Everyone says hiring AI talent is hard. That’s true, but the blanket statement obscures important nuances.

Some AI roles are nearly impossible to fill. Others are competitive but achievable. Understanding the distinctions helps organizations build realistic hiring strategies.

The Market Isn’t Monolithic

The “AI talent” category includes vastly different roles:

Research scientists. PhDs developing new AI approaches. This is genuinely scarce - there are maybe tens of thousands globally who can do frontier research. The big labs and well-funded startups are competing intensely for this tier.

Machine learning engineers. People who can build and deploy production ML systems. More available than researchers but still competitive. Growing supply as more engineers develop ML skills.

AI/ML product managers. PMs who understand AI capability and can drive AI product development. Supply is growing but the skill set is distinct.

Data scientists. Analysts who use ML techniques for business insights. More available than ML engineers. Significant supply exists.

AI application developers. Engineers who integrate AI services into applications. Using AI isn’t the same as building AI, and this distinction matters for hiring.

The scarcity and competition vary dramatically across these categories. Hiring strategy should too.

What’s Actually Scarce

The genuinely scarce capabilities:

Frontier research talent. People who can push the boundaries of AI capability. This is the hardest category, concentrated in few organizations, and you probably can’t compete for them unless you’re a major lab or extremely well-funded.

Senior ML infrastructure. People who’ve built ML systems at scale and know how to handle the operational complexity. These are hard to find because the experience is rare.

Domain + AI expertise. People who combine deep domain knowledge (healthcare, finance, manufacturing) with AI capability. Each individual combination is rare.

AI systems architects. People who can design full AI systems - not just models but the entire infrastructure around them.

What’s More Available

Other AI talent is more accessible:

Entry and mid-level ML engineers. There’s significant supply from bootcamps, university programs, and career transitions. Quality varies, but capable people are available.

Data scientists for traditional problems. Classic ML (classification, regression, clustering) skills are widely available.

People who can work with AI. Using AI tools, integrating AI services, prompt engineering - these skills can be developed relatively quickly by capable engineers.

AI-adjacent infrastructure. Data engineering, ML ops, and platform engineering skills are competitive but not impossibly scarce.

Hiring Strategies That Work

From organizations succeeding at AI hiring:

Unbundle the role. Instead of looking for unicorns who do everything, identify what you actually need. Maybe you need a senior architect to set direction, mid-level engineers to build, and junior people to grow.

Grow internally. Engineers can develop AI skills. Provide learning resources, internal projects, mentorship. This takes time but builds loyal, context-rich team members.

Use partners strategically. For specialized needs or temporary capacity, AI consultants Sydney and similar firms can provide expertise while internal capabilities develop.

Compete on dimensions besides compensation. Interesting problems, good culture, flexible work, development opportunities. Top AI talent has options; compensation alone doesn’t win.

Lower the bar appropriately. For roles that don’t require deep expertise, hire smart people who can learn. Not every AI role needs an ML PhD.

Be patient. Some roles take months to fill. Pipeline building takes time. Rushing leads to bad hires.

Common Mistakes

What organizations get wrong:

Conflating researcher and engineer. Research and production engineering are different skills. Researchers aren’t necessarily good at production systems. Engineers aren’t necessarily good at research.

Expecting unicorns. The job description requiring expert-level skills in five domains describes a person who doesn’t exist.

Undervaluing domain expertise. AI projects often fail not because of AI skills but because of domain understanding. Someone who knows your industry may be more valuable than someone with slightly better ML skills.

Ignoring retention. Hiring AI talent only to lose them to competitors is expensive. Retention requires ongoing attention to development, challenge, and compensation.

Waiting for perfect. The perfect candidate might not exist. A good candidate who’s available now may be better than a perfect candidate who never appears.

The Wage Reality

AI compensation is high, but there’s significant variation:

Frontier research commands millions per year at top labs.

Senior ML engineers at major tech companies earn $300K-$500K+ total compensation.

Mid-level AI roles at typical enterprises are $150K-$250K depending on location and company.

Entry level varies widely but is higher than comparable software roles.

Organizations outside major tech hubs and elite companies can’t always match top compensation. Other value propositions matter.

Building AI Capability

For most organizations, the answer isn’t hiring world-class AI researchers. It’s building practical AI capability:

Develop existing engineers. People who know your systems and culture can learn AI.

Hire application-level talent. Using AI services doesn’t require building AI from scratch.

Partner for deep expertise. Use AI consultants Melbourne or similar specialists for capabilities you can’t build internally.

Create learning pathways. Training programs, project rotations, mentorship. Build capability systematically.

Start with manageable projects. Build experience through progressively challenging work.

The Trajectory

AI talent supply will grow. More people are developing AI skills. University programs are expanding. Career transitions are happening.

Demand will also grow. More organizations want AI capability.

The balance will shift over time, but AI talent will remain competitive for the foreseeable future.

The organizations succeeding are the ones with realistic expectations, multi-pronged strategies, and patience. There’s no shortcut to building AI capability, whether through hiring or development.

But it’s achievable. The talent exists. The challenge is attracting it, developing it, and retaining it. Organizations that invest appropriately can build the AI teams they need.