Building Smarter AI Regulation: Putting People, Privacy and Small Medium Businesses First

As someone who has been following the progress of Artificial Intelligence since 2008 when I first became acquainted with the work of Ray Kurzweil, the past two years have been nothing short of amazing to behold. Not only are the early predictions surrounding AGI coming to fruition, but the law of accelerating returns in science and technology has been on display.  

Like many of us, I jumped on the beta version of ChatGPT and have been trying to keep up with the exponential growth in AI computational power ever since.  

In late 2024, when I was completing an in-depth AI program through MIT, I gained access to some research lab environments. It was astounding to experience some of the solutions that were already in beta form. It was also clear that Artificial General Intelligence (AGI) was right around the corner. 

The implications are just now starting to become widely recognized. 

In his groundbreaking work on The Singularity, Kurzweil also forecasts the social disruption that will occur with the emergence of increasingly powerful AI. The AI transformation is no longer an abstract promise; it is a daily reality shaping how we work, learn, shop and interact. From chatbots helping customers to diagnostic tools in healthcare, AI is delivering productivity gains and competitive advantages. Yet the same technology raises urgent questions:  

• Will jobs disappear faster than new ones are created? 

• Will privacy survive as data becomes the lifeblood of algorithms? 

• Can regulation prevent harms without suffocating innovation? 

So far, the loudest voices shaping AI regulation have been large enterprises. Industry lobbying has emphasized protecting incumbents and preserving innovation pipelines, often underplaying the distinct challenges smaller and mid-sized businesses face. This imbalance risks creating a regulatory environment where only big firms can survive, concentrating power, reducing competition and stifling the very economic growth AI is supposed to unleash. 

If regulation is to succeed, it must shift focus: from protecting the privileged few to enabling the many. That means embedding privacy as a non-negotiable, protecting human work, and ensuring that small and medium businesses—the backbone of job creation—can adopt AI responsibly without being crushed under compliance burdens. 

When Regulation Favors Big Business 

Let’s be blunt: most regulatory frameworks to date have been designed with enterprise-level firms in mind. The EU’s AI Act, while groundbreaking, has already been criticized for assuming large compliance teams, expensive audit systems and deep technical resources. In the U.S., conversations about AI guardrails have largely centered on the risks of foundation models and national security implications—again, the realm of Big Tech. 

For SMBs, the reality is very different. A 20-person marketing agency trying to use AI to speed up content creation shouldn’t be navigating the same regulatory labyrinth as a trillion-dollar company deploying generative models at global scale. Yet too often, that’s exactly what ends up happening. And the outcome is that small firms either take on unsustainable risk by ignoring the rules, or they back away from adoption altogether, losing competitiveness in the process. 

Case Study 1: The Local Retailer vs. the Global Platform 

Let’s play out some scenarios and think through how this works in everyday life. Picture a small online retailer experimenting with AI-driven personalization to recommend products. To comply with emerging rules around algorithmic transparency, they’re required to produce documentation on training data provenance, error rates, and consumer disclosures. For Amazon or Walmart, this is a line item in the compliance budget. For the retailer, it’s an existential entry to the expense line. 

Now imagine the alternative: privacy-by-default AI platforms that come with built-in compliance features like standardized protections, transparent data-handling protocols, and ways to contain sensitive data from going into the AI ether. As an example, solutions like pipIQ.com are emerging that promise precisely that: a secure, brandable workspace where SMBs can adopt AI tools without risking customer trust. In this scenario, regulation doesn’t crush the retailer; it levels the playing field by ensuring both large and small firms have affordable access to compliant and secure technology. 

Case Study 2: Healthcare Startups in the Privacy Minefield 

Or consider another hypothetical. A healthcare startup wants to use AI to triage patient questions, reducing strain on human staff. Under strict data privacy laws, they face enormous complexity: HIPAA compliance, state-by-state rules, and now AI-specific requirements about transparency and oversight. For a startup, the compliance spend can rival the payroll. 

Without adaptive regulation and accessible compliance infrastructure, these firms risk being sidelined. Yet they are often the very organizations bringing innovation to underserved communities—exactly where the economic and social impact of AI could be greatest. 

Why Privacy Is the Foundation 

Privacy is not just a consumer right, but a business enabler. Without it, public trust collapses and adoption slows. For the entrepreneur or medium-sized business, a privacy breach can be catastrophic, wiping out a reputation overnight.  

Strong, enforceable privacy rules—such as data minimization, portability, and anonymized PII—are essential, but they must be implemented in a way that SMBs can realistically achieve. That means encouraging the growth of privacy-by-default platforms and compliance kits designed for smaller organizations, not just Fortune 500 firms. 

Where Regulation Goes Wrong 

As we consider how to tackle this problem, we must recognize that over-regulation is just as dangerous as under-regulation. Several pitfalls that I see recurring across many proposed frameworks include: 

• Patchwork laws: Different jurisdictions pile up conflicting rules, creating complexity that only large corporations can navigate. 

• One-size-fits-all requirements: Imposing enterprise-scale compliance standards on SMBs creates barriers to entry. 

• Premature mandates: Regulating hypothetical risks before they materialize ties innovators in knots without improving safety. 

• Disclosure overload: Requiring extensive consumer disclosures can confuse rather than protect, while drowning SMBs in paperwork. 

The outcome of these mistakes is predictable: incumbents thrive, smaller players shrink, and innovation concentrates in fewer hands. 

Building Regulation That Works 

A smarter path is possible. Effective regulation should be: 

• Risk-based and proportional: Differentiate between high-risk systems (like healthcare diagnostics) and low-risk ones (like marketing assistants). 

• Focused on outcomes, not prescriptions: Require fairness and transparency, as well as privacy, but allow flexibility in how businesses meet those goals. 

• Adaptive and iterative: Use data-driven triggers to adjust guardrails as AI adoption evolves. 

• Supportive of SMBs: Provide shared compliance tools, privacy-first defaults, plus incentives for responsible adoption. 

Most importantly, regulation should be written not just with input from global enterprises but with active participation from SMBs, workers, and civil society. Without these voices, regulation becomes a tool of market concentration rather than a safeguard of innovation. 

Case Study 3: The Hidden Job Impact 

Think about what this might look like in contrast to some of the scenarios we talked through earlier. Let’s say a mid-sized logistics company adopts AI for route optimization. The result? Lower fuel costs, fewer scheduling errors, and fewer dispatchers needed. Without a reskilling program, those dispatchers would simply be laid off. At scale, that story would repeat across industries. 

Now imagine if regulation required companies to assess job impact before deployment and provided tax credits for reskilling displaced workers. The dispatchers could transition into higher-value roles: overseeing AI systems, managing customer relationships, or analyzing performance data. Jobs are transformed, not destroyed, and the company gains a more skilled workforce. 

Why SMBs Must Be Central 

SMBs account for around half of global employment and the majority of net job creation. They are also where much of the real-world adoption of AI will happen: in retail shops, consultancies, healthcare clinics, logistics providers, and startups. If regulation doesn’t account for their needs, the result will be a two-tiered economy, one where big tech consolidates power while small businesses struggle to survive. 

Embedding SMBs into the regulatory design process isn’t charity; it’s economic strategy. A thriving SMB ecosystem fuels competition, keeps prices fair, drives innovation, and distributes economic opportunity across communities. Regulation that ignores SMBs risks hollowing out this foundation. 

The Path Forward 

The question is not whether AI will reshape the economy, but whether regulation will shape that change in a way that works for everyone. 

For workers, that means reskilling and protections against abrupt displacement. 

For consumers, it means enforceable privacy and transparency. 

For SMBs, it means access to compliance tools, privacy-by-default platforms, and incentives that make responsible adoption feasible. 

For society, it means regulation that adapts quickly, avoids unnecessary red tape, and prevents a concentration of power in the hands of a few giants. 

AI will be either the great equalizer or the great concentrator. Smart regulation will decide which. 

References & Further Reading 

Ayub, T. (2024). Regulating AI: Balancing Innovation, Ethics, and Public Policy. SSRN. 

Rymon, Y. (2024). Societal Adaptation to AI Human-Labor Automation. arXiv. 

McKinsey & Company (2025). Superagency in the workplace: Empowering people to unlock AI’s full potential. 

Brookings Institution (2025). What the public thinks about AI and the implications for governance. 

The White House (2025). Winning the Race: America’s AI Action Plan. NatLawReview Summary.