Two Decades of Deal-Making: What I Discovered Backing Into Influencer Marketing

After twenty years managing deals across music, sports media, and traditional entertainment, I thought I understood how complex negotiations worked. Then I spent seven months researching AI's impact on content creation and backed into the influencer space - reluctantly at first, then with growing fascination as I realized what I was seeing.

This industry isn't just another vertical. It's the laboratory where fundamental changes in content distribution are being stress-tested, and the insights that emerge from understanding both legacy deal-making and this new environment reveal opportunities that pure insiders and pure outsiders both miss.

So What (Basa POV)

The most valuable insights come from understanding both the legacy deal-making world and the unique challenges of algorithmic distribution. Teams that recognize these differences can build infrastructure that serves current needs while preparing for what's coming next.

How I Backed Into This World

I didn't set out to work in influencer marketing. After seven months researching AI, I had a hypothesis: AI will create orders of magnitude more content, which means orders of magnitude more deals. I was looking for industries facing that future challenge.

Originally, I built Basa to solve problems I'd experienced myself - the deal coordination chaos I'd lived through on both the talent and producer sides in music, film/TV, and audio. Twenty years of watching simple agreements take months to execute because of administrative inefficiency, not legal complexity.

But in those legacy industries, the processes were calcified by decades of history. Everyone understood the need for change, but change happened glacially. These industries were contracting, comfortable with their established inefficiencies, protected by existing relationships and guild structures.

Then I discovered teams in influencer marketing facing the exact same coordination problems I'd experienced, but at unprecedented volume. 300 creator relationships managed via email. 18,000+ messages per campaign. The future I was preparing for was already someone's daily reality.

The difference was electric: this industry was exploding, not contracting. Teams desperately needed innovation and unique solutions. There were no calcified processes protecting the status quo. No decades of "how we've always done it" to overcome.

I saw an opportunity for maximal impact on an industry and community that was ready for transformation. We pivoted the company because solving problems in a space hungry for solutions felt infinitely more compelling than trying to modernize industries comfortable with their dysfunction.

That pivot revealed something profound: the coordination challenges weren't unique to legacy media - they were the future of all content partnerships, just happening first in the most dynamic, fastest-growing part of the industry.

From Fad to Foundation: What Outsiders Miss

Influencer marketing was dismissed by many as a fad. Marketing executives rolled their eyes at "kids on TikTok" and waited for brands to return to "real advertising." As an outsider, I initially shared some skepticism.

Then I saw the budget numbers. Unilever moving from 30% to 50% of their marketing budget into creator partnerships. Not experimentation - strategic reallocation based on performance data. The pattern repeating across industries as brands quietly shifted massive budgets away from traditional media.

The fad became foundation because the underlying distribution model changed permanently. Derek Thompson's research in "Hit Makers" confirmed what I was observing: hits don't go viral organically, but through "broadcast diffusion" where influential voices amplify content to large audiences.

What outsiders miss is that this isn't about "influencers" - it's about what happens when algorithmic distribution replaces controlled scarcity as the basis for reaching audiences.

The Distribution Disruption: What Insiders Accept

In legacy media, I understood how deals worked because distribution was controlled. Record labels, TV networks, radio programmers owned the pipes. We negotiated within scarcity constraints - limited slots, finite airtime, restricted capacity.

Algorithmic distribution eliminated that scarcity while creating infinite content competition for finite attention. People are both "neophilic - curious to discover new things - and deeply neophobic - afraid of anything that's too new." Success comes from "familiar surprises" - content that's bold yet instantly comprehensible.

What insiders accept as normal would have seemed impossible in legacy media: the need to manage hundreds of simultaneous relationships profitably, coordinate across multiple time zones and currencies daily, adapt to algorithm changes that can shift entire campaign strategies overnight.

Coming from outside, I see this as extraordinary. The operational complexity is unprecedented, but teams treat it as just how things work.

The Math That Changes Everything

Here's a perfect example of why this represents a fundamental shift in how smart money gets spent. Traditional advertising: Spend $2 million on a 30-second Super Bowl ad with a big celebrity. Commit the entire budget upfront to one demographic assumption, one creative approach, one moment in time.

But Bent Flyvbjerg's research in "How Big Things Get Done" shows that 92% of megaprojects come in over budget or over schedule, largely because they commit massive resources before testing core assumptions. Less than 1% of projects deliver on time, on budget, and with claimed benefits.

Influencer marketing enables the opposite approach: Take 5% of that budget - $100,000 - and spread it across different communities of creators. Test what resonates before committing your entire budget. Maybe you thought your product would be more impactful with one demographic, but discover it's actually another. You didn't have to blow your whole budget to figure that out.

This applies Flyvbjerg's core principle: "Think slow, act fast." Spend time in low-cost planning and testing phases, then execute rapidly once you know what works. The planning phase should be low commitment where tests and experiments are cheap. The delivery phase becomes high commitment only after you've validated your approach.

It also leverages what he calls modular design. Instead of one massive bet, you create "smaller, more manageable modules that can be replicated to minimize risk." Like Tesla's Gigafactory approach - start with a few modules connected together, begin production, then scale what works.

This is just one example of how the standard model we've existed in for decades changes completely. When I say "influencer marketing" is becoming marketing, this is what I mean - it's not just a new channel, it's a fundamentally different approach to resource allocation, risk mitigation, and audience validation that makes the old broadcast model look reckless by comparison.

The math becomes obvious once you see it: Why pay Super Bowl rates to reach everyone when you can test engagement with specific communities, validate your approach with real performance data, then scale what actually works?

UGC: Production Budget Migration

Beyond marketing budget shifts, I'm witnessing something I never saw in legacy media: production budgets following the same pattern. UGC campaigns replace traditional ad agency production entirely.

Instead of spending $100,000 on a single broadcast spot, brands hire 2,000 creators at $100 each to generate localized content. Rather than one piece targeting everyone poorly, get 500 creators from specific regions creating content that targets local audiences precisely.

This represents fundamental industry restructuring. Production companies, advertising agencies, talent management - all built for high-value, low-volume transactions - now face high-volume, lower-margin, hyper-targeted community engagement.

The Infrastructure Crisis Nobody Talks About

Here's what fascinated me as an outsider: the strategic shift happened faster than operational systems could adapt. While brands reallocated budgets, they kept using email and spreadsheets designed for managing a few high-value partnerships, not hundreds of community relationships.

In legacy media, high-value deals justified extensive coordination overhead. In influencer marketing, you need to manage hundreds of micro-relationships profitably. A $100 creator deal requires the same process steps as a $10,000 partnership.

The economics work, but the execution doesn't scale. Teams are drowning not because they're inefficient, but because the infrastructure was never designed for this volume.

The Operational Advantage

I'm seeing patterns that reveal a massive opportunity for forward-thinking agencies. While brands increasingly question traditional agency margins, the agencies that solve operational efficiency first will capture disproportionate market share as the industry matures.

The challenge isn't existential - it's operational. High-volume creator campaigns require different infrastructure than traditional high-value partnerships. The agencies building this infrastructure now will handle more volume profitably while competitors struggle with email chaos and manual coordination.

This mirrors what I witnessed in music and sports media: the teams that embraced operational efficiency during industry transitions didn't just survive - they dominated. They could take on more clients, deliver better results, and maintain healthier margins because their systems scaled efficiently.

The agencies already building internal tools and investing in coordination infrastructure are positioning themselves for the volume requirements that are coming. They're not just adapting to current needs - they're preparing to handle the exponential growth that AI-driven content creation will bring.

Meanwhile, brands asking "Why pay agency fees?" quickly discover that building internal teams is much harder than anticipated. The agencies that prove they can handle volume efficiently at competitive margins will capture the clients who tried to go internal and realized the operational complexity exceeds their capabilities.

Having watched similar transformations across industries, the pattern is clear: the early adopters of operational efficiency become the market leaders when volume explodes.

Global Complexity From Day One

Legacy media deals often started domestic and expanded internationally over time. Influencer campaigns are global by default - a single campaign touches creators in São Paulo, Dubai, and Manila while coordinating through teams in New York and London.

The complexity isn't theoretical. Contract terms that work in California don't translate automatically to international talent. Payment methods that satisfy U.S. creators don't work for European partners. Communication channels vary by region - WhatsApp in Latin America, WeChat in China, email in North America.

I've managed international deals before, but never at this volume with this many variables. The coordination matrix explodes: multiple time zones × multiple currencies × multiple legal frameworks × multiple communication preferences × hundreds of individual relationships.

The Wild West Opportunity

What excites me isn't just the growth - it's that this industry is nascent enough that fundamental infrastructure decisions are still being made. Unlike legacy media, there are no entrenched systems protecting inefficient processes.

When I show traditional entertainment executives workflow optimization, they see value but worry about disrupting established relationships. When I show influencer marketing teams the same capabilities, they ask: "When can we start using this?"

The industry is growing fast enough that solutions scale quickly, changing rapidly enough that adaptability becomes core competency, and open enough that innovation isn't blocked by legacy constraints.

What's Coming: The View From Both Worlds

Having spent months researching AI's impact and now understanding current operational realities, I see challenges that both pure outsiders and pure insiders miss.

The volume will increase exponentially as AI makes content creation nearly free. But the coordination requirements will become more complex, not simpler. International expansion, regulatory frameworks, new platforms with different algorithms, creators professionalizing their business operations.

The teams that solve today's coordination chaos earn credibility to tackle whatever comes next. When a brand trusts you to manage 300 creator relationships efficiently, they'll trust you to handle 3,000.

But the future challenges go beyond volume. AI will change creator behavior, audience expectations, and content formats in ways we can't predict. New platforms will emerge with different algorithms and engagement patterns. Regulatory frameworks will evolve as governments figure out how to handle distributed content at scale.

Building Infrastructure for What's Different

This is why Basa exists. Not to digitize existing workflows, but to build infrastructure for coordination requirements that don't exist anywhere else. We're solving today's pain points to build the trust needed for tomorrow's unknowns.

The trust we build managing current chaos becomes the platform for whatever emerges next - AI-generated content workflows, new platform integrations, global expansion challenges, or distribution models we can't imagine yet.

That's what makes this space compelling: positioning at the center of an industry transformation that's just getting started, where being the infrastructure that adapts becomes the ultimate competitive advantage.