Word of the year: "Slop"

Today, we’re looking at the U.S. government’s most aggressive tech hiring push in decades: a 1,000-person AI task force embedded inside federal agencies. Backed by companies like Microsoft and OpenAI. We’re also looking at Nvidia’s most strategic shift yet in the AI race: embracing open source at scale. With the surprise acquisition of SchedMD (maker of Slurm) and the launch of its Nemotron 3 model family, Nvidia is turning openness into a competitive edge and staking its claim as the infrastructure layer for AI’s future.

In today’s post:

• The U.S. just launched its biggest AI hiring spree ever

• Nvidia goes all-in on open source AI

• AI’s influence just got a new name: slop

What’s Trending Today

POLICY

Trump’s Tech Force: 1,000 AI Specialists Join the Government

The Trump administration announced a major federal tech initiative: a new “U.S. Tech Force” to ramp up AI infrastructure and digital innovation.

Here’s what’s actually happening and why it matters:

• 1,000 engineers and specialists will be hired for two-year terms inside federal agencies.

• They'll report directly to agency heads and work on AI, app dev, and data infrastructure.

• Top tech firms including AWS, Apple, Microsoft, Nvidia, and OpenAI are backing the project.

• These companies can nominate their own staff to serve in the Tech Force on rotation.

• Participants are eligible for full-time roles with the same tech giants after their term ends.

• Salaries range from $150K to $200K, aiming to rival private sector compensation.

• The move follows a sweeping executive order establishing a national AI policy framework.

This is a smart talent play wrapped in a policy push. By embedding engineers inside government, the U.S. isn’t just catching up, it’s laying groundwork for how tech and policy might finally learn to collaborate. The real test? Whether this becomes a revolving door of innovation or bureaucracy.

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BREAKTHROUGH

Why Nvidia just bought the backbone of AI workloads

Nvidia just made two big moves to deepen its grip on the AI infrastructure stack and both signal a bigger shift toward open ecosystems.

Here’s what’s actually happening and why it matters:

• Nvidia acquired SchedMD, makers of the open source Slurm workload manager used in high-performance computing.

• Slurm will remain open and vendor-neutral key to Nvidia’s push for developer trust.

• The tool is widely used in AI training pipelines and has been critical for scaling generative models.

• Nvidia also dropped Nemotron 3, its newest suite of open AI models for agentic systems.

• Models include Nano (lightweight), Super (multi-agent), and Ultra (complex tasks).

• CEO Jensen Huang says Nemotron is about “transparency and efficiency at scale.”

• These moves follow last week’s launch of Alpamayo-R1, a vision-language model for autonomous vehicles.

This isn’t Nvidia trying to catch up, it’s Nvidia shaping the rules of the next phase. By going open source, they’re locking in developers, influencing standards, and ensuring their chips stay at the center of AI’s evolution. The race now is about who powers the most experiments and Nvidia’s betting openness is the fastest route there.

RESEARCH

Slop is the Word of the Year

Merriam-Webster just named “slop” its 2025 Word of the Year and it’s not exactly a compliment to the state of the internet.

Here’s what it means:

• Slop” is defined as mass-produced, low-quality digital content, usually AI-generated.

• The term echoes the public’s growing fatigue with endless, mediocre AI output.

• From books to ads to movies, generative tools like OpenAI’s Sora and Google’s Veo flood every corner of the web.

• One study found 75% of web content in May involved AI in some way.

• A “slop economy” is emerging where bad content still makes good ad revenue.

• Critics warn this divide could split the internet into high-quality paywalls and low-quality free feeds.

• The word’s mockery not fear reflects a cultural shift in how we view AI’s role.

Slop is more than a word: it’s a signal. We’re moving past AI hype into AI exhaustion. And the question isn’t “can AI make content?” anymore. It’s: “Should it?” The answer might depend on whether we start valuing information again not just output.

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