Written By: Chris Allaire
Right now, everyone has access to the same information.
The same tools.
The same AI assistants.
The same tutorials.
However, that knowledge doesn’t equalize ability.
So the question isn’t:
“Can you figure it out?”
Instead, the question is:
“Can you see it immediately?”
Because when something matters — revenue, security, uptime, people, risk — nobody wants a well-intentioned generalist experimenting on their problem.
They want the person who’s seen this exact failure pattern a hundred times before.
That’s exactly why specialization in 2026 is becoming one of the most valuable advantages in the market.
Are you handing over your tax and financial documents to a mechanic?
If a pipe bursts, would you call an electrician?
Since when would you turn something critical over to a “generalist”? Come to think of it, what is a “Generalist” in the first place? When you study the best in breed and the people at the top of the pyramid, they’re not generalists. They’re EXPERTS.
In life, we know this, but why does this somehow get ignored in business?
Could they just Google it? Don’t they have ChatGPT?
Of course!
But because experience matters more than information.
The plumber doesn’t need to “look it up.”
They walk in, glance at the pipe, and already know where the problem is.
As a home owner, how does that make you feel?
Assured.
Confident.
At ease.
A great plumber doesn’t stop learning.
Rather, they just learn only what makes them a better plumber.
They don’t wake up thinking:
“Maybe I should add electrical work.”
Instead,they think:
“How do I diagnose faster? Fix cleaner? Prevent the next failure?”
That’s the difference.
Specialists don’t do less. They make less noise.
AI and automation makes it easier than ever to attempt everything.
However, it does not make it safer to be mediocre at many things.
The separation happening now isn’t about tools.
It’s about judgment.
Pattern recognition.
Depth earned over time.
The people who will pull away in 2026 aren’t adding more skills for the sake of relevance.
They’re doubling down on the thing that made them valuable in the first place and using technology to amplify that edge, not dilute it.
Generalists compete on price.
Specialists compete on trust.
And trust is the only currency that compounds.
By Chris Allaire | Feb 3, 2026
I gave a presentation at my daughter’s middle school about AI tools vs intelligence, critical thinking, and overall what is happening in the “real world” out there.
These are my notes turned into a more readable format.
Every cycle has a moment where the story everyone is telling is slightly wrong.
Right now, the story is that AI is the divider.
That it’s machines versus humans.
That people who “get AI” will win, and everyone else will fall behind.
However, that’s not what’s happening.
What’s actually happening is quieter, and more uncomfortable.
AI didn’t create a new advantage.
It removed the old excuses.
The popular framing is simple: adopt AI tools and you’ll be fine; ignore them and you’ll fall behind.
But in reality, AI tools vs intelligence isn’t a race to collect tools. It’s a separation between people with strong fundamentals and people who relied on “being the answer person.”
In other words, the tools don’t create capability, they reveal it.
You’ve all heard my analogy on the Bad Golfer with Great Clubs vs the Great Golfer with Great Clubs, but in case you haven’t:
A few years ago, something interesting happened.
People who were already good at what they did started using AI early. Not because it was trendy, but because they understood how and why it could help. Those people didn’t become different overnight, but they quietly moved up a level.
At the same time, there were people starting from scratch who used the tools to get “good enough” very fast. The tools compressed the gap. For a moment.
That moment is over.
People with foundational fundamentals are uncapped because they have critical thought, reasoning patterns and talent to begin with.
I’m a decent golfer with good clubs. If Rory McIlroy gave me his clubs, I MIGHT be a little better.
If I gave Rory McIlroy MY clubs, he’d destroy me. Honestly, I could give Rory a set of shovels and he’d still wreck me.
That’s talent.
The clubs don’t create the golfer. They only reveal them.
That’s what’s happening now.
“Better than most” used to be enough.
It isn’t anymore.
Knowledge and experience with tools = Power
Little knowledge, little experience with tools = Disposable
Here’s the thing no one wants to say plainly:
Answers used to be a proxy for intelligence.
They aren’t anymore.
When anyone can generate a decent response, write passable copy, sketch an architecture, or summarize a strategy in seconds, the value of “having the answer” collapses.
There’s a big difference between:
Having answers can make you look smart. Knowing how to solve problems means you have intelligence.
And that’s the core of AI tools vs intelligence: tools can produce answers, but they can’t automatically produce reasoning.
Tools are incredible.
Use them to:
But when tools give you the path every time:
That’s not intelligence. That’s dependency
It’s the same reason we teach kids math without calculators, we teach cursive, maps without GPS, and writing without spell check.
Not because tools are bad, but because thinking is the point.
The fun part is solving the problem.
And in a world where answers are cheap, the people who can still do that will separate fast.
The question is, what side of the divide do you want to be on?
Written by: Chris Allaire
We’ve entered a market where the ability to think clearly under pressure matters more than where someone worked or what their title says.
Not because experience is irrelevant but because experience without judgment is just memory.
That’s why critical thinking in hiring is now the real separator. In 2026, the companies pulling ahead aren’t chasing the latest tools or hiring the loudest experts. They’re quietly prioritizing something much harder to find:
People who can observe, reason, connect dots, and solve problems when the playbook doesn’t exist.
They don’t just sound experienced. They perform.
Who has the skill beats who has the title.
Not because titles are meaningless but because titles are lagging indicators.
Skills are leading indicators.
According to the World Economic Forum, skill gaps are now the single biggest barrier to business transformation, and upskilling is no longer optional, it’s existential.
So, if you’re hiring, as you’re trying to differentiate everyone, the play is simple:
That’s exactly what critical thinking in hiring is designed to identify.
Hiring ML / AI, Security, Platform Engineering, Robotics, and Product Engineering, titles have become especially unreliable because:
In these environments, the capability gap between candidates can be massive and titles won’t tell you who can actually deliver. Critical thinking will.
Two people can hold the same title and have completely different capability levels yet the market still pretends the title is the baseline.
It’s not.
This is where most hiring processes break, and where the biggest opportunity lives.
Let’s call it out – Everyone is suddenly an “AI expert.”
They’re not.
Here’s how the market breaks down when you apply critical thinking in hiring instead of title assumptions.
At first they sound impressive; however, the confidence fades when you ask “How did you do?” or “How are you going to?
In practice, they brag about prompts.
On paper, they list every model, framework, and library on their resume.
These people are now abundant, and replaceable.
They understand fundamentals.
They use AI as a multiplier, not a crutch.
This is where strong teams are built.
This is the real separation.
These people:
They don’t “use AI.”
They govern it.
This is the tier clients are actually searching for, even if they don’t yet have the language to say it.
Stop writing job descriptions like shopping lists.
Define:
This turns hiring from filtering into forecasting.
Real operators love these questions.
Pretenders disappear.
2026 is the year of Separation
You don’t hire the tools, you hire the TALENT.
You’ve always had.
Now is not the time to change.
PEOPLE hire PEOPLE.
At Averity, we don’t sell resumes, we deliver talent with proof.
Written by: Chris Allaire
The Separation Playbook (2026)
The conversation has shifted.
Not because of AI.
Not because of tools.
Not because of technology.
But because the gap in TALENT is now visible.
2026 isn’t asking who’s busy.
It’s asking who’s useful.
This is the year where operators separate — not by volume, not by noise, not by stack — but by clarity, judgment, and execution.
Here’s the beginning of The Separation Playbook: a set of operating rules and questions designed to help you define where you actually stand, who you should be in business with, and what it really takes to play at the top tier.
This isn’t motivational.
It’s diagnostic.
If you don’t know why you’re doing something, everything else is noise.
AI doesn’t fix this.
Automation doesn’t fix this.
Scale doesn’t fix this.
Top-tier operators are anchored.
They can answer:
If your “why” collapses into tools, trends, or money, you’re already behind.
Separation starts with purpose.
Energy compounds. Burnout doesn’t.
Elite operators don’t confuse suffering with seriousness. They design their work so effort fuels momentum instead of draining it.
Ask yourself:
If your work consistently drains you, it will eventually drain your results.
Generalists compete on price.
Specialists compete on value.
Top-tier operators know their edge. They protect it, sharpen it, and build around it.
The hard questions:
If you can’t articulate your edge, the market will decide it for you.
Reputation isn’t built in theory.
It’s built through action.
Elite operators don’t wait for perfect conditions. They move decisively with imperfect information.
Ask yourself:
Comfort is expensive. Momentum isn’t.
You can’t automate what you don’t understand.
Top-tier operators earn the right to automate by first mastering the process manually.
Key questions:
Automation without understanding doesn’t scale results.
It scales mistakes.
We all have the same sport’s car now.
Same tools. Same AI. Same access.
The difference is skill.
Using better tools doesn’t make you better — it makes your strengths (and weaknesses) louder.
Ask yourself:
Tools expose skill. They don’t create it.
Research from JYX and Carnegie Mellon shows that overreliance on AI erodes critical thinking and expertise. Like any muscle, judgment and skill require constant use — or they weaken.
Top-tier operators use AI deliberately, not lazily.
They ask:
AI should sharpen you.
If it’s dulling you, that’s a problem.
Most tools don’t fail.
They’re just solving problems that don’t matter.
Elite operators are ruthless about problem definition.
They ask:
No problem. No tool. No exception.
Not all advice is equal. Most of it is noise.
Top-tier operators curate their inputs the same way they curate their teams.
They ask:
If the answer is no, the advice is suspect.
This is the year where:
The field isn’t level anymore.
And it’s not going to be again.
The Separation Playbook isn’t about judgment.
It’s about honesty.
Honesty about where you are.
Honesty about what you’re good at.
Honesty about who you should — and shouldn’t — be in business with.
Because in 2026, the market isn’t rewarding effort.
It’s rewarding execution.
And execution belongs to the operators who did the work before they scaled it.
The Separation Playbook is a set of operating rules and diagnostic questions that help you sharpen clarity, judgment, and execution so you can compete at the top tier in 2026.
Top-tier operators separate through purpose, decisive judgment, and consistent execution, not louder activity, more tools, or more hours.
Because automation scales whatever you already have clarity or confusion. Manual mastery ensures you understand the process before you scale it.
Use AI as an amplifier, not a replacement. Keep verification, judgment, and high-stakes decisions human-led, and audit outputs before acting.
Look at what people consistently rely on you for, where you win disproportionately, and what work remains if you cut 80% of your activity, then build around that.
Written by: Chris Allaire
Value Add Wins. Shortcuts Lose. Skill Decides.
The last three years didn’t just reshape the market, they exposed it.
What we lived through from 2023 to now wasn’t a cycle.
It was a sorting mechanism.
And walking into 2026, the divide is no longer subtle.
It’s clear.
It’s structural.
It’s permanent.
This is the year where value add separates from volume,
execution separates from talk,and real skill separates from convenience.
If you’re a founder or employer, this matters because you’re not just hiring people anymore, you’re hiring judgment, accountability, and outcomes.
2023 was unforgiving.
Budgets froze.
Hiring slowed to a crawl.
Late-stage deals died.
Trust eroded across the board.
Everyone felt it.
This wasn’t a “bad year.”
It was a stress test.
And stress tests don’t lie. They reveal:
2023 stripped everything down to fundamentals.
No momentum.
No padding.
No hiding.
By late 2024, the narrative shifted.
Not toward mastery but toward shortcuts.
“The year of efficiency.”
“The year of leverage.”
“The year of automation.”
And it sounded like this:
“Let’s automate it.”
“Let’s AI it.”
“Let’s remove humans from the equation.”
AI exploded, and yes, it mattered. Still does.
A lot of people didn’t pivot.
They didn’t evolve.
They didn’t double down on skill.
But instead of using it to augment skill, many tried to use it to replace effort.
Critical thinking? Deferred.
Communication? Automated.
Listening? Skipped.
Judgment? Outsourced.
The market didn’t collapse in 2024, it got quiet.
That quiet was the warning.
Then reality hit.
Hard.
It’s not what AI IS doing, its what it’s NOT doing!
2025 became the year of the uncomfortable question:
“Why isn’t AI doing my job?”
“Why isn’t it finding problems to solve?”
“Wait… I still have to think?”
“I still have to work?”
Email SPAM at its peak:
This is where the 95% failure rate showed up.
Because AI doesn’t:
And for a lot of people, that realization was brutal.
They weren’t under-skilled at the tool.
They were under-skilled at the job.
AI didn’t fail them.
It exposed them.
Here’s the truth no one can dodge anymore:
We all have the same bat (think baseball 😉) .
Same tools.
Same AI.
Same access.
Same platforms.
But can you actually hit a 100-mph fastball? (Again, the baseball reference…stick with me)
Because that is skill.
Timing is everything.
Reps build consistency.
Pressure reveals judgment.
And experience is earned the hard way.
Using a calculator doesn’t make you good at math.
Using AI doesn’t make you valuable.
Everyone can add.
Everyone can prompt.
Not everyone can execute.
What used to be fuzzy is now undeniable:
Amazing —
Very Good —
Decent —
Poor —
This isn’t about effort alone.
It’s about skill density.
And the market has stopped pretending otherwise.
Let’s reset expectations.
You still have to work your ass off.
There is no automation for:
AI can help you move faster.
It cannot help you think deeper — unless you already can.
It amplifies skill.
It does not create it.
2026 isn’t a rebuild year.
It isn’t a bounce-back year.
Instead it’s a separation year.
This is where:
At that point, you’re either:
The field isn’t level anymore.
And it’s not going to be again.
No vendor.
No middleman.
And definitely no noise generator.
Value add.
Clarity.
Judgment.
Execution.
Delivery.
Measured, not imagined (recruiting data from Averity):
That’s not marketing.
That’s performance.
2026 belongs to:
As a result, The people who kept sharpening real skills while others chased ease?
They’re already pulling away.
Not loudly.
Not dramatically.
Decisively.This is the year of separation.
And separation favors the ones who can still hit the fastball.
Written by: Chris Allaire
It still surprises hiring teams when a great candidate declines an offer, especially in a market that’s no longer the free-for-all it was in 2022. But even though the economy cooled and tech experienced massive layoffs, candidates are still saying “no,” and they’re doing it for predictable reasons.
Let’s ground this conversation in what’s actually happening out there.
The last few years flipped the market on its head:
So yes, the market changed. But the psychology behind accepting or rejecting a job has not.
Here’s why people still turn down offers in 2025, backed by data.
After years of layoffs and reshuffling, candidates are more cautious than ever. They’re not leaving for a slight bump, they’re leaving for meaningful progress.
Studies show candidates prioritize:
If the move feels lateral, risky, or unclear, they stay put.
In this market, stability is compensation.
The #1 reason candidates rejected offers in the last two years? Low or unfair pay.
A few more numbers:
People aren’t chasing the biggest number — they’re chasing fairness, transparency, and stability.
If your comp philosophy is vague or your offer is off-market by 10–20%, candidates will pass without hesitation.
Despite a shift toward hybrid and office-first policies:
People have redesigned their lives around flexibility. They’re not giving that up unless the role is exceptional.
This is the most avoidable reason offers fall apart and the most common.
Multiple studies show:
Candidates don’t judge your culture by your Careers page, they judge it by:
The experience is the product.
Candidates have become investigators. Before accepting an offer, they’re digging into:
And here’s a big one:
If what you say and what they see don’t match, trust breaks and the offer dies.
Even with fewer competing offers:
Silence is not safety.
Candidates need to hear why hiring is happening now and why the role is stable.
1. Diagnose “Why Now?” on Day One
Understand motivation early and track it every step of the way.
2. Treat Candidate Experience Like a Core Product
Intentional, consistent, human communication can increase acceptance rates dramatically.
3. Be Transparent About Comp, Flexibility & Stability
No surprises. No vague answers. Clear beats clever every time.
4. Use Data to Guide, Not Guess
Market alignment is now a competitive advantage.
5. Partner with Recruiters Who Actually Talk to People
AI screens resumes.
Averity screens humans.
We catch concerns early, keep candidates engaged, and help teams land the people they truly want, not the first person who says yes.
Candidates aren’t rejecting offers because they’re picky.
They’re rejecting offers because:
When employers understand these patterns and address them with honesty, clarity, and empathy, hiring becomes faster, easier, and far more successful.
And that’s what people-first recruiting looks like in 2025.
Written By Chris Allaire | Founder & CEO, Averity
People Hire People.
Every few years, the tech industry hits an inflection point — a moment where multiple forces collide at once: new tools show up, old structures break down, and the way we build, secure, and hire starts to shift under our feet.
Right now, we’re living through one of those moments.
Platform engineering is maturing.
AI is opening new frontiers — and new vulnerabilities.
Cybercrime is booming on a global scale.
And recruiters are no longer just sifting resumes… they’re fighting off deepfake candidates and AI-generated personas.
To unpack this moment, I sat down with someone (watch the full interview here) who’s been in the middle of it for nearly a decade: Daniel “Danny” Wellner, Averity’s Director of Security, DevOps & Platform Engineering Recruiting. Danny isn’t just filling roles — he’s watching entire disciplines transform in real time.
This is the state of modern engineering, straight from the front lines.
When Danny entered the space nine years ago, DevOps was the shiny new promise. If you said “CI/CD” and “Kubernetes” enough times, people assumed you were building the future.
But like every wave, DevOps matured.
“Platform engineering is DevOps 2.0. It’s DevOps… with structure.”
Here’s the difference:
DevOps was about speed.
Move fast. Automate everything. Break fewer things than before.
SRE was about stability.
Uptime. SLIs. Observability.
Platform engineering is about productizing the developer experience.
Self-service. Guardrails. Repeatable pipelines. Secure AI environments.
It’s “you build it, you run it” — but with an internal platform that actually supports it.
Danny put it simply:
“A great platform engineer thinks like a product owner.”
Not just building infra… but designing the system that lets everyone else build faster and safer.
It’s no surprise the demand is exploding.
This is exactly what the DevOps boom felt like in 2018 — only bigger.
Here’s a stat Danny dropped that should be front-page news:
Cybercrime will cost $10.5 trillion annually.
If it were a country, it would be the world’s 3rd largest economy.
Let that sink in.
AI hasn’t just changed how software gets built — it has changed how it gets attacked:
The threat surface is no longer just enterprise systems.
It’s your phone. Your inbox. Your Wi-Fi. Your kids’ devices.
“Everyone with a laptop is part of the global attack surface now.”
Yet cybersecurity barely gets mainstream attention.
Why? Because the danger isn’t loud until it hits home.
Fraudulent invoices… changed wiring instructions… cloned voices… all happening daily.
We’re in a new era — and most people don’t realize the rules have changed.
If you’ve wondered whether AI is disrupting recruiting — here’s your confirmation:
“I see fake candidates multiple times a week. Sometimes daily.”
Fake resumes.
Fake identities.
Fake LinkedIn profiles.
Deepfake interviews.
Real engineers impersonated by people overseas trying to funnel U.S. salaries elsewhere.
And the scary part?
A few of them are good.
Really good.
They speak like real engineers.
They use authentic terminology.
They pass first-round screens.
They know how to mimic experience.
Until someone highly specialized — someone like Danny — asks the layered questions that expose the cracks.
You can’t outsource “experience.”
You can’t fake niche knowledge.
This is exactly why specialized recruiters matter more now than ever.
The old hiring playbook doesn’t work anymore.
Everything has changed.
Here’s what Danny says every CTO and VP of Engineering needs to do:
1. Stop trusting resumes.
AI writes flawless ones now.
Every candidate looks like a superhero.
2. Pick up the phone and talk to humans.
A real engineer can explain what they built.
A fake one cannot.
3. Ask scenario-based questions.
“How much Python?” won’t tell you anything.
“What observability patterns did you use to debug X?” will.
4. Partner with specialists.
If your recruiter doesn’t live in your world, you’re flying blind.
5. Accept that verification is now part of recruiting.
You need more signal.
More context.
More trust.
This isn’t about gatekeeping — it’s about protecting teams, culture, and data.
Despite the risks, the future is bright.
And Danny is bullish on where things are going.
AI security is becoming one of the most important fields in tech.
Defenders are learning to fight AI with AI.
Platform engineering is defining the next decade of developer productivity.
Internal tools are the new competitive edge.
Recruiting is returning to its roots: relationships over automation.
The more the world gets automated, the more valuable human trust becomes.
“Trust is the differentiator in a world full of noise.”
And Danny’s right.
For all the buzzwords, automation, AI agents, hallucinations, bots, and deepfakes…
the competitive advantage is still human connection.
People hire people.
People work with people.
And people trust people.
That’s the through-line that isn’t changing.
Danny is one of the best in the business.
He lives on LinkedIn (10–12 hours a day).
He knows the real market, the real people, and the real risks.
Connect with him on LinkedIn or at Averity: https://www.averityteam.com
Written by: Chris Allaire
After 28 years and nearly a thousand placements, Averity’s own relationship king, Alex Dubovoy, explains why trust, conversations, and community still outperform any algorithm in a so-called AI-obsessed world.
AI is everywhere in recruiting. It sources candidates, screens them, handles scheduling and in some cases, it’s even conducting the interviews.
We’re living in a time where human first recruiting in an AI world isn’t just a catchy phrase, it’s a competitive advantage.
Chris Allaire jokes that we’re heading toward a world where an AI agent representing the company argues with an AI agent representing the candidate—Alexa vs. Siri fighting it out while the humans watch from the sidelines.
For Alex Dubovoy, nearly three decades into recruiting, that future isn’t just silly. It’s dangerous.
“In my 28 years in recruitment,” Alex says, “I have never not one time placed a person without having a detailed conversation with them. Never. Not once.”
Because the real mechanics of hiring still live in one place: human nuance.
It’s not just what people say—it’s what they don’t say.
A bot can’t hear that.
A seasoned recruiter can.
Alex tells a familiar story: a candidate who sounded flat and distracted on a call. A bot would have rejected him instantly.
Alex heard something else: nerves.
So he did the most “low-tech” thing imaginable—he became human.
Cracked a joke. Opened up. Created safety.
And suddenly the candidate came alive.
He became someone with a story, skills, ambition, and heart.
That kind of transformation doesn’t happen in a screening portal.
Same with relocation.
A bot hears:
“I’d consider moving from Delaware to New York.” ✔️
Alex hears:
“Wait—why would someone move from a low cost-of-living area to New York while fully employed? What’s really happening?”
Same with compensation.
A bot hears:
“I want $200K.” ✖️ Too expensive. Reject.
Alex asks:
“If you absolutely loved the job, would $180K work?”
The candidate:
“Yeah… of course.”
That small space between checkbox answers and real conversations?
That’s where recruiting actually happens and where human first recruiting in an AI world quietly outperforms automated decision-making.
Alex isn’t anti-AI. Not even close.
He loves AI—when it’s used in the right places.
“I want to be on the phone all day,” he says. “Talking to interesting people. Solving business problems. If AI gives me more time to talk to humans, I’m in.”
Here’s where AI earns its keep:
1. Transcribing job intake calls
When a CTO fires off five roles and three urgent business problems in 90 seconds, AI keeps track.
2. Summarizing candidate conversations
Not rewriting resumes—but capturing real human stories in the candidate’s actual words.
3. Reminders and follow-up tasks
Humans forget. AI doesn’t.
Where AI belongs:
➡️ The back office (busywork)
Where humans belong:
➡️ The front office (trust, judgment, relationships)
Recruiting is relationship-first.
Technology is support-first.
That’s the essence of human first recruiting in an AI world: let machines handle mechanics so humans can handle meaning.
When Chris asks how important the network really is, Alex doesn’t blink:
“It’s why I can feed my children.”
Averity isn’t hired because we run clever Boolean strings.
We’re hired because of who answers the phone when we call.
Alex’s first-level network isn’t contacts—it’s a community built over 28 years:
People who return calls same-day.
Leaders who say, “Tell me what you’re working on.”
Engineers who trust him enough to consider opportunities they weren’t even looking for.
That’s why Alex is radically transparent. If a role is outside his lane, he says so. And when it is in his network?
That’s when lightning strikes.
• The FileMaker developer
Met through a friend. No open roles. One week later, a client says, “Know any FileMaker folks?”
That developer has now been there 14 years.
• The Go engineer
Chris insisted a client meet him even though there wasn’t an exact role.
The company created a job, paid above budget, hired him and later exited in a multi-billion-dollar sale.
You don’t get that from bots.
You get that from long-term relationships.
Alex makes a sharp observation: trust in this industry is the lowest he’s ever seen.
Resumes are AI-polished.
Profiles can be fake.
Candidates can be fabricated outright.
He tells the story of a candidate whose background looked “too good to be true.”
It scared him enough to check Averity’s database.
There he has been logged since 2018.
A real human.
The fact that he had to verify it says everything.
Trust is now currency.
It’s the engineer who calls you the moment they’re laid off.
The CTO who sighs with relief when you ask, “What business problem are we actually solving?”
The candidate who calls back years later just to say:
“You changed my life. You opened the door.”
Bots don’t build that.
People do.
Chris drops a powerful reminder:
You spend 75–80% of your waking life working—or preparing for work.
If that’s true, then who you work with matters more than anything.
That’s why Averity obsesses over humans, not just acronyms.
It’s why our average recruiter tenure is around seven years in an industry famous for churn.
It’s why Alex runs DevOps & Drinks, the largest DevOps meetup in NYC, built on three rules:
No recruiting.
No sales pitches.
No demos.
Just people showing up for connection, learning, and community.
Because when great humans choose to work together, you don’t just build teams.
You build momentum.
You build trust.
You build outcomes.
“People hire people,” Alex says. “When you get the right group of humans together, you’re an unstoppable force.”
If you’re hiring especially in AI, ML, Cyber, DevOps, Product, or Software Engineering—here’s the bottom line:
1. Use AI to eliminate friction.
Automation is great for transcripts, summaries, scheduling, and reminders.
2. Use humans to build trust.
You cannot outsource judgment, nuance, or relationship-building.
3. Invest in your network before you need it.
Your future hires already exist, you just haven’t met them yet.
4. Never outsource your first impression to a bot.
Your employer brand lives or dies on human connection.
Because at the end of the day, your hiring success won’t be defined by your tech stack.
It’ll be defined by a single line we hear more and more in conversations with candidates and clients:
“I’m just glad you’re not a bot.”
That’s the quiet power of human first recruiting in an AI world—and it’s not going away.
Written By: Chris Allaire
Interview with Daniel Wellner, Director Platform Engineering, Security and DevOps at Averity
In today’s technology landscape, the traditional boundaries between engineering roles are dissolving.
Once, DevOps, Data Engineering, and Software Engineering were distinct lanes. Now, those roads converge into something far more complex — and far more in demand.
That’s where Danny Wellner lives.
Danny has spent nearly a decade recruiting some of the most advanced engineers in infrastructure, DevOps, and platform engineering. Trained by Averity co-founder Alex Dubovoy — widely regarded as one of the godfathers of DevOps recruiting — Danny has become one of the most trusted specialists in the field, helping companies build the technical backbone behind AI and next-generation systems.
“Pretty much every single technical role now has some sort of AI play or understanding built into it,” says Wellner. “It’s no longer just about deployment speed — it’s about building the ground for agentic-based systems and AI-driven services.”
What skills do AI-era engineers need most?
Traditional job boundaries are vanishing.
“You used to have a software engineer who built code, a data engineer who managed ETLs, and an infrastructure engineer who deployed systems,” Danny explains. “Now, companies want someone who can do all three — and understands AI tools on top of it.”
With nearly a decade of experience recruiting elite DevOps, platform, and infrastructure engineers, Danny brings deep technical fluency and a vast network of senior-level talent. Having witnessed how DevOps evolved into Platform Engineering and now into AI-driven infrastructure, he’s been at the center of that transformation since day one.
Where are the best AI engineers coming from?
If you think companies are training their people for this — think again.
“A lot of companies aren’t running this stuff in production yet,” Danny notes. “So the people who really know it? They’re learning on their own time — taking courses, experimenting, building projects, or working at the few companies actually pushing this tech forward.”
That curiosity and self-driven learning are the differentiators. Engineers who tinker are the ones who thrive.
Recruiters like Danny stay close to the action — tracking which companies are truly running AI in production and maintaining deep personal relationships across the industry.
“It’s not hard to keep up with people,” Danny says. “It’s just time-consuming. Maybe only 10% are working on cutting-edge AI, but those relationships are gold.”
How is AI impacting cybersecurity and data governance?
Every innovation creates new exposure. In 2025, security has never been more volatile.
“The attacks have ramped up tenfold,” says Danny. “It’s not just ransomware — it’s the sheer volume of attempts coming from everywhere.”
As companies race to integrate AI, new risks surface — from data leaks to unintentional public disclosures via tools like ChatGPT.
“You upload a public document to an AI tool, and it’s now public information,” Danny warns. “One small mistake can leave your entire company vulnerable.”
That’s why AI security and data governance have become core pillars of modern engineering. The rise of Application Security Engineers — software-savvy security experts who understand vulnerabilities in code and architecture — is reshaping what it means to protect a business.
“Data is the most valuable resource in the world right now,” Danny says. “Protecting it and keeping it clean — that’s the real challenge.”
How much do AI engineers make today?
Averity’s world lives at the top of the talent pyramid. Senior, Principal, and Staff-level engineers aren’t cheap — and they shouldn’t be.
“Baseline, you’re looking at $180K to $190K,” Danny shares. “But total comp can range up to $400K–$500K, depending on experience and specialization.”
And yes — the unicorns exist. Some engineers in AI and platform architecture are commanding $800K to $1M+ total compensation packages.
“The top-round draft picks get paid,” Danny says. “If you want to compete with the best companies in the world, you’ve got to pay top-round draft-pick salaries.”
Even in the age of automation, the human touch still separates the good from the great.
Danny emphasizes that relationships remain the currency of elite recruiting.
“Relationships are still everything,” he says. “Keeping the old ones healthy and building new ones — that’s where the magic happens.”
Few recruiters understand the evolution of infrastructure roles like Danny Wellner. After nearly ten years in DevOps and platform recruiting Danny’s perspective bridges the past, present, and future of how engineers build the systems that power AI.
And that’s where firms like Averity stand out — blending deep technical specialization with genuine human connection.
In a world where AI writes job posts and scans résumés, Danny and his team are talking to the people building the future — one connection at a time.
Interview with John Birchall, Director of AI, ML, and Data at Averity
Written by: Chris Allaire — November 2025
Hiring elite AI or machine learning engineers isn’t just hard — it’s competitive at the highest level. Between inflated titles, AI-written résumés, and skyrocketing salaries at big tech companies, finding the right person — not just anyone with “AI” in their LinkedIn headline — is harder than ever.
Few people know that better than John Birchall, Averity’s Director of AI, ML, and Data, who’s been recruiting in this space since 2018 — long before AI engineers were a thing.
Meet John Birchall, Averity’s Director of AI, ML, and Data—widely recognized as New York City’s foremost expert on data and AI recruiting. Since 2018, John has built relationships with the engineers, scientists, and leaders driving the city’s AI revolution. His insight comes not from trends, but from thousands of real conversations with the people building what’s next.
“When we first started,” Birchall laughs, “clients would say, ‘We don’t even know what a data scientist does, but we want one.’ That’s how early we were.”
Back then, roles were neat and separate—data engineers built pipelines, scientists modeled data, software engineers deployed code.
Today? Those lines have completely blurred.
“Now clients want individuals who can do it all—build infrastructure, fine-tune models, deploy them, and turn those insights into business outcomes,” Birchall explains.
That evolution birthed the modern AI Engineer—a blend of software engineer, data scientist, and MLOps specialist. And nowhere has this transformation been faster—or more competitive—than in New York City’s tech scene.
Averity’s NYC data practice shows four roles dominating demand:
“Two companies can use the same title, but the jobs are totally different,” Birchall notes. “We tell clients: don’t stress the title—tell us what the work actually is, and we’ll find the right person.”
Early-stage startups need builders who can create the AI foundation. Mature organizations want experts who can scale and optimize models for measurable impact. Birchall’s team helps both—matching capability to business outcome, not buzzword.
Post an AI job online and you’ll get 500 applications in a day—most from people who aren’t even close.
“We’ve been in this space so long that we already know the people,” says Birchall. “Many of the top AI engineers we’re placing now? We’ve known them for eight years.”
That’s why Averity’s numbers crush industry averages:
It’s not volume. It’s precision. This is expertise built in New York’s toughest market.
“We recruit AI engineers,” Birchall says, “but we don’t automate recruiting. People hire people.”
That’s The Averity Experience—real relationships, earned trust, and years of human context that no algorithm can mimic.
“We’ve seen these engineers’ résumés for 6-8 years” he adds. “We know what’s real and what’s AI-generated.”
When Averity presents a candidate, clients know that résumé represents a conversation worth having.
For senior-level AI or ML engineers, the New York market looks like this:
“You can absolutely get world-class talent without paying a million dollars,” Birchall says. “Start with outcomes and hire the person who can deliver them.”
“If you run an ad for an AI engineer, you’ll drown in applications,” Birchall warns. “The time you’ll waste is enormous—and 99 percent of the candidates won’t meet your bar.”
That’s why recruiting is marketing.
“Your recruiting partner is your first line of defense,” says Allaire. “They’re the voice of your brand in the market. If you’re not empowering that function—or partnering with experts like Averity—you’re already behind.”
Birchall’s advice to New York CTOs and hiring managers:
✅ Define the business outcome first.
✅ Forget titles—clarify the problem to solve.
✅ Partner with specialists who live in your ecosystem.
✅ Remember: relationships > résumés.
“It’s not about tech stacks anymore,” Birchall says. “It’s about finding the human who can make the technology matter.”
Hiring AI engineers isn’t a keyword game—it’s a relationship game.
The best recruiters know the humans behind the code, the context behind the résumé, and the pulse of the New York tech community.
“We’re not trying to automate human connection,” Birchall says. “We’re amplifying it.”
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