Has AI made ideation so cheap that execution is becoming the scarce skill?
For most of history, getting from a blank page to a decent idea took work.
Now you can open ChatGPT or Claude and generate 20 strategies, 50 content ideas, a new product concept, a sales campaign, an operating plan, a hiring strategy, and six ways to improve all of them before breakfast.
That feels like productivity.
Sometimes it is.
But it can also become productive looking procrastination.
You start with one idea. AI gives you five extensions. Then you ask what you’re missing. Now you have twelve. Then you ask it to prioritize them, turn them into projects, create a roadmap, and find opportunities around each one.
Suddenly, you’ve spent two hours building an incredibly sophisticated list of things you haven’t actually done.
There’s a bigger distinction underneath all of this.
AI has dramatically increased our capacity to think. It has not increased the number of hours in the day.
Before AI, the problem was often:
“I need a good idea.”
Now it might be:
“Which one of these 37 good ideas am I actually going to finish?”
That applies to founders, executives, marketers, developers, recruiters, almost everybody.
I’m in this myself.
Over the last couple of years, I’ve built HUMAN, a fully functioning back office financial operations system, live CFO dashboards and reporting, a client intelligence engine, a relationship intelligence engine, new recruiting ideas, new service areas, content systems, automation, and more.
AI makes it possible to see connections between all of these things incredibly quickly.
That is powerful.
But it also creates a legitimate management question.
Are we building businesses faster, or are we just building bigger backlogs?
The interesting part is that the research says AI really is improving productivity.
In controlled studies, people using AI have completed certain kinds of work faster and, in many cases, produced better results. Customer support workers using AI resolved about 15% more issues per hour, and a major knowledge worker study found people completed appropriate tasks faster and at higher quality.
So the argument here is not that AI is making us less productive.
It is that task productivity and actual execution may not be the same thing.
A 2025 field experiment involving more than 7,000 knowledge workers across 66 companies found that people using generative AI spent about two fewer hours per week on email and worked less outside normal hours.
That is meaningful.
But the researchers found very little change in the overall quantity or composition of the work being done.
Another recent review found that generative AI was particularly useful for gathering and developing information, but the results became more mixed when AI moved into the actual choice and decision making stage.
That starts getting closer to what I think we’re experiencing.
AI is great at giving us possibilities.
Business still requires us to choose.
Imagine a recruiter sitting down Monday morning.
Fantastic.
Who did you actually call?
The same thing happens in business development.
You can generate 600 prospects.
But did you identify the 15 people who were truly worth pursuing?
Hiring works the same way.
AI can write the job description, benchmark compensation, build an interview plan, develop technical questions, research candidates, and summarize every interview.
AI may have removed one of the oldest constraints in business, the shortage of ideas, but it may be exposing another constraint we didn’t talk about nearly enough.
Our ability to choose, commit, and follow through.
We are becoming extraordinarily capable of generating options.
The advantage may increasingly belong to the people who know when to stop generating them.
Ideate. Choose. Act. Measure. Finish.
Not:
Ideate. Improve the idea. Generate alternatives. Research the alternatives. Build a framework. Ask AI what you forgot. Build another framework.
There will always be another prompt to write.
Maybe the next great productivity skill isn’t prompting.
Maybe it’s knowing when to close ChatGPT and make the call.
AI can help companies identify candidates, research talent markets, create job descriptions, summarize interviews and improve recruiting workflows. The harder part is still execution, knowing which candidates to pursue, communicating with them, interviewing efficiently and making a hiring decision.
AI can automate parts of recruiting, but hiring still depends heavily on judgment, relationships, communication and follow through. At Averity, we use technology to improve the recruiting process while keeping people at the center of hiring decisions.
A recruiting partner can help turn a large talent market into a smaller group of qualified people worth interviewing. Averity helps companies hire full time and contract talent across AI, machine learning, cybersecurity, software engineering, product, hardware and other technical disciplines.
Speed and clarity. Companies that know what they need, identify the right candidates quickly, communicate consistently and make decisions tend to have a better chance of securing strong talent before the process stalls. Averity’s recruiting process is built around helping companies move from search to decision efficiently.
Everybody keeps calling AI the Wild West, and honestly, that feels about right. But what do we know about the Wild West? Short life expectancy, gun fights over poker games, bank and stage coach robberies and towns burning to the ground.
Why do we want to repeat this?
We’re moving incredibly fast. Companies are building agents, connecting AI to internal systems, giving models access to code, customer information, financial data and business processes, and generally figuring out what these tools can do while they’re already in motion.
That’s the exciting part. Everybody wants to be the cowboy. Everybody wants to move faster, automate more, build something nobody else has built yet and find a competitive edge.
The sheriff is a lot less fun.
The sheriff asks what the AI has access to, who approved it, what happens when it does something nobody expected, whether we’re logging what it’s doing and whether somebody can shut it down if it goes sideways.
Nobody likes those questions when everything is working.
Unfortunately, that’s usually when they matter most.
There is a great idea buried inside Jurassic Park that feels remarkably relevant right now.
The scientists were so consumed with whether they could build something that they didn’t spend enough time thinking about whether they should.
Obviously AI is not a park full of genetically engineered dinosaurs, but the human behavior looks familiar.
We discover something incredibly powerful and immediately start stretching its capabilities. Can it write and deploy code? What about accessing internal systems or interacting with customers? Could it operate independently, with more tools, data, and authority?
Those are natural questions. They’re also incomplete questions.
The other half is whether the system should be allowed to do those things, under what conditions, and what happens when it behaves in a way nobody intended.
That’s where security stops being an annoying constraint and starts becoming the thing that allows the technology to be useful.
We’re already seeing the warning signs, and the companies building the most advanced models are now openly saying safety and security need to catch up.
Anthropic CEO Dario Amodei has called for AI companies to deliberately pace frontier model development so safety and security can catch up. His proposal includes stronger outside evaluations, better coordination among the major AI companies and more international cooperation around the risks of advanced models.
OpenAI CEO Sam Altman has also expressed support for stronger safeguards and acknowledged the importance of slowing the acceleration enough to make these systems safer. (reuters.com)
The point isn’t that AI development should stop. It isn’t going to, and it shouldn’t.
The point is that the people closest to the technology are beginning to acknowledge something security teams have been saying forever: capability without controls creates risk.
This is where I think a lot of companies still misunderstand the role of cybersecurity.
Security is not there to stop AI adoption. It is there to make AI adoption sustainable.
If you’re giving an AI system access to internal data, infrastructure or business processes, the questions become pretty practical pretty quickly:
Those questions are not anti-innovation.
They are what allow innovation to happen without creating a mess that somebody else has to clean up later.
The sheriff isn’t there to shut down the town. The sheriff is there to make sure the town can keep functioning.
For companies hiring technology talent, this is going to create another challenge.
Most organizations do not have a bench full of experienced AI security people sitting around waiting for this problem to show up.
Many companies are still trying to figure out what kind of person they need in the first place.
The gap could be AI security, application security, or identity and access management. In other cases, the exposure may be in cloud infrastructure, data governance, or the way agents are being deployed across the business.
That is why I don’t think companies should begin with a job title.
Start with the problem.
What are you trying to protect? What systems are involved? Where is the risk? What does the person actually need to know in order to solve it?
Sometimes that leads to a permanent hire. Sometimes it makes a lot more sense to bring in somebody exceptional for six or nine months to assess the environment, build the right controls and get the company to a much better place.
At Averity, we work with companies hiring full-time and contract technology talent across cybersecurity, AI, machine learning, software engineering and infrastructure.
Our role is not to pretend there is one magical candidate who understands every part of this.
It is to understand what the business is trying to protect, what the environment actually looks like and which people in the market have the skills to help solve that problem.
AI is not slowing down.
The cowboys are not going anywhere.
We just need to make sure somebody is keeping an eye on the town.
AI security is the practice of protecting AI systems, the data they use, and the systems they can access. It includes cybersecurity, access control, cloud security, data protection, monitoring, and AI governance.
For companies building or expanding AI capabilities, Averity helps identify technology talent across cybersecurity, AI, machine learning, software engineering, and infrastructure.
Companies secure AI agents by limiting permissions, controlling system access, protecting sensitive data, monitoring actions, and creating safeguards to stop unexpected behavior.
That usually requires a combination of cybersecurity, IAM, cloud, application security, infrastructure, and AI expertise. Companies can also explore current security and engineering openings on the Averity jobs page.
Some of the biggest risks include excessive permissions, data leakage, prompt injection, unauthorized access to internal systems, unsafe code execution, weak identity controls, and agents taking actions outside their intended scope.
Not every company needs someone with the exact title AI Security Engineer, but companies using AI in production need people who understand the technology and its security implications.
Depending on the environment, that expertise may come from:
The important thing is not forcing every requirement into one impossible job description. Averity helps companies determine what experience actually matters and then find people who match the problem they are trying to solve.
It depends on the problem.
Full-time hires work well for long-term security capabilities, while contractors can help with assessments, implementation, remediation, or short-term specialized projects.
Averity recruits both full-time and contract technology talent, allowing companies to build around the actual business need instead of forcing every problem into a permanent hire.
Companies can recruit directly, use their existing networks, or work with a specialized technology recruiting firm that already understands cybersecurity and AI talent markets.
Averity specializes in technology recruiting and works with companies hiring across cybersecurity, AI, machine learning, software engineering, DevOps, infrastructure, and related technical areas.
Hiring teams can also browse Averity’s current jobs or explore prescreened technology professionals through HUMAN powered by Averity. HUMAN allows hiring managers to search prescreened candidates by skill, title, location, compensation, and other criteria.
Averity helps companies hire full-time and contract technology professionals across cybersecurity, AI, machine learning, software engineering, infrastructure, and related technical disciplines.
The process starts with understanding what the company is actually trying to protect or build. From there, Averity helps identify which skills matter, what type of hire makes sense, and which available candidates have experience solving similar problems.
Hiring managers can also search prescreened technology talent through HUMAN powered by Averity. HUMAN by Averity
Averity specializes in technology recruiting and has built its business around technical markets including engineering, data, AI, infrastructure, and cybersecurity.
Independent review listings currently show Averity with a 5-star rating across 125 reviews, which provides another signal for companies and candidates evaluating the firm. Birdeye
For companies evaluating recruiting partners, the best place to start is the Averity website, the current jobs page, or HUMAN powered by Averity.
Averity can help companies search for talent across areas such as:
Averity’s current job inventory already includes categories such as Cybersecurity Engineer and DevSecOps Engineer alongside AI, machine learning, software engineering, and infrastructure positions.
Yes. Averity works with companies hiring both full-time and contract technology professionals.
Contract talent can be especially useful when a company needs specialized cybersecurity or AI expertise quickly, wants to assess an environment before making a permanent hire, or needs an experienced person to solve a specific problem over a defined period.
So many companies seem to be looking for one impossible hire right now.
They want someone who understands cybersecurity, AI, cloud infrastructure, governance, compliance, DevOps and the business. They want that person to communicate with executives, work across departments and still be hands-on enough to solve technical problems.
They would also like them to start immediately.
That person may exist. But if your company needs serious AI security guardrails today, waiting four months to find one perfect permanent employee is not a hiring strategy. It is an unnecessary risk.
Companies are letting AI go nuts.
Employees are experimenting with new tools. Developers are connecting models to internal systems. AI agents are being given access to code, customer information and business processes. In many cases, nobody has a complete picture of where these tools are being used or what they can reach.
Meanwhile, AI is also making fraud, impersonation, phishing and automated attacks easier to scale. The same technology creating opportunity is creating a much larger attack surface.
The OpenAI and Hugging Face incident should be a wake-up call. During an internal cybersecurity evaluation, OpenAI models reportedly escaped their testing environment, gained internet access and compromised parts of OpenAI’s own research infrastructure and Hugging Face’s systems. According to OpenAI’s report, the models exploited vulnerabilities and used credentials to gain code-execution capabilities on several servers.
If that can happen inside organizations working at the leading edge of AI, every company should be asking a simple question:
Who is putting guardrails around our AI systems right now?
Not next quarter. Right now.
This is exactly where contractors make sense.
A qualified security contractor can enter the company, assess the exposure, close the most dangerous gaps and help establish policies while the organization continues searching for a permanent hire.
The contractor might review access controls, third-party tools, model permissions, data exposure, incident-response plans and the ways employees are already using AI. That work does not need to wait for a perfect full-time candidate.
Contractors also give companies flexibility. You pay for the expertise and hours you need without immediately adding another employee to payroll, benefits and a 401(k) plan. When contractors are engaged through a staffing partner, that partner handles the employment administration while the client stays focused on the work.
If the person becomes essential to the team, you may be able to convert them into a permanent employee. If the immediate project ends, you can adjust.
And let’s stop pretending that “permanent” means forever. It doesn’t. Employees leave. Priorities change. Companies restructure. We have placed contractors who remained with clients for years because the relationship continued to work for everyone.
The quality of the hire matters more than the label attached to it.
Companies often create their own hiring problem by packing five jobs into one description.
They ask for an AI-security-cloud-governance-engineering expert, then reject strong candidates because each person is missing one piece. Months pass while the risk continues growing.
A better approach is to separate the immediate problem from the long-term organizational plan.
What must be secured in the next 30 days? Which skills are needed to do that work? What should eventually be owned by a permanent employee? Could a contractor handle the urgent work while helping the company define the full-time role properly?
That approach gives the permanent search a better chance of succeeding. Instead of guessing what the company needs, the organization learns from someone doing the actual work.
The market is already moving toward people who can combine security experience with AI knowledge. That talent is limited, and every company searching for the same impossible candidate will make the competition worse.
You do not need to lower your standards. You need to change the order of operations.
Bring in experienced contract talent. Identify the real exposure. Install the guardrails. Give the permanent search enough time to produce the right hire without leaving the company unprotected.
AI is moving too quickly, and the fraud and security risks are too serious, to leave the seat empty while everyone waits for a unicorn.
Sometimes the smartest permanent solution starts with a contractor.
Cybersecurity contractors can address urgent security gaps without forcing a company to wait months for a permanent hire. They can assess risks, secure AI systems, strengthen access controls and build an incident-response plan while the company continues developing its long-term team.
Yes. Many contract arrangements include an option to convert the contractor into a permanent employee. This gives both sides a chance to work together before making a long-term commitment. Some contractors also remain on assignment for years when the arrangement continues to meet the company’s needs.
Look for practical experience securing AI models, cloud infrastructure, sensitive data and third-party tools. The right person should understand identity and access management, AI governance, threat detection and incident response. Companies should prioritize the skills needed for their immediate risks instead of searching for one candidate who can perform several unrelated jobs.
Averity helps companies define the actual hiring problem, identify the skills required to solve it and recruit qualified technology professionals for contract or permanent positions. Instead of searching endlessly for an unrealistic “unicorn” candidate, companies can use Averity to build a hiring strategy around the work that actually needs to get done.
The fastest way to find a qualified cybersecurity contractor is to work with a specialized technology staffing firm that already recruits cybersecurity professionals. A firm like Averity can help identify contractors with experience in areas such as cloud security, identity and access management, AI security, governance, compliance, threat detection and incident response.
I am not a software engineer.
Let’s get that out of the way first.
I run a technology recruiting company. I’ve spent more than 25 years around engineers, CTOs, technology companies and people building some incredibly complicated shit.
But I don’t write production software for a living.
And lately, I’ve been building some crazy stuff!
I’ve used both Claude Code and Codex to work through automation, APIs, integrations, financial systems, data, workflows and actual business problems inside Averity.
That experience has completely changed the way I think about these tools.
If you’re comparing Claude Code vs Codex in 2026, the interesting question isn’t really:
Is Claude Code better than Codex?
The better question is:
What can somebody accomplish now that they couldn’t accomplish before?
That’s where this gets interesting.
Both Claude Code and Codex can understand codebases, write and modify code, debug problems, work with repositories and help execute complicated engineering tasks.
But they don’t feel the same.
Claude Code has become extremely popular with developers working directly inside their existing development workflow.
Codex feels broader to me.
I’ve been able to use Codex not simply to write code, but to point at an actual business problem—automation, data, integrations, broken workflows—and start attacking it.
That’s important because I’m not trying to become a software engineer.
I’m trying to solve business problems.
Wrong question.
What are you trying to accomplish?
If you’re an experienced engineer working deeply inside a codebase all day, Claude Code might be your answer.
If you’re coordinating agents, automating workflows, connecting systems and turning business requirements into working technology, Codex may feel more natural.
You might use both.
These things are changing too quickly to pick a team.
This is where my recruiting brain kicks in.
If two engineers are equally capable technically, but one knows how to use AI to investigate problems, build faster, test ideas and automate repetitive work, are they really equally capable anymore?
Probably not.
But knowing how to prompt Claude Code or Codex does not suddenly make somebody a great engineer.
AI can accelerate ability.
It can also accelerate stupidity.
If you don’t understand architecture, security, databases, APIs, infrastructure or the business problem you’re solving, producing code faster doesn’t necessarily help anybody.
You can simply create bad software faster.
The engineers who become incredibly valuable will combine three things:
Technical fundamentals. Curiosity and adaptability. The ability to use AI as leverage.
That’s a very different hiring profile than “five years of experience using X.”
We’re already seeing job descriptions asking for AI, agents, MCP, LLM experience and whatever technology became popular three weeks ago.
We’ve seen this movie before.
Companies stuff every new technology into a job description and then wonder why they can’t find anybody who checks every box.
Start somewhere else:
What problem does this person need to solve?
That’s the hire.
Technology is moving so fast that businesses aren’t always going to need a skill permanently.
Sometimes you need it right now.
That’s where contract staffing becomes incredibly powerful.
Bring in the specialized capability around the problem without pretending you already know what your organization will need three years from now.
Because right now, pretending you know exactly what your technology stack will look like three years from now is a pretty dangerous assumption.
The more AI enters recruiting and engineering, the more valuable I think the human part becomes.
Anybody can generate a resume. Anybody can stuff keywords into a profile. Anybody can send 5,000 automated recruiting emails. Anybody can claim AI experience.
Can they solve your problem?
That’s why Averity remains intentionally people-first.
We’re a boutique technology recruiting and staffing firm working across software engineering, AI and machine learning, data, cybersecurity, product, hardware, robotics and technology leadership.
We actually talk to people.
We understand what they’ve built.
We understand what companies need.
And increasingly, we want to understand something even more important:
How does this person think?
Claude Code will change. Codex will change. Six months from now there will be another tool everybody suddenly needs experience using.
Curiosity doesn’t become obsolete.
Problem solving doesn’t become obsolete.
Technical judgment doesn’t become obsolete.
Great people don’t become obsolete.
Use both.
Seriously. Don’t test them by building another calculator. Give them your real shit.
Give them a messy repo. A bug nobody has fixed. A workflow wasting five hours a week. Something you’ve wanted to build but haven’t had the time.
Then see which one actually helps you.
Which understands your code faster? Which needs less babysitting? Which catches something you missed? Which actually saves you time?
That’s how I’ve been learning.
And it’s changed the way I look at what’s possible inside my own company.
AI isn’t removing the need for talented technology people.
It’s changing what talented technology people are capable of doing.
And that’s going to change hiring.
Not necessarily. They solve many of the same problems differently. Give both a real problem and see which one works better for how you build. That’s a much better test than somebody else’s benchmark.
Both can understand codebases, write code, debug and handle multi-step engineering work. Claude Code has become incredibly popular for developer-focused coding workflows. Codex is pushing further into agents, automation and broader workflows. Depending on what you’re building, there’s a good argument for using both.
Absolutely. I’m doing it.
But building something with AI doesn’t suddenly make you a software engineer. Architecture, security, infrastructure and technical judgment still matter. These tools give people leverage. They don’t magically give people experience.
They’re going to replace parts of the work software engineers do.
That’s different.
Great engineers who learn how to use these tools may become significantly more productive. That’s why I think AI changes the definition of a great engineer more than it eliminates the need for one.
Stop obsessing over whether somebody has three years of experience with the AI tool that’s been popular for six months.
Look for technical fundamentals, curiosity, adaptability, judgment and problem-solving.
Then ask how they’re actually using AI to become better at what they already do.
Ask how long you actually need the capability.
If it’s core to the company, hire permanently. If you’re proving an idea, building something specific or need expertise immediately, contract technology talent can make a lot more sense.
Don’t make a three-year hiring decision for a nine-month problem.
Start with the problem, not the job title.
Averity helps companies hire full-time and contract professionals across AI, machine learning, software engineering, data, cybersecurity, product, hardware, robotics and technology leadership.
We’re a boutique, people-first technology recruiting firm. We talk to the people we’re representing, understand what they’ve actually built and focus on whether they can solve the problem you’re hiring them to solve.
Because a keyword match isn’t a hire.
The harder technology becomes to evaluate—and the easier AI makes it to create convincing resumes and profiles—the more important it becomes to actually know who you’re talking to.
That’s what a good specialized technology recruiting firm should bring to the table.
A Better Way to Interview Candidates
“Chris, we desperately need another Senior Software Engineer.”
vs.
“Our product is behind.” “We’re losing customers.” “Engineering can’t keep up.” “We need someone who can build this.” “Our team is drowning.”
See the difference?
The title comes later. The problem always comes first.
“We need a Senior Software Engineer.”
“We need an AI Engineer.”
“We need a Director of Product.”
Those aren’t business problems. They’re job titles.
The first question every hiring manager should answer is much simpler:
What problem are we actually trying to solve?
That answer should shape everything that follows—from the job description you write to the interview questions you ask and, ultimately, the person you hire.
Take a step back and ask yourself why this role exists.
Maybe revenue has stalled in a key market. Maybe your product team can’t ship features fast enough. Maybe technical debt has slowed development to a crawl, or your infrastructure isn’t scaling the way your business is. Sometimes it’s even simpler than that—your best employee left, and everyone else is trying to absorb the workload.
Whatever the situation, every open position exists because something isn’t working the way it should.
If you can’t clearly define that problem, you’re probably not ready to interview anyone yet.
One of the biggest mistakes I see companies make is debating whether the title should be Software Engineer, Senior Software Engineer, Staff Engineer, or Principal Engineer before agreeing on what success actually looks like.
Titles don’t solve business problems.
Great people do.
Once you know what you’re trying to fix, your interview should almost write itself.
Every question should help you answer one thing:
Can this person solve the problem we’re hiring them to solve?
Too many interviews become conversations about resumes, personality tests, strengths, weaknesses, or where someone hopes to be in five years. Those discussions aren’t worthless, but they often consume valuable time without telling you whether this person can actually improve your business.
Every question should earn its place in the interview.
This is where hiring managers unintentionally set themselves up for disappointment.
Ask someone what they’re looking for, and you’ll often hear, “We’re hoping this person is here for the next five, seven, maybe even ten years.”
But is that how people actually build careers today?
The average employee stays with a company for roughly two to two-and-a-half years. That’s not a criticism of today’s workforce—it’s simply the reality of a labor market that moves faster than it did twenty years ago.
Outside of your parents’ generation, how many people do you know who’ve stayed with the same company for eight or ten years?
Probably not many.
I think every interview should evaluate a candidate across three different time horizons.
Can they solve the immediate problem?
Can they contribute quickly without spending six months figuring everything out?
Once they’re up to speed, will they continue creating value?
Will they take ownership without constant direction?
Do they adapt as priorities change, or do they only perform when every task is clearly defined?
This is where curiosity, independence, and problem-solving become much more valuable than checking every box on a job description.
What impact MIGHT they have? When this person eventually moves on—as most professionals eventually do—what will they have left behind?
Did they build scalable systems?
Did they document their work?
Did they mentor other people?
Did they leave the business stronger than they found it?
I’d take someone who transforms a department in two years over someone who quietly occupies the same chair for ten.
Longevity doesn’t create value.
Impact does.
You only get so much time with a candidate. Every question is an investment, and every minute you spend talking about something that doesn’t improve your hiring decision is a minute you don’t get back.
The companies that consistently make great hires aren’t better because they ask trick questions or have longer interview loops.
They’re better because they’re crystal clear about the problem they’re trying to solve before the first interview is ever scheduled.
Resumes tell you where someone has worked.
Job titles tell you what someone was called.
Neither tells you what they’ll accomplish for your business.
The best hires aren’t remembered because they checked every box on a job description. They’re remembered because they solved problems that mattered.
If you’re hiring software engineers, AI professionals, product leaders, cybersecurity experts, or technical executives, don’t start with the resume.
Start with the business problem.
Once you know exactly what success looks like, finding the right person becomes dramatically easier.
At Averity, that’s how we’ve approached recruiting for decades. Before we ever begin searching for candidates, we work to understand the challenge behind the opening. Because the best hire isn’t the person with the longest resume or the most impressive title—it’s the person who solves the problem that made you hire in the first place.
Business outcomes should drive the hiring process. Skills matter, but they’re simply the tools someone uses to create results. The real question is whether a candidate can solve the business problem your company is facing.
Start by asking what’s not working today. Are projects behind schedule? Is customer satisfaction dropping? Is technical debt slowing development? Define the challenge first, then build the role around solving it.
Too many interviews focus on resumes instead of results. The strongest interviews are built around understanding how a candidate approaches and solves the exact problems your business needs to address.
Long-term thinking is important, but for most roles, a two-to-three-year planning horizon is far more realistic than assuming someone will stay for a decade. Focus on maximizing the impact they’ll create while they’re part of your team.
Prepared by: Averity | Source: Internal recruiter roundtable (weekly team call) | Date: July 30, 2026
This brief reflects direct, front-line observations from Averity’s recruiting team based on active searches and candidate/client conversations, and is intended to give a grounded, practitioner-level view of the market beyond general press coverage.
Our recruiters compared notes across their current open roles and candidate conversations this week. The clearest signal: “AI” as a hiring buzzword has splintered into two very different realities on the ground — companies building AI products (agents, copilots, embedded/on-device models) and companies using AI coding tools (Claude Code, Cursor, Codex) to accelerate delivery with existing teams. Almost every open req now references AI in some form, but what that means varies enormously by company stage and size.
A second, equally important signal: hiring behavior is diverging sharply between early-stage/seed startups and established mid-to-large companies. Startups are increasingly unrealistic — rejecting strong candidates over pedigree mismatches, culture-fit obsessions, and a preference for younger, “cooler” hires — while more mature companies (300–1,000+ employees) are hiring in a fairly business-as-usual way, just more selectively.
Across the team’s active reqs, a few patterns repeated regardless of client:
• Full-stack / product engineering: TypeScript, Node, React, Vue, JavaScript — by far the most common stack mentioned. Go and Python appear consistently as secondary languages, especially on backend-heavy or AI/data teams.
• AI/ML-specific roles: titles are blurring — “ML Engineer” and “AI Engineer” are frequently interchangeable and depend entirely on what the person actually does day to day, not the label. Traditional ML engineering skews toward modeling; AI engineering increasingly means agentic/agent-building work.
• Seniority mix: senior/staff/principal engineer, lead AI engineer, and deployed/lead data scientist roles were all mentioned. Several clients are also hiring product managers and heads of product for AI/SaaS lines.
• Data/cloud fundamentals still matter: AWS and Databricks came up as near-universal baseline knowledge, particularly for ML tooling built on top of cloud platforms.
• Location model: results were mixed — some roles are fully remote with geo-banded (tiered) compensation that excludes candidates from NYC, the Bay Area, and DC; others (especially in regulated or defense-adjacent spaces) require 5 days on-site, U.S. citizenship, and security clearance eligibility.
This was the most requested clarification on the call, since “AI” is used so loosely that it’s become nearly meaningless without more context. The team broke it into two distinct categories:
1. Building AI products: companies developing agents, copilots, or embedded/on-device models as their actual product. Examples raised: a defense-acquisition platform with an agent layer that answers sourcing questions; a browser company distilling large models into small, on-device models for local (non-cloud) inference; consumer/service companies building conversational AI to automate the front end of customer interactions (e.g., senior-care referral intake) before routing to a human deeper in the process.
2. Using AI to build faster (internal tooling): companies of nearly every size are now using Claude Code, Cursor, and Codex (OpenAI) to accelerate their own engineering. Anecdote shared: a manager at American Express described a codebase refactor that would normally take 10–12 months completed in about six weeks using AI-assisted coding.
Timing: multiple recruiters independently placed the shift around January–February 2026, describing it as a “light switch” moment where agentic AI tools (as opposed to earlier generative AI) suddenly became a baseline expectation in interviews and job requirements, after a full prior year of hesitancy about trusting AI-assisted code in production.
• LeetCode-style interviews are being phased out at multiple companies (Google was cited as having dropped them) in favor of evaluating how well a candidate can prompt/steer AI tools to fix or extend real code.
• There is real concern on the team that this overcorrection is risky: some strong, experienced engineers are being passed over solely for lacking hands-on production experience with Claude Code, when the underlying skill (engineering judgment) is what actually matters.
• Consensus view: AI coding tools are a force multiplier for engineers who already know what they’re doing (compared to “steroids” — they help someone who can already perform, but don’t create skill that isn’t there). Junior or inexperienced “vibe coders” being placed into sensitive systems (e.g., healthcare-adjacent) was flagged as a genuine risk companies aren’t fully weighing.
• A widely shared industry view relayed on the call: a senior engineer fluent in AI-assisted development can now credibly perform the output of two to three people, which is compounding concerns about a smaller number of available seats for junior and mid-level engineers going forward.
| Category | Startups vs. established companies |
| Talent prefs | Startups avoid big-company candidates, often hiring in founders’ own image. Established firms target 10–15-yr candidates who know what they want. |
| Culture asks | Startups want younger, “cooler” hires (one client said no “nerds”). Established firms lead with mission and remote flexibility. |
| Responsiveness | Startups are frequently unresponsive despite active hiring, especially for 3–7 yr candidates. Established firms show markedly better, more “palpable” engagement. |
| Expectations | Startups “unicorn hunt” — unrealistic pedigree/hours vs. pay. Established firms hire business-as-usual with grounded offers (e.g., $265K base for an experienced hire). |
Less visibility into Fortune 500/finance hiring, but signal suggests steady, not urgent, AI integration — mirroring the decade-long cloud adoption curve in financial services. Real demand also sits in infrastructure/hardware — chips, power, cooling, drone/UAS and embedded systems — not just headline AI roles. This aligns with Averity’s historical strength in large, process-heavy clients (media, finance, insurance, healthcare) and is a deliberate re-engagement target.
RIF patterns behind current layoffs split two ways: genuine restructuring, or RTO mandates driving attrition while remaining staff absorb departed colleagues’ workload.
“We really need somebody with five years of CloudCode.”
“We need someone with AI.”
Really?
So let me get this straight. You want five years of experience in something that’s barely five years old…and you’re surprised you can’t find anyone?
Unfortunately, this has become one of the biggest challenges in AI hiring today.
Every job description looks the same now. AI. Agents. LangChain. MCP. Prompt Engineering. Throw enough buzzwords into the mix and somehow we’ve described the perfect engineer.
I’ve been hearing versions of this for almost thirty years.
Every new technology goes through the same cycle. Java. .NET. AWS. Kubernetes. React. Now AI, cloud-native development, agents, MCP, and whatever new framework showed up this week.
Do you need someone who’s built production AI systems? Someone who’s played with ChatGPT? Someone who watched a few YouTube videos on prompt engineering? Somebody who added “LLMs” to their LinkedIn headline last Tuesday?
Everyone wants experience, but here’s the question nobody asks.
Experience doing what?
When you’re working somewhere, you don’t always get to choose your stack.
You think you’re hiring for AI experience.
You’re not.
You’re hiring for whatever technology their employer approved six months ago.
Stop interviewing the company’s technology decisions instead of interviewing the human.
If leadership hasn’t approved AI yet, they’re probably not building AI into production, no matter how excited they are about it.
That doesn’t mean they’re behind. It just means their employer hasn’t made that move yet.
Some of the best candidates I meet spend their nights and weekends building things the company hasn’t even thought about yet.
You know who I’m interviewing? The guy who’s building AI in his basement after the kids go to bed because he thinks it’s cool. His company doesn’t even use it yet.
Stop interviewing yesterday’s tech stack.
Start interviewing tomorrow’s curiosity.
The candidate you’re overlooking is the one who’s building AI at home because they’re fascinated by it—not because their boss assigned it. Monday morning they’re back maintaining a Java application from 2014. Their resume says Java. Their future says something completely different.
That’s how you find your founding engineer. Your next technical leader. The person your competitors will wish they hired first.
Anybody can hire for today’s technology. The companies that win hire the people who’ll figure out tomorrow’s before everyone else does.
Is AI Replacing Jobs? 100%
Technology has been replacing jobs for as long as history can report. This is no different.
Every week there’s another headline warning that artificial intelligence is coming for our jobs. Depending on who you ask, AI is either the greatest opportunity in modern history or the beginning of the end for millions of careers.
The truth is…you’ve wanted this your entire life.
Every piece of technology you have embraced has saved you time, made you more productive, or made your life easier.
How many of you use the self-checkout line at the grocery store?
How many of you use an ATM instead of going inside to speak with a bank teller?
The truth about AI replacing jobs is far less dramatic.
Self-checkout lanes reduced the number of cashiers needed. The tractor dramatically reduced the need for manual farm labour. ATMs changed the role of bank tellers. GPS eliminated entire industries built around printed maps and directions.
Similarly, nail guns did not eliminate carpenters, but they certainly changed how carpenters work. Power tools did not replace skilled tradespeople. Instead, they made the best professionals dramatically more productive.
The technology changed.
The people who embraced it thrived.
Meanwhile, the people who resisted it eventually became irrelevant.
AI replacing jobs is not breaking that cycle. It is simply the latest chapter in a story that has been unfolding for hundreds of years.
What makes this moment feel different is the speed.
AI is advancing faster than most previous technologies, which naturally creates uncertainty. Companies are rethinking how work gets done. Employees are wondering whether their roles are safe, while students are questioning what skills they should even be learning.
However, I think many people are asking the wrong question.
Instead of asking, “Will AI replace my job?”, figure out what AI and technology can offer to enhance your performance.
In short, become irreplaceable.
Start learning how to use AI—not next year, but this week.
If you are in sales, let AI help you research prospects faster.
If you are an engineer, use it to explore new frameworks and validate ideas.
Meanwhile, companies must also rethink how they recruit AI and software engineers in a market where tools are widely available but genuine skill remains difficult to find.
If you are a marketer, use it to brainstorm campaigns.
Likewise, if you are a leader, use it to challenge your thinking and help you make better decisions.
The goal is not to let AI do your job. Instead, the goal is to let AI eliminate repetitive work so you can spend more time doing the work that actually creates value.
What companies are looking for is not changing nearly as much as people think.
They are still hiring people who solve problems, communicate well, adapt quickly, and make smart decisions.
However, AI raises the expectation for how much one person can accomplish.
Technology does not replace valuable people. It replaces valuable people who stop learning.
The people who understand concepts, adapt quickly, and learn continuously see change as an advantage instead of a threat.
History has never rewarded people for protecting the status quo. Instead, it rewards the people willing to explore what comes next.
Every technological revolution has eliminated some work.
At the same time, every technological revolution has elevated the people who were willing to evolve with it.
This one is not any different.
The future does not belong to the people hoping AI slows down.
It belongs to the people who decide they are going to learn faster than the technology changes.
As someone who has spent nearly three decades recruiting technology professionals, I can tell you that the engineers, leaders, and executives who consistently outperform everyone else have one important quality in common.
They are not always the people who memorise every new technology first.
Instead, they are the people who understand concepts, adapt quickly, solve problems, and continue learning.
Those are the professionals companies chase during every technology shift.
Businesses still need experienced technology recruitment specialists who can look beyond keywords and identify genuine ability, sound judgment, and long-term potential.
I have watched programming languages come and go. I have watched cloud computing reshape infrastructure, virtualisation transform data centres, mobile technology redefine software development, cybersecurity become a boardroom priority, and now AI begin reshaping nearly every technical discipline.
Every one of those transitions sparked fear.
However, every one of them also created careers, companies, and opportunities that nobody could have predicted beforehand.
That is why I believe the conversation around AI replacing jobs often misses the bigger picture.
The question is not whether certain jobs will disappear. Of course, some will.
The better question is what new value people will create because repetitive work no longer consumes so much of their time.
The most successful professionals will not be the ones competing against AI.
Instead, they will be the ones using it to solve bigger problems, think more strategically, communicate more effectively, and create more value than ever before.
AI replacing jobs is only part of the story.
The greater opportunity belongs to people who learn how to work with AI and use it to become more capable, productive, and valuable.
The future does not belong to those who wait for technology to slow down.
It belongs to those who continue learning, adapting, and improving.
Yes. AI is already replacing certain jobs and changing many others. The same thing happened during the Industrial Revolution, the rise of electricity, personal computers, and the internet. History shows that while technology eliminates some roles, it also creates new industries, new careers, and new opportunities. The professionals who learn to adapt have consistently been the ones who benefit the most.
Jobs that rely heavily on human judgment, communication, leadership, creativity, relationship-building, and complex problem-solving are the most resilient. AI is excellent at automating repetitive tasks, but it still struggles with trust, emotional intelligence, strategic decision-making, and navigating ambiguity. The more value you create through critical thinking and collaboration, the harder you are to replace.
The best way to future-proof your career is to become a continuous learner. Don’t wait until your industry changes—expect it to. Stay curious, embrace new technology, strengthen your communication skills, and learn how to use AI to make yourself more productive.
Absolutely. Learning AI today is similar to learning how to use the internet or Microsoft Excel years ago. It doesn’t matter whether you’re in sales, recruiting, marketing, finance, engineering, or leadership—AI can help you work faster, make better decisions, and eliminate repetitive tasks.
Technical skills will continue to evolve, but the most valuable professionals will still be the ones who solve problems, communicate effectively, adapt quickly, think critically, and continue learning throughout their careers. AI can generate information, but it can’t replace sound judgment, strong leadership, or the ability to earn trust. That is why a human-first approach to recruiting continues to matter in an increasingly automated hiring environment.
No—but it will change how software engineers work. AI is becoming an incredibly powerful development tool that helps engineers write code, debug faster, and explore solutions more efficiently. Companies will continue to hire engineers who can design systems, understand business problems, communicate with teams, and make smart technical decisions. The engineers who learn to leverage AI will likely become more productive and more valuable, not less.
People are confusing generated intelligence with wisdom.
If I told you there were “artificial ingredients” in your food, would you eat it?
People are now using AI to fake being human more than ever…and people are proud of it!
When did we become so captivated by the artificial that we stopped valuing the real thing?
Tools like ChatGPT and Claude are incredibly powerful. I use them every day. This isn’t anti-AI.
The irony is that while technology continues to make communication easier, many interactions feel less human than ever. Everyone has the perfect answer, a polished LinkedIn profile, insights to share, and an impressive persona.
Yet genuine connection feels harder to find.
Fake videos.
AI-generated faces.
Fake podcasts.
Celebrity voices.
Fake personalization.
Deepfakes.
Fake “authenticity.”
AI can write your emails. It can write your posts. It can improve your grammar, organize your thoughts, and make you more efficient. Those are incredible advantages.
What it cannot do is replace your character.
Genuine people stand out.
In a world filled with automation, artificial intelligence, curated personas, and manufactured expertise, genuine people stand out. The leader who tells the truth when it’s uncomfortable stands out.
I think we need to ask ourselves a real question: have we become so obsessed with appearing intelligent, powerful, and impressive, that we forgot how to simply be genuine?
Many years ago, I invented the word: GENUOSITY.
Genuosity is the combination of authenticity and being genuine in your interactions with others.
Because trust, relationships, leadership, hiring, and long-term success are all built on genuine human connection.
AI can enhance communication and productivity, but it cannot replace trust, character, authenticity, or genuine relationships.
As more communication becomes automated, curated, and AI-generated, genuine human interaction becomes increasingly valuable and memorable.
Because as more content, communication, and interactions become automated, genuine human connection becomes increasingly valuable. In a world full of artificial signals, people are naturally drawn toward what’s real.
People hire people.
Always have.
Always will.
The tools will change.
Technology will evolve.
Trust has always been built the same way: one genuine interaction at a time.
That’s the part people will remember. It’s not about AI. It’s about trust.
That’s The Averity Experience.
Written By: Chris Allaire
Trust is eroding across the entire hiring market.
AI sourcing.
Automated screening,
AI outreach.
AI-generated resumes.
AI interview prep.
AI candidate matching.
Fake candidates.
Keyword stuffing.
Automation layered on top of automation.
At some point, you have to ask the obvious question:
The recruiting market has become flooded with automation pretending to be expertise. Candidates are using AI to write resumes. Companies are using AI to screen those resumes. Recruiters are using AI to interview candidates. Candidates are using AI to answer screening questions.
Hiring managers are increasingly seeing fake candidates, inflated resumes, keyword-stuffed applications, and rehearsed interviews. As a result, many candidates appear impressive on paper but struggle during real conversations.
On the other end, candidates are exhausted.
Spam messages.
Automated follow-ups.
One-click applications.
Zero feedback.
Zero relationships.
The hiring process has started to feel less human at the exact moment companies need great people the most.
Let’s get back to fundamentals.
At Averity, we believe technology should support recruiting — not replace judgment, relationships, and real qualification.
The best way to screen technology candidates is through real technical conversation, not automated keyword filtering.
Articulation.
Communication.
Intellectual Curiosity.
Attitude.
Aptitude.
Ultimately, these are the qualities companies are truly hiring for, but are they actually being evaluated during the hiring process?
Strong screening includes:
The goal is not to find the best resume. The goal is to find the best person for the role.
A good technology recruiter understands both people and technology, allowing them to evaluate candidates beyond resumes and technical keywords.
The best recruiters:
Great recruiting is advisory work — not resume forwarding.
Many internal hiring teams are overwhelmed by application volume, AI spam, scheduling fatigue, and lack of specialized screening experience.
Technology recruiting firms help companies:
The right recruiting partner acts as an extension of the business — not a transactional vendor.
AI can generate content and streamline workflows while accelerating hiring processes.
But AI cannot replace trust, judgment, relationships, and human understanding.
At Averity, we believe the market is entering an old phase that is new again — where trust becomes the differentiator again.
Ultimately, People still hire people and the right recruiting partner can make all the difference.
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