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Let’s start with you. Who are you, and what problem are you trying to solve in social intelligence?
I’m Erich Giordano, Head of Business Development at Nextatlas.
The problem I focus on is simple: social intelligence is still obsessed with the present, while competitive advantage lives in the future.
Most tools are good at explaining what just happened. They help teams confirm assumptions, optimize reactions, and move slightly faster than competitors. That’s useful, but it’s not strategic.
We work on social intelligence as anticipation. Detecting cultural change before it becomes visible, before language stabilizes, before trends are named. When companies see change early, they don’t react, they choose. That time advantage is the real asset.
When it comes to social data, what do you think is still misunderstood or underdeveloped?
There’s a persistent myth that better technology automatically produces better insight.
We saw it with big data. We’re seeing it again with AI. More volume, more automation, more dashboards, but not necessarily more understanding.
Social data without strong framing is just noise at scale. Insight only emerges when data is constrained, questioned, and interpreted. Technology accelerates the process, but it doesn’t define meaning. That still requires judgment.
The future of social intelligence won’t be decided by who has the biggest model, but by who has the clearest epistemology.
What’s something you’ve seen lately, maybe a trend, a tool, or behaviour, that felt like a glimpse of the future?
A quiet rejection of the idea that effort equals value.
After a decade of optimization culture - hustle, productivity, self-tracking - we’re seeing a reversal. Ease is becoming aspirational. Low-maintenance lifestyles signal status. Time is treated less as a resource to extract value from, and more as something to protect.
This changes how brands create relevance. Performance alone is no longer enough. The brands that win will be the ones that give people time back, mentally, emotionally, socially.
If we want social intelligence to be more than a tech category, what needs to change in how we build or buy the tech?
We need to stop pretending that insight can be fully automated.
The real progress happens when we’re explicit about roles: machines scale pattern detection, humans decide what matters. When those boundaries blur, outputs become impressive but strategically empty.
Social intelligence isn’t a software problem, it’s a decision-making problem. Technology is the infrastructure, not the intelligence itself.
What’s the hardest part of turning data into action? And how do we make that easier without dumbing it down?
Finding the right balance between insights that are too generic to act on and insights that are so specific they lose strategic value.
This balance is also the core of every client conversation we have. There’s no universal “right” level of insight. That’s why we start from a robust, controlled analysis model and then create multiple delivery formats using generative systems.
That way, the insight base stays solid, but each client gets exactly the level of depth and focus they need.
What’s a quote, concept, or model you return to often when things get messy?
William Gibson’s line still cuts through everything: “The future is already here, it’s just not very evenly distributed.”
When complexity rises, that sentence is a method. It tells you where to look: not at averages, not at mainstream narratives, but at early adopters, edge communities, and uneven signals.
That’s where cultural change actually begins, and where strategic advantage is still possible.
What’s the last non-work thing you read, watched, or played that reshaped how you think?
Books, especially fiction and history, are my way of stress-testing contemporary narratives.
John Steinbeck’s “The Grapes of Wrath” reframed how I think about long-term social tension in the US. Julia Lovell’s “The Opium War: Drugs, Dreams and the Making of China” dismantled simplistic ideas of East–West history and showed how deeply entangled those stories really are.
They’re reminders that culture is always thicker, slower, and more layered than our real-time interpretations suggest.
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