The hiring bottleneck is shifting from “can we build it?” to “can we run it, power it, and evaluate it cleanly.” Two mega-rounds (Anthropic: $30B; Databricks: $5B) are going to push comp, accelerate platform roadmaps, and pull more talent into infra, reliability, and enterprise deployment. At the same time, US electricity demand forecasts and utility capex plans are making power and capacity a real constraint on AI scaling. Inside: the 3–5 stories that will actually change hiring decisions, one ATS workflow you can pilot this week, and a data-backed interview upgrade you can implement without rewriting your entire process. Listen to this edition here!

The Drop

1) Anthropic raises $30B at a $380B valuation (and says run-rate revenue hit $14B)

What happened: Anthropic closed a $30B round at a $380B post-money valuation. Reuters reports an annual revenue run-rate of $14B, with more than $2.5B attributed to Claude Code.
Why it matters for hiring: This is a talent market accelerant. When frontier labs print money like this, comp expectations rise, and everyone else has to sell mission, scope, and ownership harder.

Roles you will see ramp first:

• Applied AI engineers (coding agents, tool use, evals)

• Infra and performance (training and inference efficiency)

• Enterprise deployment (security, compliance, solutions engineering)

2) Databricks raises $5B at a $134B valuation (AI product revenue called out)

What happened: Databricks raised $5B in equity (plus $2B debt capacity) at a $134B valuation. Reuters notes a $5.4B revenue run-rate and that $1.4B is tied to AI products.
Why it matters for hiring: The “data layer for AI” is still eating budgets. Expect more hiring in data engineering that looks suspiciously like product engineering plus governance.

Roles to watch:

• Data platform engineers (lakehouse, streaming, governance)

• AI product engineers (agent workflows, retrieval, orchestration)

• Field engineering (enterprise rollouts, migrations, security review)

3) US power demand is forecast to hit record highs (AI and data centers are in the mix)

What happened: US power consumption is forecast to hit record highs in 2026 and 2027 (4,268 and 4,372 billion kWh respectively), per the EIA.
Why it matters for hiring: “We can scale the team” increasingly means “we can secure power and capacity.” Site selection, data center strategy, and infra reliability are now hiring constraints, not background noise.

Roles to watch:

• Infra reliability, capacity planning, and FinOps

• Data center ops and vendor management

• Energy procurement and sustainability roles inside large AI buyers

4) Duke Energy raises capex plans, citing data center demand (including AI customers)

What happened: Duke raised its five-year capex plan to $103B and flagged data center demand, including contracted capacity and a pipeline.
Why it matters for hiring: Utilities are becoming a bottleneck and a partner. Large employers will increasingly need people who can navigate grid constraints, interconnects, and long-lead infra timelines.

Roles to watch:

• Grid and energy program roles (inside hyperscalers and big enterprises)

• Strategic sourcing for energy and facilities

• Program managers for data center expansion

5) Entire launches with $60M seed to build an “AI developer hub”

What happened: Former GitHub CEO Nat Friedman’s new company, Entire, launched with a $60M seed (AI2 incubator and other backers) to build a developer hub for AI coding.
Why it matters for hiring: DX is the new battleground. If dev tools compress delivery cycles, hiring managers will push for fewer, stronger engineers with higher leverage.

Roles to watch:

• Developer experience, toolchain, and platform engineers

• Agent evaluation, safety, and workflow design

• Product engineers who can ship end-to-end with AI copilots

2) AI Tool of the Week

Greenhouse AI: Scorecard Attribute Suggestions

What it does: Suggests candidate “attributes” while writing scorecard feedback to make evaluations more consistent and easier to compare across interviewers.
Who it’s for: Hiring teams already using Greenhouse who struggle with vague feedback (“seems strong”) and messy debriefs.
Fast pilot (this week):

• Pick 1 role with 5+ weekly interviews.

• Standardize scorecards to 5 attributes (role-specific).

• Turn on suggestions and enforce written feedback before debrief.
  Metrics to track:

• Feedback SLA: % submitted within 24 hours

• Debrief duration: median minutes to decision

• Interviewer variance: “no hire” vs “hire” disagreement rate

• Pass-through stability: week-to-week changes in onsite pass rate

3) Hiring / Interview Insight

Structured interviews beat unstructured interviews on predictive validity

A major meta-analysis found structured interviews have higher predictive validity for job performance (0.44) than unstructured interviews (0.33). Translation: structure is not “process for process’ sake”, it’s literally better signal.

What to implement immediately:

• 4 competencies max per role (anything more becomes theater).

• Fixed questions per competency.

• Scored anchors (1–5 with examples).

• “No debrief without feedback” rule.

Benchmark to steal: If you add candidate experience surveys, Ashby reports an average 17.8% response rate across their dataset. Treat that as your “okay baseline,” then improve it with shorter surveys and faster follow-ups.

4) Funding Watch

Anthropic: $30B Series G, $380B valuation

Likely hiring first: research engineers, inference efficiency, enterprise security.

Databricks: $5B funding, $134B valuation

Likely hiring first: data platform, AI product, enterprise field engineering.

Apptronik: $520M Series A extension, about $5B valuation

Likely hiring first: robotics software, perception, embedded, safety, manufacturing ops.

Runway: $315M Series E, $5.3B valuation

Likely hiring first: multimodal infra, video systems, product + creator workflows.

Tem: $75M Series B to rebuild electricity markets using AI

Likely hiring first: energy market specialists, infra engineering, enterprise growth.

Complyance: $20M Series A (AI-native GRC)

Likely hiring first: enterprise security/GRC product, solutions, integrations.

5) Quick Bytes

• Salesforce cuts fewer than 1,000 roles as it streamlines operations. More senior talent enters the market, while “agent delivery” and efficiency keep getting prioritized.

• Tech layoffs tracker: TrueUp shows 102 layoffs impacting 39,132 people so far in 2026 (as of the tracker’s latest update). That is a lot of supply in the market, especially across enterprise software.

• Modal Labs in talks to raise $250M at a $2.5B valuation. More momentum behind “serverless AI infra” and platform teams.

• Reco raises $30M Series B for AI-driven SaaS security (agents monitoring tools like Google Workspace, Microsoft 365, Salesforce). Expect more hiring where security meets SaaS sprawl.

• Kyndryl pushes “policy as code” for agentic AI governance. Governance is becoming productized, and it creates real headcount in security and risk teams.

• Okta report flags “shadow AI” as a problem, with common unsanctioned tools showing up in orgs. Hiring will follow: detection, governance, enablement.

6) What to do this week

1) Set a 24-hour feedback SLA (then enforce it)

• Metric: % of scorecards submitted within 24 hours (target: 80%+)

• Why: Faster loops reduce candidate drop-off and stop decisions being driven by whoever talks loudest.

2) Convert one role to a structured interview loop

• Metric: onsite pass-through rate variance (week-to-week), and offer acceptance rate

• Why: Structured interviews are materially more predictive than unstructured ones.

3) Add a 2-question candidate experience survey

• Metric: response rate (baseline benchmark ~17.8%), plus “time-to-offer” median

• Why: You cannot improve what you refuse to measure.

That’s all for this week’s Tech Talent Drop - stay informed, and see you next week!