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This is one of those updates I immediately file under actually useful.

n8n’s new instance-level MCP makes it stupid simple to connect my n8n instance to AI tools so they can search, view, and run workflows without me doing weird glue work or pasting giant chunks of JSON into prompts.

That matters because most of us already have workflows doing real work.

Form triggers. Webhooks. Schedules. Chat triggers.
Now those can become usable tools inside the AI products I’m already in.

What I like most is the setup is clean.

You enable MCP at the instance level, then choose which workflows to expose. That’s it. No rebuilding the workflow just to make it “AI-ready.” No extra abstraction layer. No giant implementation project.

That’s the kind of product decision I love: use what you already built.

A few things clicked for me right away:

1. Existing workflows become agent actions
If I already have a workflow that searches contacts, creates records, drafts responses, logs bugs, or updates a system, an AI tool can call it directly.

That means my automations stop being trapped inside n8n and start acting like reusable capabilities across Claude, ChatGPT, Mistral, Lovable, and anywhere else MCP is supported.

2. The AI gets workflow context
This is the big one.

Instead of me explaining every parameter, endpoint, field, and edge case, the tool can inspect the workflow and understand what’s available. That’s a huge jump from “here’s an API doc, good luck” to “just use the thing I already made.”

For builders, that means less documentation theater and faster shipping.

3. Frontends get easier
The Lovable example is exactly where my brain went.

If a frontend builder can read the workflows I already made, it can infer a ton of product logic without me handholding it. That’s a very real shortcut for turning backend automation into usable interfaces.

I’ve said this for a while: the best automations don’t just save steps, they become products.
This moves n8n closer to that.

The debugging angle is interesting too.

An AI hitting a broken workflow, seeing the failure, and pointing me toward a fix? That’s not the headline feature, but it’s absolutely the kind of accidental superpower that ends up being valuable in practice.

I also like that this isn’t locked behind some premium-only setup. It’s available across cloud, self-hosted, enterprise, and even community edition, as long as you’re on a recent enough version. That’s the right move.

And the “Connect with n8n” button showing up natively in tools like Lovable, Claude, and Mistral is smart. But the real win is I don’t have to wait for every app to add the pretty button. If it supports MCP, I can wire it in now.

That’s the pattern I’m betting on:

Build the workflow once.
Expose it selectively.
Let AI tools use it as infrastructure.

No extra ceremony.

For anyone building internal tools, AI copilots, client portals, support ops, CRM helpers, bug trackers, or lightweight SaaS on top of automations, this unlocks a much cleaner path from workflow to interface.

I’m especially into the fact that it works with the workflows people already have.
That’s elegant.

Not “here’s a whole new framework.”
Just: turn on access and go.

I think a lot of people are going to realize they’ve already built the backend for an AI tool without noticing it.

That’s a fun place to be.

I’ve been in this business long enough to watch the cash flow vs. appreciation debate come around every cycle like it’s brand new.

It isn’t.

By 2027, I think the investors who do best won’t be the ones chasing whichever side is getting the most attention online. They’ll be the ones who actually understand what they’re trying to build.

If you want income now, then cash flow matters more.
If you want long-term wealth and can afford to wait, appreciation matters more.
And if you buy well, manage well, and hold long enough, you usually want some of both.

That’s the part people skip over. They want a universal answer when real estate has always been local, personal, and very timing-dependent.

I’ve owned properties that looked great on paper because the cash flow was strong, but the day-to-day headaches were heavier than expected. Lower-end price points can get you in the game, but they can also come with more management, more deferred maintenance, more turnover, and less room to force rents upward no matter how much money you put into the place.

I’ve also seen the other side: better areas, stronger tenant pool, less drama, tighter margins up front—but better staying power over time.

That’s why I’m skeptical whenever someone says one strategy is the strategy for the next few years.

There is no magic lane in 2027.
There’s only the lane that fits your goals, reserves, skill set, and tolerance for friction.

For newer investors, especially house hackers, I think there’s one more layer people forget: you’re not just buying an investment, you’re buying your living situation. If the cheaper market gives you better numbers but worse quality of life, longer commute, or a place you don’t actually want to live, that cost is real too. Not every sacrifice pays off the way people think it will.

What I’d be asking in 2027 is pretty simple:

• Do I need this property to support my lifestyle now?
• Am I in a financial position to wait for equity growth?
• What kind of tenant, neighborhood, and maintenance profile am I signing up for?
• If the market goes sideways for a couple years, does this still work?

That last question matters more than people want to admit.

I’ve lived through two downturns. Appreciation is great—until it pauses. Cash flow is great—until repairs, vacancy, insurance, taxes, and bad underwriting eat it alive. That’s why I don’t like oversimplified debates. In the real world, the winner is usually the property that gives you enough margin for error.

Another point I agreed with: a lot of investors don’t have an education problem, they have a decision problem. They’ve listened to every podcast, watched every clip, read every thread, and still haven’t chosen a lane. At some point, more information stops helping.

You don’t need to master every strategy before starting. You need to pick one that matches your life and go get reps.

If you’re high income and short on time, your answer may be very different from someone who’s handy, flexible, and willing to take on a heavier lift. If you care deeply about a niche—whether that’s house hacking, value-add, or a specialized housing model—that can work too, but only if you’re honest about the operational load. Some of these strategies are real estate plays. Some are operating businesses wearing real estate clothes.

That distinction matters.

My own view for 2027:
Cash flow keeps you in the game. Appreciation builds a lot of wealth. If I had to lean one direction for a first deal, I still want something with enough cash flow or payment support to survive mistakes, surprises, and softer market conditions. I never like being forced to depend on future appreciation to bail out a mediocre buy.

That’s not fear talking. That’s experience.

The best investors I know aren’t asking, “Which one matters most?”
They’re asking, “What am I optimizing for, and what can I realistically hold through a bad stretch?”

That’s usually where the right answer shows up.

Curious where you land on it: in 2027, would you rather buy for stronger monthly cash flow or better long-term appreciation?

I’ve said this for years: early sales is not a delegation problem. It’s a founder problem.

Too many founders want to “hire a salesperson” before they’ve earned the right to scale anything. That’s backwards.

In the early days, the founder is the product.

Not because the product doesn’t matter. Because the market is still deciding whether your insight matters at all. And no salesperson can carry that burden for you. They didn’t live the problem. They didn’t form the thesis. They won’t catch the subtle moments in a conversation where the real wedge appears.

And that’s the job.

As Drucker would remind us, the purpose of the business is to create a customer. Early on, that means learning why someone would buy, not forcing a pipeline report that looks respectable to investors.

A few things stood out to me.

First, most founders are taking late-stage sales advice and applying it to a company that still hasn’t found its footing. That’s like putting enterprise process on top of unresolved uncertainty. It looks sophisticated. It kills learning.

If you’re pre-repeatability, your sales process is not mainly about efficiency. It’s about discovery.

That means your first calls should sound less like a polished pitch and more like:

Here’s the problem I think matters. Here’s why I think it matters now. Here’s what I’m still learning. Tell me how this shows up on your side.

That level of candor is not weakness. It’s signal.

The second point: better is a terrible pitch.

“Better” is vague. “Better” invites debate. “Better” usually loses to inertia.

What gets attention is a perspective that feels different, specific, and a little uncomfortable. Something that makes the buyer pause and think, I haven’t heard it framed that way.

That’s what strong founder outreach does. Not a feature dump. Not fake personalization. Not “noticed you recently posted…”

Just relevance, a sharp point of view, and a real problem.

Short enough to read on a phone. Clear enough to earn a reply.

Third, I liked the discipline around lead generation: before touching tools, can you manually identify 30 people you genuinely want to learn from and write each of them a serious note?

That’s operator behavior.

Most teams jump straight to automation because it feels productive. It isn’t. It’s usually just faster confusion.

If you can’t manually identify the right people, the right pain, and the right message, adding tooling only helps you fail at scale.

This is where Lencioni’s point about clarity matters. When there’s no clarity around the problem, the buyer, or the message, you don’t have a sales execution issue. You have a leadership issue.

And then there’s the part too many founders resist:

Founder-led sales is not about maximizing revenue on day one. It’s about maximizing learning velocity.

That doesn’t mean revenue is irrelevant. It means revenue without learning can fool you.

A few early deals can mask bad positioning. Friendly buyers can create false confidence. Services-heavy contracts can make you think you have product pull when you really have founder hustle.

That’s why I’m always telling operators to watch the right signals:

• Are the right people responding?
• Are they describing the problem in their own words?
• Does urgency exist before you show the product?
• Does momentum survive procurement, internal alignment, and budget scrutiny?
• Are you winning because the problem is real, or because you’re persuasive?

Those are different businesses.

The biggest takeaway for me is simple:

Sales early on is research disguised as revenue.

If you treat it that way, you learn faster. If you learn faster, you position better. If you position better, you eventually earn repeatability.

And only then should you start trying to scale the motion.

A lot of founders want to skip to the part where they hire an AE and “focus on product.”

I get the impulse. I’ve built and exited companies. I know how tempting it is to stay in your comfort zone.

But if you’re the founder, your job is not comfort. Your job is contact with reality.

And in the early stage, reality lives in the sales conversation.

One of the biggest misconceptions in audio is that buying an SM7B automatically gets you that polished, radio-ready vocal.

It doesn’t.

I’ve worked around enough spoken-word and podcast recordings to know this mic earns its reputation after the chain is dialed in, not before. Out of the box, the SM7B/SM7dB can absolutely sound dull, hissy, wooly, or oddly lifeless if the gain staging and processing aren’t doing their job.

What stood out to me most is the honest point about the noise floor.

A lot of people assume if they hear hiss, something is broken. Usually it’s not. Usually it’s the reality of the mic, the interface, the preamp being pushed too hard, or all three stacking up. That’s a very normal behind-the-scenes problem, especially for creators building a “pro” setup on a budget interface.

The practical fix isn’t exotic. It’s restraint.

A light noise suppressor can clean up the hiss without destroying the top end, but only if you don’t overdo it. That’s the part newer creators miss. The second you get aggressive, you trade noise for that muffled, underwater sound that screams overprocessing. I’m a perfectionist about clean dialogue, but I’d still rather hear a tiny bit of natural air than a vocal that’s been sanded down into nothing.

The other thing people underestimate is the SM7B’s frequency personality. It’s not neutral in the way many people think. It has that familiar low-mid density and presence-region push that helps it cut, which is great for speech and certain vocals, but it can also get congested fast once compression starts stacking.

That’s why I liked the focus on dynamic EQ instead of brute-force static cuts.

That’s a more realistic way to mix a mic like this. You’re not trying to carve the life out of it. You’re just controlling the parts that jump forward when they become a problem. In practice, that usually means low-mid buildup, some boxiness, and the 2k–4k area getting a little sharp or tiring.

Then there’s compression.

I’m very much in the camp of serial compression over one plugin doing all the heavy lifting. In production, that’s usually what gets you a vocal that feels controlled but still human. One compressor catching peaks, another helping with density, and suddenly the voice sits where it should without sounding clamped to death.

That’s especially true for podcast and voice-heavy content. If your compressor is doing too much in one stage, you hear it. And once you hear it, your listener definitely feels it, even if they can’t name the problem.

I also appreciated the reminder that this mic often needs additive EQ, not just cleanup.

A lot of people buy darker mics, hear that smoothness, and then mistake “dark” for “finished.” Those are not the same thing. Sometimes the vocal needs a little top-end sheen to feel expensive, intimate, and present. The trick is that once you add that brightness, you’ve also invited sibilance to the party, so the de-esser has to follow behind and keep things civilized.

That’s just real mixing: boost a little, control a little, listen again.

And finally, I’m always glad to see people say the quiet part out loud: limiter last. Not because it’s glamorous, but because it’s practical. If the goal is a competitive, stable vocal level without clipping, that last stage matters. It’s one of those moves listeners never notice when you do it right, and absolutely notice when you don’t.

My main takeaway is simple:

The SM7B isn’t a magic mic. It’s a workflow mic.

If you understand the hiss, control the low mids, shape the presence, compress in stages, manage sibilance, and finish with intention, it can sound fantastic. If you don’t, it can sound like you spent $400 to record through a blanket.

That’s not a knock on the mic. It’s just the truth about audio:
great sound usually comes from the chain, not the logo on the microphone.

One of the most common things I hear from professionals is:
"I want to update my LinkedIn, but I don’t want my boss seeing activity and connecting the dots."

Fair concern.

A lot of people delay important profile updates because they assume every edit will trigger a notification to their network. In reality, LinkedIn gives you control over that.

If you want to refresh your profile a little more quietly, go to:

Me → Settings & Privacy → Visibility → Visibility of your LinkedIn activity

From there, look for Share profile updates with your network and switch it to No.

That one setting matters more than people realize.

It gives you room to:

• update your headline
• revise your About section
• add a new role
• include a certification or degree
• tighten your keywords for recruiter searches

...without making it feel like a public announcement.

As a former recruiter, I’ll say this clearly: keeping your profile current is not suspicious — it’s smart. The bigger risk is leaving your profile outdated because you’re worried about visibility.

If you’re planning to make changes, I’d also recommend taking a few extra minutes to review the rest of your visibility settings. LinkedIn has several controls that affect what people can see, what gets shared, and how active you appear.

A strong profile helps with ATS alignment, LinkedIn Recruiter search results, and shortlist decisions. You should be able to improve it without unnecessary noise.

Sometimes the best career move is simply making updates strategically — and privately.

you have 20k followers. you publish something valuable. you share your experience. you’re making content that matters and could help someone. and then...13 likes.

relatable?🥲

btw, those 20k people didn’t click follow by acciden. at some point, they looked at your page and chose to be here!

so why are you ghost on their feed now? stop blaming the quality of your content here.

the feed is overcrowded now. the algorithm is constantly changing the rules. your followers want to hear from you, but the platform just won't let them.

the connection is broken. and here's how we fix it!

instead of focusing on getting more followers, you should also think about how to build stronger connections with the audience you already have.

this is why community wins.

---

at Sociala, we're building an infrastructure that helps experts bring their audience into one dedicated space and create more ways to share and monetize their knowledge.

you don't need to stop using LinkedIn or any other social platforms. they're powerful tools for discovery and reaching new people.

but stop renting your audience from algorithms. build a dedicated space where your dive into your world, buy your offers and stay connected for real.

anyone can buy or hack a follower count. but you can't fake a real connection!

i'm always happy to connect with experts who want to explore new ways to grow their communities and monetize their expertise 😊

I spend a lot of time with leadership teams that say they’ve “done a tabletop,” but when I ask a few basic follow-ups, it turns out they mostly walked through a slide deck and called it readiness.

That’s not readiness. That’s theater.

What I liked here is the emphasis on using a simple game structure to make people think operationally. Not in an abstract “what does the policy say?” way, but in the much more useful way:

What would we actually do first?
Who would we call?
What data do we have?
What can we prove?
What would fail because of people, process, or technology?

That’s where tabletops become valuable.

A lot of security teams don’t need more awareness. They need more reps. They need a low-friction way to practice decision-making under uncertainty, especially when the facts are incomplete and the clock is moving.

That’s why I think this kind of format works. It forces a team to deal with the reality I see every week in live incidents:

• people make assumptions too early
• logs are incomplete
• ownership is fuzzy
• someone thought a control was in place, but it isn’t
• everyone has a different mental model of the same attack path

Those are the things that matter.

I also liked the distinction between seeking clarity and doing analysis. That’s a subtle point, but it matters. Good responders know the difference between asking a basic scoping question and burning time on a deep technical path before they’ve even validated the premise.

In practice, a lot of teams struggle because they jump straight into tooling. They want to hunt, isolate, pull logs, review EDR, block traffic — all before they’ve aligned on what they actually know. When I run exercises with clients, that premature pivot is one of the most common failure points.

Another thing I appreciated: the exercise kept surfacing a question I wish more teams asked at the end of every incident simulation:

Is this plausible?

That question protects you from two bad outcomes.

First, it keeps the exercise grounded in real attacker behavior instead of fantasy breach scenarios designed just to be dramatic.

Second, it prevents teams from overfitting to their tools. I’ve seen too many organizations assume a control would catch something because the vendor says it should, not because the team has validated it in their own environment.

Those are very different things.

The other practical takeaway is one I use constantly in fractional CISO work: if an exercise uncovers 15 gaps, do not leave with a 15-item action plan.

That usually kills momentum.

Pick one meaningful fix. Maybe it’s a logging gap. Maybe it’s an undocumented escalation path. Maybe it’s the fact that nobody knows who owns a third-party connection. Fix one thing well, then run the next rep.

That’s how capability matures in the real world.

I also think this approach is useful beyond the IR team. The best tabletops I’ve seen include the people who are usually adjacent to the incident but still materially affect the outcome — IT, legal, comms, facilities, execs, and the business owner for whatever system is involved. Real incidents are messy, and containment decisions rarely stay inside the SOC for long.

If I were advising a client on using this style of exercise, I’d keep it simple:

Use a plausible scenario, force discussion, document assumptions, and pay close attention to where the team hesitates.

That hesitation is often the real finding.

Not the malware.
Not the card.
Not the scenario twist.

The real finding is usually the pause before someone says, “I’m not actually sure we can do that.”

That’s useful. That’s actionable. And that’s exactly why these exercises are worth doing.

I’d take one honest, slightly uncomfortable tabletop over five polished compliance exercises every time.

I’ve spent enough years around engineering orgs to know that every wave of change brings the same executive question in new clothes:

“What’s the one metric?”

In the AI era, it shows up as lines of code, PR throughput, commit volume, or some variation of “are we going faster yet?” And just like before, the tempting answer is the wrong one.

What stood out to me is that AI doesn’t reduce the importance of developer experience. It raises the stakes.

If you’re rolling out AI across the SDLC, you’re not just adding a tool. You’re changing how developers think, review, validate, and trust what lands in production. The work shifts from primarily writing code to a more complex loop of prompting, evaluating, accepting, editing, and verifying. That’s a workflow change, a cognitive change, and a systems change.

So if I’m leading engineering today, I’m not asking, “Did we buy AI tools?”

I’m asking:

Where is friction actually being removed?
Where is cognitive load increasing?
What part of the workflow improved, and what part just got noisier?
Can developers trust the output enough to move faster without creating downstream drag?

That’s why I continue to believe developer experience is one of the most strategic lenses a leadership team can use right now.

A few ideas here really resonate with me.

First, measurement still needs to be multi-dimensional. The industry keeps trying to compress productivity into a single number because single numbers are easy to put on slides. But software delivery has never worked that way, and AI makes that even more obvious.

A useful model still has to look across multiple dimensions: satisfaction, performance, activity, collaboration, and flow. Not because frameworks are fashionable, but because any one metric in isolation gets gamed, misunderstood, or both.

AI-generated code makes this painfully clear. If a machine can produce hundreds of lines in seconds, then code volume becomes even less meaningful than it already was. The question isn’t whether more code exists. The question is whether better outcomes happen with less friction.

Second, surveys matter more than a lot of technical leaders want to admit.

I know some people still flinch at anything subjective. But if you care about end-to-end workflow friction, tool handoff pain, trust in generated output, or whether engineers feel interrupted versus in flow, system telemetry alone won’t save you.

I’ve seen this firsthand for years: the hard problems in engineering productivity are rarely visible from one dashboard. You need both forms of evidence:

what the systems say and what the developers say.

That combination is the closest thing we have to a complete picture.

Third, you have to translate DevEx into business language.

This is where many well-intentioned engineering leaders lose the room. Executives do not wake up caring about cycle time because cycle time is beautiful. They care about market speed, capital efficiency, capacity, risk, and whether the organization can absorb change without collapsing into chaos.

So if I’m trying to get support for a DevEx initiative, I’m not stopping at “developers are frustrated” or “builds are slow.”

I’m connecting the dots to:

• recovered engineering capacity
• reduced cloud spend
• faster path from idea to customer value
• lower delivery risk
• better ROI on AI investments

That’s the story. The metrics support it, but they are not the story by themselves.

Another point I strongly agree with: metrics should be treated like a product.

That’s a subtle idea, but an important one.

Too many organizations build metric systems the way they build internal admin tooling: dump a bunch of numbers into dashboards and assume the job is done. But if metrics are a product, then they need customers, use cases, adoption, iteration, and eventually deprecation.

A metric that doesn’t answer a decision is just decoration.

A dashboard without context is usually a blame surface.

And a productivity metric handed to managers without training is often an incentive bug waiting to happen.

The other strategic takeaway for me is around prioritization.

Once you actually start measuring friction, you uncover more problems than you can possibly fix. That’s normal. The real challenge isn’t finding pain; it’s deciding what deserves coordinated investment.

This is where product thinking helps again. Reach, impact, confidence, and effort are far more useful than chasing whatever complaint is loudest or whatever bottleneck feels emotionally important this quarter.

I’ve made this mistake before, and I’ve watched plenty of teams make it too: they go after the biggest visible problem without accounting for organizational feasibility. Six months later, they realize they picked a fight they couldn’t win.

In practice, the best DevEx programs balance two truths:

Some problems are local and should be fixed by teams closest to the work.
Some problems are systemic and need top-down sponsorship to move at all.

That distinction matters a lot. You can absolutely get momentum from grassroots improvements, but the big structural wins usually require executive air cover.

And that brings me to the part I think matters most right now.

AI rollout is not mainly a tooling exercise. It’s a change-management exercise wrapped in a productivity narrative.

If leadership wants ROI from AI, they need more than licenses and announcements. They need a disciplined way to identify friction, measure outcomes, build trust, and communicate value across different audiences.

That’s developer experience work.

Not as a side project. Not as developer happiness theater. As core operational strategy.

My view is simple: in the AI era, the companies that win won’t be the ones that generate the most code.

They’ll be the ones that create the most trustworthy, low-friction, high-leverage environment for developers to turn ideas into outcomes.

That’s what DevEx is really about. And if anything, it just became more important than ever.

I’ll say it plainly: the MQL is dead as a primary metric in B2B SaaS.

And honestly, it’s been dead for a while. We’ve just had a lot of teams continuing to report on a 2014 concept while wondering why pipeline quality keeps getting worse.

The core issue is simple. MQLs measure activity better than they measure revenue.

A whitepaper download is not intent.
A generic demo request is not always urgency.
A single contact filling out a form is definitely not the same thing as an account moving toward a buying decision.

That mismatch gets expensive fast.

I’ve seen too many teams celebrate “500 MQLs this month” while sales is drowning in low-intent follow-up and pipeline stays flat. If your MQL-to-closed-won rate is sitting anywhere near the current median of 0.9%, the metric is not helping you run the business. It’s hiding the problem.

What broke the MQL model is buyer behavior.

Buyers now do the majority of research before talking to sales. Committees are the norm, not the exception. The median buying committee is around 6 people, and most decisions involve 4+ stakeholders. On top of that, the journey is fragmented across dozens of visits, dark social, review sites, product touches, and third-party intent signals.

That means the old assumption of one person, one form fill, one linear funnel is gone.

Form fills no longer equal buying intent.
In many categories, they’re just one weak signal inside a much bigger account-level motion.

What replaces MQL is not one metric. It’s a better operating system.

For PLG motions, I’m bullish on PQLs. Product usage is simply a stronger signal than content consumption. Activation, feature adoption, seat expansion, repeat usage, admin invites — these behaviors tell you much more about purchase likelihood than someone downloading a guide. If you’re product-led and still optimizing around MQL volume, you’re probably ignoring your best intent data.

For ABM, the better model is Qualified Accounts. Score the account, not the individual. The best teams are combining CRM data, first-party engagement, third-party intent from platforms like 6sense/Bombora/G2, and velocity signals to understand whether an account is actually moving. That’s much closer to reality than pretending one junior manager who clicked an ad is “qualified.”

For sales alignment, I think SQO is where a lot of teams should land. Not “did marketing generate a lead?” but “did marketing help create a legitimate sales opportunity?” That shift forces marketing and sales to agree on what counts. It also cuts an enormous amount of SDR waste.

And the most accurate model, in my view, is Buying Group Score.

Because that’s how companies buy now: in groups. If four people from the same account are engaging, intent is spiking, product interest is deepening, and the account matches ICP, that signal is dramatically stronger than any one contact becoming an MQL. This is one of the biggest reporting upgrades SaaS teams can make over the next 12 months.

The board-level implication matters too.

I would much rather see a CMO report:

• sourced pipeline
• pipeline coverage
• SQOs created
• conversion velocity
• CAC payback

…than lead volume.

Investors don’t care that you generated 2,000 MQLs if revenue didn’t follow. In this market, especially with tighter multiples and a lot more scrutiny on efficiency, pipeline quality and payback matter more than top-of-funnel inflation.

That’s why I think the best dashboards in 2026 will be built around three lenses:

Quality: SQO rate, PQL quality, Qualified Account conversion
Speed: pipeline velocity by segment and source
Efficiency: CAC payback, cost per SQO, sourced pipeline per dollar spent

That’s the shift.

Not “marketing generated interest.”

But: did marketing generate revenue-producing opportunities, how fast, and at what cost?

One other point I strongly agree with: SDRs should increasingly work accounts, not random lead queues. If your reps are each working 200+ leads with no committee visibility, you’re not running a modern B2B SaaS motion. The stronger model is fewer accounts, deeper context, and coordinated outreach around actual buying group activity.

Also, we should stop pretending form gates are harmless. In a lot of SaaS categories, they suppress demand capture more than they improve qualification. If removing friction increases engaged account volume and you have the infrastructure to identify and score intent properly, that’s usually a trade worth making.

My takeaway is pretty straightforward:

MQL can still exist as a diagnostic funnel data point. It should not be the KPI that runs the go-to-market motion.

If I were resetting a B2B SaaS dashboard today, I’d move from lead-centric reporting to:

• PQL for PLG
• Qualified Account for ABM
• SQO for sales alignment
• Buying Group Score for forecasting accuracy
• sourced pipeline, velocity, and CAC payback for exec/board reporting

That’s a much better reflection of how buyers actually buy now.

And candidly, the companies that make this transition faster are going to have a major advantage. Better sales focus. Better ad optimization. Better handoff quality. Better board conversations. Better capital efficiency.

Fewer “leads.” More pipeline.

That’s the game.

One of the biggest problems with RED-S education is that so much of it has historically been framed around women’s bodies. And while menstrual changes can be a very clear clinical clue in female athletes, that has unfortunately left a lot of men feeling like they have no obvious marker to watch for.

I want to say this clearly: men can absolutely experience RED-S, and they are not “the exception.”

At its core, RED-S comes from low energy availability—not taking in enough energy to support both training and basic physiological function. The body adapts by downregulating systems that aren’t essential for immediate survival. That can affect performance, mood, recovery, bone health, digestion, and reproductive health regardless of sex.

For men, one of the most important areas to pay attention to is sexual and reproductive health.

That may look like a noticeable drop in morning erections, a decrease in libido, or concerns around fertility, since sperm count may be reduced in a low energy availability state. These signs are often under-discussed, but they matter. They are not random, and they are not something to brush off as just stress or aging without looking at the bigger picture.

Beyond that, many of the “classic” RED-S symptoms apply across the board.

Things like feeling unusually cold, becoming more irritable, having sleep disruption, dealing with GI issues, struggling to tolerate fuel during training, or repeatedly running into bone stress injuries can all be part of the picture. None of these symptoms alone confirms RED-S, but when they cluster together—especially in an athlete under-fueling relative to their training load—I take that seriously.

What I appreciate here is the reminder that we need to expand the conversation, not force men into a model that was never fully built with them in mind. The physiology of energy deficiency does not care about gendered assumptions. Bodies will compensate when they are under-fueled.

And this is where I always come back to nuance: this is not about eating “perfectly,” and it’s also not about glorifying under-fueling in the name of discipline, leanness, or performance. I’m not interested in diet culture dressed up as sport science, and I’m also not interested in pretending nutrition doesn’t matter. Adequate fueling is a health issue, a performance issue, and for many athletes, a relationship-with-food issue too.

If you’re male and this sounds familiar, please know you are not the only one, and you are not making it up. RED-S in men deserves recognition, screening, and support.

Sometimes the first step is simply asking:
Am I eating enough for the life and training I’m asking my body to sustain?

That question can change a lot.