Signal intelligence · Forecasting
Forecast the talent market before it moves.
Predictive talent mapping, data synthesis and trade-offs, where AI sits in the hype cycle right now, and what the signals I track mean for the AI and energy industries.
Systems-based talent strategy and operations leader with 12+ years in technical recruitment, founder-operator and former CGO. I apply Lean principles across departments, removing waste from hiring and making the tradeoffs between speed, quality and cost explicit.
I operate as a player-coach and lead with predictive talent mapping: mapping the people you will need 6 to 18 months before the req opens, hands on the work, while coaching founders, executives and hiring managers to act on the signals first.
Predictive talent mapping
Know what you're up against before the req opens.
Every role sits inside a market, whether it's an engineer, a plant manager or a CFO. Predictive talent mapping shows you who can actually do the job, where they sit today, who else is chasing them and what it will take to move them.
Who exists
The real talent pool, not the applicant pool. Who has done this work, at what scale, and where they are now.
Who's competing
Which companies are raising, acquiring or reorganizing for the same people, and how fast they're moving.
What it costs
Comp bands, counter-offer risk, and the cost of the empty seat while the role stays open.
When to move
Flight-risk triggers: acquisitions, leadership changes, restructures and layoffs that put talent in play.
Predictive talent mapping 6 to 18 months ahead turns hiring from reactive to planned: shorter time-to-fill, fewer bottlenecks and a leaner organization that isn't scrambling every time someone resigns.
Data synthesis and trade-offs
Synthesis over noise. Trade-offs made explicit.
Data on its own is noise. The value is in pulling different signals together, testing them against each other, and being honest about what each decision costs.
Collect
Funding rounds, acquisitions, contracts, executive moves and filings across 113 industries.
Normalize
Same window, same filters every time. Raw counts and company counts, not just percentages.
Cross-check
Test against lagging public data like BLS and StatCan to measure how far ahead the signal is.
Decide
Name the trade-off (speed, depth or cost) and the confidence level behind the call.
The AI hype cycle
From "can we build it?" to "can we account for it?"
Gartner's 2026 research says the defining AI question has shifted from whether a company can build with AI to whether it can account for what that AI does and what it costs. Here's where that puts us.
Placed in the Trough of Disillusionment in Gartner's 2025 cycle. Concerns have moved from model capability to reliability and return on investment.
At the Peak in 2025. Gartner finds only 17% of organizations have deployed agents, more than 60% expect to within two years, and over 40% of agentic AI projects will be cancelled by the end of 2027.
The control layer: governance, AI observability and compute-cost accountability. Compute pressure keeps rising, and that lands on the grid.
What it means for AI and energy
One market cools. The other gets squeezed.
My read of the signals, set against public research. A developing view, not a verdict.
From builders to operators
- Hiring shifts from building models to running them: governance, observability, AI FinOps and integration.
- Agent projects that can't show value get cut, so expect uneven, choppy AI hiring rather than a straight line up.
- AI/ML is still #1 on signal volume in my data, so the competition for proven operators stays intense.
Compute lands on the grid
- The IEA expects data-centre electricity use to more than double to about 945 TWh by 2030. In the US, data centres account for nearly half of electricity demand growth.
- Energy growth in my signal is acquisition-led right now. Consolidation means integration, overlapping roles and flight risk: a retention problem before it's a hiring problem.
- Power engineers, grid specialists and electrical trades get pulled toward AI infrastructure.
How I forecast
Pre-registered rules. Receipts kept.
I don't move the goalposts. Before calling whether energy is overtaking AI in signal growth, I set the rules on October 23, 2026, before the data came in. Here's where the call stands.
30-day signal growth: Energy +20.5%, AI +18.1%. AI still #1 on volume.
Same window, same filters. Two in a row makes it a developing call.
Earliest possible confirmation date.
Anomaly tracking
I track anomalies, because that's where the edge is.
Deceleration under growth
Week 2: Energy +18.6% and AI +18.0%, but both are slowing (growth pace down 16.2% and 14.9%). Headlines see growth. The signal says momentum is fading.
Same number, different story
Energy's growth is coming from acquisitions, not new builds. Identical headline growth can mean very different talent consequences.
Signals vs official data
When filings and the official labour numbers disagree, that gap is where lead time lives. I measure it every month against BLS and StatCan.
Sectors moving against the pack
A sector that contracts while its neighbours grow often signals restructuring, and talent about to come into play.
Sources: Gartner, "Hype Cycle for Artificial Intelligence, 2025" (press release, Aug 5, 2025) and Gartner 2026 Hype Cycle for AI research (Oct 1, 2026); IEA, "Energy and AI" (2025). Hype Cycle is a trademark of Gartner, Inc. The curve above is a simplified illustration, not a Gartner graphic. Signals are drawn from public filings and announcements and tracked as they're published. They show direction, not certainty, and are meant to inform decisions, not replace them. Nothing here is investment advice.