Articles - Quarterly

Subject: Ins7ghts: Q1 2026 - The Illusion of Progress

Friends,

Let's cut the crap. Q1 2026 was not about groundbreaking innovation. It was about the slow, painful realization that the data industry is finally grappling with the consequences of its adolescent recklessness. We're paying the piper, and the tune is regulatory compliance.

The numbers don't lie. "Regulatory Compliance" dominated the conversation with 130 mentions, dwarfing even the perennial hype around "Competitive Landscape" (107 mentions). But here's the kicker: the compliance articles weren't about new regulations. They were about companies scrambling to understand and implement existing ones, primarily the expanded GDPR and the US's increasingly aggressive CCPA enforcement.

The Illusion of Progress:

We're celebrating "AI in Healthcare" thanks to the Gates Foundation, while simultaneously drowning in articles about "Data Governance Challenges." This is the illusion. We're building faster cars, but the roads are crumbling beneath us.

Hot Take: The "Competitive Landscape" articles are mostly noise. Yes, AI Lab Humans& raised a huge seed round. Yes, Lemong is trying to "enhance" restaurants with AI. But these are isolated incidents. The real competition is for talent who understand both the technology and the regulatory landscape.

The Nvidia Elephant: Nvidia's investment in Humans& is the real story here. It signals a strategic shift towards funding entire ecosystems rather than just individual startups. This is a power play to control the future of AI development, and everyone else is just playing catch-up.

The D&I Mirage: The paltry 41 mentions of "Diversity and Inclusion" are frankly embarrassing. Posting a "Staff Data Engineer" job at Playstation or a "Director" role at Databricks isn't D&I. It's window dressing. We need systemic change, not performative allyship.

Advice for Q2:

1. Audit your data governance practices now. Don't wait for a regulatory slap.
2. Invest in training your team on data ethics and compliance. Technical skills are worthless without a strong ethical foundation.
3. Stop chasing the shiny object (AI) and focus on building a solid data foundation. Garbage in, garbage out, even with the fanciest algorithms.
4. Demand more from your leadership on D&I. Hold them accountable for creating a truly inclusive workplace.

Q1 2026 wasn't a leap forward. It was a wake-up call. Let's hope we're finally listening.

Yves Mulkers
Ins7ghts

Subject: Re: Ins7ghts: Q1 2026 - The Illusion of Progress

Friends,

You asked about themes. Let's be honest, the "themes" of Q1 read like a compliance checklist from 2016. Data Quality? Governance? Security? Groundbreaking. NOT. The real story is buried beneath the avalanche of vendor-sponsored "thought leadership."

The numbers don't lie, but they do need interpreting. Look at the emergence scores:

My hot take: The explosion in "Data Quality & Governance" isn't because we're suddenly good at it. It's because the GenAI honeymoon is OVER. Companies like Databricks and Snowflake promised AI-driven utopia, but now they're facing the music. Garbage in, garbage out, amplified by LLMs. Remember when everyone was obsessed with vector databases? Now they're realizing those vectors are only as good as the data they represent.

The "Collaboration & Communication" spike? That's the sound of data teams scrambling to explain why their AI projects are failing. Expect a lot more finger-pointing and CYA memos.

"Enterprise AI Adoption" being so high is a lagging indicator. It reflects the investments made LAST year, not the ROI achieved THIS year. The real story will be the Q2 numbers, when the budget cuts start hitting.

The glimmer of hope: "Agentic AI" is still showing strong emergence. This suggests that while the promise of general AI is fading, the practical application of AI agents for specific tasks is gaining traction. Think autonomous data cleaning, automated report generation, and proactive security monitoring. Companies like UiPath and even smaller startups are quietly building real value here.

My advice:

1. Stop chasing the shiny object. Focus on the fundamentals: data quality, governance, and security. Invest in tools and processes that actually improve your data, not just promise to.
2. Demand accountability from your vendors. Don't let them sell you snake oil. Ask for concrete examples of how their products will improve your ROI.
3. Embrace Agentic AI, but with caution. Start small, focus on specific use cases, and measure your results. Don't let the hype cloud your judgment.

Q1 was a wake-up call. Let's not waste Q2 repeating the same mistakes.

Yves.

Subject: Re: Ins7ghts: Q1 2026 - The Illusion of Progress

Friends,

You asked about themes. Let's be honest, the "themes" of Q1 read like a compliance checklist from 2016. Data Quality? Governance? Security? Wake me up when something actually changes.

But you also asked about key takeaways for investors. That's a different story. Because while the industry yawns, money smells opportunity.

Here's the uncomfortable truth: Q1 2026 was the quarter the illusion of AI started to crack, and the smart money is pivoting hard into the picks and shovels of the AI gold rush. Forget the flashy AI startups promising general intelligence. Focus on the infrastructure.

The Smart Building Sector: A Microcosm of the AI Shift

The Memoori report on smart building startups perfectly illustrates this. The headline is "Startup Formation Collapses While Acquisitions Hit High." Translation: The applications layer is consolidating. The low-hanging fruit is gone.

The Real Opportunity: AI Plumbing

The real story isn't the smart buildings themselves, but the AI plumbing that makes them tick. And that's where the AI Workflow Orchestration piece comes in.

Think about it: every smart building generates a torrent of data. To actually use that data for predictive maintenance, energy optimization, or even just better security, you need a robust, reliable, and automated AI pipeline. That means:

• Data Ingestion: Getting the data from sensors, systems, and legacy infrastructure.
• Model Training: Building and refining AI models that can actually make sense of the data.
• Evaluation & Deployment: Putting those models into production and measuring their performance.
• Monitoring & Retraining: Continuously monitoring the models for drift and retraining them as needed.

This is where companies like Prefect and Airflow come in. They're not building AI models themselves; they're building the tools to manage the entire AI lifecycle. And that's a much more defensible, and scalable, business.

My Hot Take:

The hype around "AI-powered everything" is a distraction. The real value lies in the infrastructure that makes AI possible. We're moving from a "build it and they will come" mentality to a "show me the ROI" reality. And the ROI is in the efficient and reliable deployment of AI.

Advice for Investors:

1. Look beyond the hype: Don't chase the latest AI buzzword. Focus on companies that are solving real-world problems with practical AI solutions.
2. Invest in the picks and shovels: The AI infrastructure layer is where the long-term value lies. Look for companies building tools for data ingestion, model training, deployment, and monitoring.
3. Due diligence is critical: Don't just take companies at their word. Dig into their technology, their team, and their track record. Ask hard questions about their data quality, their model performance, and their security protocols.

Q1 2026 wasn't about the promise of AI. It was about the reality of building and deploying it. And the investors who understand that will be the ones who reap the rewards.

Yves