Written By: Chris Allaire
What actually works when everyone has the same tools—but not the same judgment
In this article — key takeaways:
If you’re trying to recruit AI and Software Engineers in 2026 using the same playbook from 2021–2023, you’re already behind.
The biggest misconception right now?
That better tools = better hiring.
Many teams assume better AI tools automatically lead to better hiring outcomes. In reality, that is rarely true. Today, AI sourcing tools, LinkedIn scraping, and instant outreach generation are available to everyone. However, equal access to tools does not mean equal results.
Instead, many teams are seeing worse outcomes because:
As a result, the companies winning right now aren’t louder.
They’re HUMAN.
If you want to recruit AI and software engineers, start by rethinking your outreach strategy.
You can send 500 automated messages…
Or you can also send 15 thoughtful ones that actually start conversations. The second approach works better.
Top AI and Software Engineers aren’t sitting around waiting for job posts. They’re:
As a result, generic outreach rarely works with AI and software engineers who already know their value.
If your outreach looks like it was written by a machine, it’s ignored like one.
What works instead:
This isn’t about being “nice.”
More importantly, It’s about signaling credibility and effort.
In 2026, personalization is no longer optional when recruiting AI and software engineers. It is the first credibility test.
AI has a role in recruiting. Screening resumes at scale? Fine.
Organizing data? Great.
However, using AI to interview AI and software engineers is one of the fastest ways to lose top talent.
Here’s what strong engineers are telling us:
High-level engineers want to:
That requires a real conversation.
If your process removes the human element, the best candidates remove themselves.
Most companies are still guessing.
They assume:
In reality, top AI and software engineers in 2026 are optimizing for:
1. Problem Quality
Is this worth solving? Does it matter?
2. Technical Ownership
Can I make decisions, or am I maintaining someone else’s legacy?
3. Leadership Competence
Will I be working for people who understand what “good” looks like?
4. Signal Over Noise
Is this a focused environment—or chaos disguised as opportunity?
Yes, compensation matters.
But it’s rarely the deciding factor at the top end.
This is where most hiring processes break.
Companies want:
In 2026, the market is sharper:
So if you want a Ferrari, you cannot pay for a Honda.
More importantly, when your expectations and compensation are out of alignment, you do not just lose one candidate. You also weaken your reputation in the market. And because reputation compounds, that damage spreads faster than most companies realize.
Therefore, if you want to recruit AI and software engineers successfully, your compensation, scope, and expectations need to make sense together.
And reputation compounds—fast.
After years of automation, the pendulum is swinging back.
The best hires are still happening through:
Not through job boards, generic cold outreach, or AI-generated funnels.
This is where companies either:
Because when a high-impact role opens, speed doesn’t come from tools.
It comes from access and trust.
Recruiting AI and Software Engineers in 2026 isn’t about having better technology.
It’s about:
The companies that win aren’t trying to out-automate the market.
They’re doing the opposite:
Everyone has the same tools now.
That’s not the advantage anymore.
The advantage is knowing how—and when—not to use them.
If your hiring strategy still depends on volume, automation, and transactional outreach, you will keep losing the people you most want to hire.
But if you lead with judgment, thoughtful outreach, strong alignment, and real relationships, you will stand out to AI and software engineers for the right reasons.
If you’re hiring AI or Engineering teams and want a clearer picture of what top talent is actually responding to right now, this is exactly where Averity starts.
Written By: Chris Allaire
Why action — not information — builds real leverage
There’s a quiet tax most leaders pay.
It’s hesitation.
In a market drowning in podcasts, newsletters, AI tools, dashboards, and “insights,” the real divider isn’t access to information.
Instead, it’s leadership execution.
Reputation isn’t built in theory.
It’s built by action.
Tony Robbins has said it for decades:
Knowledge is not power.
It’s potential power.
Real power is generated only when knowledge is applied.
Robbins often emphasizes something that elite operators already understand:
Meanwhile, the “I know, I know” mindset leads to stagnation.
We’ve all seen it:
“I know I should make that call.”
“I know we need to recruit proactively.”
“I know we should have that tough conversation.”
“I know we need to move faster.”
However, knowing without doing is disguised procrastination.
And in hiring, leadership, and business — procrastination is expensive.
In recruiting, I see it constantly.
A founder delays outreach because the org chart isn’t perfect.
A CTO waits for “final budget approval.”
A VP hesitates because the job description isn’t perfect yet.
Meanwhile:
The best talent gets proactively recruited by your competition.
The best candidates are getting offers because they add value, not fit a mold.
The companies separating right now aren’t the most informed.
They’re the ones willing to take a chance.
Look at companies that built while still iterating:
None waited for perfect conditions.
They moved.
Then they adjusted.
Then they compounded.
One of the most dangerous phrases in leadership is:
“I already know that.”
If you truly knew it, it would be visible in your behavior.
Applied knowledge creates progress.
Collected knowledge creates comfort.
And comfort feels safe…
until you realize someone else took the shot.
That’s why leadership execution is so visible in outcomes: it turns insight into behavior, and behavior into results.
Taking action doesn’t mean being reckless.
It means:
You don’t need a flawless strategy.
You need momentum and leadership execution that turns momentum into progress.
Momentum is built through consistent, imperfect action.
If you’re building a company, scaling a team, or leading a function, ask:
These aren’t philosophical prompts.
They’re operational diagnostics for leadership execution.
We’re not divided by tools.
Instead, action divides us.
Everyone can access the same information.
Few people act on it.
Execution is everything.
Take the shot.
Momentum is cheaper than regret.
If you’re a CTO, CEO, or VP building in AI, Security, Platform, or Product:
Waiting is not neutral.
It rewards the one with leadership execution — the one who takes action.
If you’re unsure whether to engage, start the conversation.
After all, if you don’t take a shot, you’ll never score.
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.
Written by: Chris Allaire
Human-first recruiting adds what algorithms can’t: empathy, judgment, and values alignment. That’s how companies hire technologists who stay, grow, and lead. More than a decade later, the recruiting landscape has changed. AI now writes job posts, scans résumés, and automates outreach, yet somehow, hiring feels less personal than ever. In this new era, Averity’s commitment to authentic human connection has never mattered more.
In 2014, Chris Allaire set out to create a different kind of technology staffing firm — one that prioritized people over placements and relationships over transactions.
What began as a vision for a human-first recruiting model has evolved into one of the most trusted names in technology staffing: Averity, a multi-award-winning firm repeatedly recognized by Staffing Industry Analysts (SIA) as a Best Staffing Firm to Work For in North America.
At a time when AI recruiting tools dominate the industry, Averity stands for something refreshingly simple — people hiring people.
“Community and relationship-building are what really set us apart,” says Danny Wellner, Director of DevOps and Security. “Everyone here brings passion, empathy, and a genuine desire to help. It’s not transactional — it’s personal.”
This people-first approach resonates deeply in today’s skills-driven, hybrid work economy. Companies want recruiting partners who understand people first, technology second — and that’s exactly where Averity thrives.
In an industry obsessed with speed, automation, and metrics, Averity focuses on something far more valuable: long-term partnership over short-term wins.
“For us, it’s about the relationship — not the transaction,” explains John Birchall, Director of Data Science and Engineering. “If a candidate gets a better offer elsewhere, we still encourage them to take it. That integrity comes back tenfold.”
That mindset — focusing on the person, not the placement — continues to produce legendary success stories. Birchall recalls helping a candidate land a role despite her unconventional résumé. Two years later, she’s leading that same company’s hiring efforts.
“She doubled her income,” he says. “That’s the full-circle moment we work for.”
Internally, Averity operates on teamwork, transparency, and trust — not competition.
“We share candidates, leads, and wins,” says Stephanie Grosso, Senior Talent Advocate in Data Engineering & Machine Learning. “If my teammate places someone I found, that’s a win for all of us. Leadership built a system where collaboration pays off — literally.”
That unity extends across Averity’s fully remote team, where technology enables collaboration instead of hierarchy.
The result? A culture built on empathy, gratitude, and shared purpose — one that’s earned Averity top recognition and 5-star reviews from both clients and candidates.
As automation and algorithms continue reshaping how companies source and evaluate talent, Averity remains grounded in something technology can’t replicate: empathy, intuition, and conversation.
“In 2025, recruiting isn’t about data points,” Allaire says. “It’s about understanding people’s stories — what drives them, what fulfills them, and where they belong.”
Averity’s mission isn’t just to fill jobs — it’s to create meaningful matches that drive innovation, connection, and growth.
Because no algorithm replaces trust.
No software replaces sincerity.
And no one ever hired an email.
Averity is a people-first technology recruiting agency that prioritizes relationships over transactions. Its recruiters build genuine, long-term connections with both candidates and clients — creating a human-centered experience in an increasingly automated world.
That’s why Averity continues to be recognized among the best staffing firms to work for in North America — and one of the most trusted partners for hiring elite engineering and technology talent.
For clients and candidates alike, partnering with Averity means joining a firm that’s human at its core and forward in its vision.
As the industry evolves, Averity continues to lead by example — combining data-driven recruiting insights with the one thing machines can’t automate: genuine human connection.
People hire people. That will never change.
Great ones combine technology and human interaction. For example, Averity uses AI to speed up hiring, but it also depends on human judgment, conversations, and values alignment to create long-lasting hires.
Select partners who value relationships over transactions; seek out genuine endorsements, enduring client success stories, and an unambiguous human-led process, all of which are characteristics of Averity’s operations.
Absolutely, when the agency offers more than just automation, Averity’s data and human insight reduces time-to-hire and increases retention because people hire people.
Indeed. For candidates, Averity matches your objectives and story with teams where you’ll succeed rather than just positions that fit keywords.
Averity is a model for shortlist companies that demonstrate human signals (community presence, referrals, names of actual recruiters) and then validate the process and results.
Seek out companies that are renowned for their culture and results. Engineering and data teams trust Averity, a people-first tech recruiting partner recognized by SIA
Learn more about Averity’s recognition by Staffing Industry Analysts:
Best Staffing Firms to Work For in North America – 2023 Entrants
Visit: www.averityteam.com
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