The article proposes a new agency model where talented engineers, designers, and copywriters are recruited to train AI, with the promise that their roles will be phased out over time. Unlike traditional consultancies, this model aims to leverage AI for scalability and higher profit margins, allowing employees to receive ongoing salaries and profit shares even after their roles are automated. The idea highlights the need for changes in employment contracts to reflect the new economic realities of AI-driven work. While the potential benefits are significant, challenges like the pace of AI advancement and the need for proper training data remain. The author suggests that if successful, this model could lead to a new era of professional services, where employees contribute data rather than time.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| AI-Driven Employment Models | A shift towards employment structures where workers train AI and share in the profits. | Transition from traditional employment contracts to models rewarding data contribution. | Widespread adoption of AI-driven roles where employees earn from their data contributions. | The need for businesses to rapidly scale and reduce costs through AI automation. | 4 |
| Fractional Work Opportunities | The rise of fractional roles in professional services, allowing flexibility and access to top talent. | Shift from full-time employment to fractional roles for specialized skills. | A new normal where professionals work part-time across multiple projects and companies. | The demand for specialized skills at lower costs through flexible work arrangements. | 3 |
| Mullet Consultancy Model | Professional services firms adopting a hybrid model of visible services and backend AI automation. | Moving from traditional consulting to a software-like margin profile. | Consulting firms will heavily rely on AI, leading to significant operational changes and efficiencies. | The pursuit of higher margins and scalability in an increasingly competitive market. | 4 |
| Increased Automation of Creative Processes | AI tools becoming integral in creative fields like design, copywriting, and engineering. | From human-centric creativity to AI-assisted production processes. | Creative industries will be reshaped, with AI significantly contributing to content creation. | Advancements in AI capabilities and the pressure to improve efficiency. | 5 |
| Changing Nature of Professional Services | AI enabling the transformation of professional services into productized offerings. | From bespoke services to standardized, AI-driven solutions. | Professional services will be more commoditized, affecting pricing and quality perception. | The need for businesses to innovate and offer scalable solutions in a competitive landscape. | 4 |
| New Legal Structures for Employment | Emerging legal frameworks to accommodate AI’s impact on traditional employment contracts. | Evolving from hourly pay to compensation based on data contribution and AI output. | Legal norms will adapt, recognizing data contribution as a form of labor deserving compensation. | The need to protect workers’ rights and ensure fair compensation in an AI-driven economy. | 4 |
| name | description | relevancy |
|---|---|---|
| AI Job Replacement | As AI technologies evolve, professionals may face redundancy, leading to job insecurity and shifts in employment structures. | 5 |
| Insufficient AI Progress | If AI development does not progress quickly enough, businesses may struggle to adapt, resulting in inefficiencies and job displacement. | 4 |
| Data Compensation Issues | Lack of fair compensation models for employees who contribute data may lead to exploitation and legal disputes. | 5 |
| Quality Sacrifice for Automation | The push to automate may lead to a reduction in service quality as firms seek efficiency over excellence. | 4 |
| Commoditization of Professional Services | As more companies adopt AI-driven models, the unique value of professional services may diminish, leading to price wars. | 4 |
| Legal and Social Norms Lagging | Current legal frameworks may not support new employment models, leaving workers vulnerable. | 5 |
| Dependence on Human-Crafted Data | The potential inefficiency of AI when trained without high-quality, specialized human input may hinder service advancements. | 4 |
| name | description | relevancy |
|---|---|---|
| AI-Driven Job Replacement | Professionals train AI models to replace their roles, receiving ongoing salary and profit sharing instead of traditional employment. | 5 |
| Mullet Consultancy Model | A hybrid business model combining professional services with AI-driven efficiency, resembling software companies in profitability. | 4 |
| Data as Currency | Workers exchange their expertise and data for ongoing compensation, shifting from time-based pay to value-based pay. | 5 |
| Enhanced Professional Services | AI enables high-quality professional services to be produced more efficiently, changing the landscape of traditional consultancies. | 4 |
| Flexible Employment Contracts | Emerging employment agreements that prioritize ongoing value generation over time investment, adapting to an AI-centric economy. | 5 |
| AI Augmentation of Creative Work | Professionals leverage AI tools to enhance their creative outputs, leading to a new collaboration between humans and machines. | 4 |
| Commoditization of Professional Services | As AI-driven consultancies proliferate, services may become commoditized, leading to reduced prices and lower margins. | 4 |
| description | relevancy | src |
|---|---|---|
| Agencies that recruit talent to train AI, allowing employees to eventually be replaced by the systems they help create. | 5 | cb1de23b85f5c592ad2f8e720a7811a0 |
| Services that connect startups with vetted senior developers on a fractional basis, optimizing costs and resources. | 4 | cb1de23b85f5c592ad2f8e720a7811a0 |
| An AI concept where complex tasks are broken down into smaller processes for easier training and automation. | 4 | cb1de23b85f5c592ad2f8e720a7811a0 |
| Consultancies that leverage AI to improve efficiency and revenue-to-employee ratios in professional services. | 5 | cb1de23b85f5c592ad2f8e720a7811a0 |
| Contracts that compensate employees based on the ongoing value generated by their contributions to AI models. | 4 | cb1de23b85f5c592ad2f8e720a7811a0 |
| name | description | relevancy |
|---|---|---|
| AI Training Employment Model | A new employment model where professionals train AI to replace their roles while receiving ongoing compensation from AI-generated work. | 5 |
| Shift in Professional Services Dynamics | The potential transformation of professional services to operate with software-like margins due to AI integration. | 4 |
| Evolving Employment Contracts | The need for new employment agreements that reflect the changing value of human contributions in an AI-driven environment. | 5 |
| Data Compensation Issues | Concerns around fair compensation for employees providing data that trains AI models, raising questions about labor rights. | 5 |
| AI-Driven Service Commoditization | The risk of commoditization in professional services as more firms adopt AI, leading to price wars and decreased margins. | 4 |
| Quality vs. Automation Dilemma | The challenge of maintaining high-quality outputs while automating tasks traditionally done by skilled professionals. | 4 |
| Regulatory Framework for AI Labor | The need for new legal and social norms to govern employment and compensation in an AI-centric job market. | 5 |