $$ on sales/marketing) they have a unique opportunity to achieve significant profitability and be the single platform to rule them all in the age of profitable, efficient growth.