In 1848, a carpenter and sawmill worker named James W. Marshall was helping to build a sawmill for John Sutter, a Swiss-American pioneer, near Coloma, California, when he made a historic discovery. While working by the river, Marshall noticed what looked like flakes of gold in the water—a find that would spark one of the most feverish quests for wealth in history.
As news of the find slowly spread, miners from across the globe flocked to California, dreaming of instant riches. Within a year, California’s population exploded from around 14,000 to over 100,000. Ships were abandoned in San Francisco Bay as entire crews rushed inland. Everyone imagined themselves as the next millionaire. Yet few realised that turning raw gold into fortune required more than luck—it required tools, supplies, and infrastructure.
Enter Samuel Brannan. A Mormon elder and entrepreneur running a general store in San Francisco, Brannan was well-connected, shrewd, and always alert to opportunity. When he heard of Marshall’s discovery, he rushed ahead—not to dig for gold—but to stock up on shovels, picks, pans, and other mining essentials.
Brannan then executed a masterstroke: he paraded a vial of gold through the streets of San Francisco, effectively announcing the discovery. Almost overnight, miners flooded in, desperate for the tools they needed. Brannan’s foresight ensured he was selling exactly what everyone required—the shovels, pans, and picks that made gold mining possible.
Meanwhile, Marshall struggled. He had made the discovery, but lacked the business acumen and timing to capitalise on it. Over time, his role became more a matter of historical record than personal fortune. Brannan, by contrast, quietly amassed a fortune, proving that the real opportunity often lies not in chasing gold but in enabling others to succeed.
What happened in the 19th century wasn’t just about gold—it was about an ecosystem forming almost overnight, with winners and losers determined not by who struck gold, but by who built the picks, shovels, and supply lines. Those who provided the tools thrived, long after the gold rush hype had faded.
Fast forward to today, and we are in the midst of a new kind of rush—the AI boom. The parallels are striking: companies race to build the next breakthrough AI model or app, yet the real “shovel” opportunities lie in providing the tools, services, and infrastructure that make AI usable, reliable, and safe.
Here’s what’s fueling the frenzy today:
The global AI market is estimated at $391 billion in 2025, projected to soar to $1.81 trillion by 2030—a compound annual growth rate (CAGR) of nearly 36%. (Founders Forum Group)
The AI infrastructure market alone is expected to grow from $57 billion in 2024 to over $74 billion in 2025—a booming 30% CAGR—and climb to $223 billion by 2029. (The Business Research Company Spherical Insights)
Organisations are adopting AI at unprecedented speeds—78% of firms used AI in 2024, up from 55% just the year before. (Stanford HAI, G2 Learn Hub)
Executives plan to increase AI spending: 92% expect to spend more on AI in the next three years, and more than half expect to boost budgets by at least 10%. (McKinsey & Company)
Morgan Stanley reports real-world returns from AI—insurance firms increased AI adoption from 48% to 71%, and adoption in retail and real estate is enabling powerful gains in efficiency and performance. (Business Insider)
Giants like Microsoft, Amazon, Meta, and Alphabet are investing massively—roughly $340 billion on AI data centres and development in 2025 alone, contributing significantly to GDP growth. (Barron's)
30 AI “Shovel” Opportunities for Early Founders
Here are real-world niches where early founders can build essential AI tools and services. Each includes examples of companies already in the space to spark ideas.
Data Preparation & Labelling
The Need: AI models require vast amounts of clean, well-labelled data to learn effectively. Many companies lack the internal resources to prepare and label this data efficiently.
Opportunity: Labelling is tedious and specialised; new platforms could focus on niche domains (e.g., medical imaging) or efficiency improvements like semi-automated labelling pipelines.
Example: Scale AI provides tools and human-in-the-loop services to label images, text, video, or sensor data, often for autonomous vehicles and computer vision applications.AI Integration Tools
The Need: Organisations struggle to plug AI into existing workflows without specialised technical knowledge.
Opportunity: Tools that simplify AI adoption or integrate with popular business software are in demand.
Example: Levity offers no-code platforms that allow businesses to integrate AI into email triage, document processing, or customer workflows.Monitoring & Testing AI
The Need: AI models can behave unpredictably or degrade over time. Organisations need ways to track, debug, and maintain model performance.
Opportunity: Solutions that simplify AI observability, error detection, and alerting are highly sought after.
Example: WhyLabs monitors AI models in real-time to detect data and prediction anomalies.Knowledge & Search Tools
The Need: AI performs better with structured knowledge and relevant context, but companies struggle to organise and query their data effectively.
Opportunity: AI-powered search solutions for corporate or private knowledge bases are increasingly valuable.
Example: Kagi provides an AI-powered search for private or corporate knowledge.Privacy, Security & Compliance
The Need: Organisations must maintain privacy, meet regulatory requirements, and secure sensitive data while using AI.
Opportunity: Tools that simplify compliance and protect data in AI workflows are essential for businesses in regulated industries.
Example: BigID helps manage data privacy, compliance, and governance for AI applications.Managed AI Services
The Need: Many organisations lack internal AI expertise or teams to build and deploy models.
Opportunity: Outsourced AI training and deployment services can help companies adopt AI without hiring full-time specialists.
Example: Spell provides managed AI training and deployment services.Specialised Marketplaces
The Need: Companies need easy access to datasets, pre-trained models, or AI APIs.
Opportunity: Platforms that simplify the discovery, licensing, and sharing of AI assets can create significant value.
Example: Hugging Face AI Hub offers datasets, models, and APIs.Synthetic Data
The Need: Real-world datasets can be limited or sensitive, making AI training challenging.
Opportunity: Generating synthetic, privacy-preserving data allows companies to train models safely and efficiently.
Example: Mostly AI generates realistic synthetic datasets for AI training.AutoML & Model Building Tools
The Need: Many organisations cannot afford AI engineers; automating model building makes AI accessible.
Opportunity: Platforms that simplify model creation, training, and deployment help non-experts leverage AI.
Example: DataRobot automates building and deploying AI models.Industry-Specific AI Solutions
The Need: Generic AI models often underperform in specialised domains like healthcare or finance.
Opportunity: Customised AI solutions tailored to sector-specific problems are in demand.
Example: PathAI provides AI for pathology and medical diagnostics.AI-Powered Document Automation
The Need: Organisations produce large volumes of documents that are difficult to manage manually.
Opportunity: AI-driven document understanding, summarisation, and structuring can save time and reduce errors.
Example: Docugami turns complex documents into structured data.Automated Transcription & Translation
The Need: Audio and video content requires accurate transcription and translation for accessibility and analysis.
Opportunity: AI tools that deliver fast, precise, multilingual support are in high demand.
Example: Rev and DeepL provide transcription and translation services.Robotic Process Automation (RPA)
The Need: Businesses perform repetitive, rule-based tasks that can be automated.
Opportunity: Integrating AI with RPA enhances efficiency, accuracy, and scalability.
Example: UiPath helps automate workflows using AI.AI Observability & Debugging
The Need: Monitoring AI performance in production and understanding model decisions is crucial for trust and compliance.
Opportunity: Tools that provide explainability and model health tracking are critical for enterprise adoption.
Example: Fiddler AI offers explainability and monitoring for AI models.Annotation & Labelling Platforms
The Need: Labelled data is required for supervised learning across multiple data types.
Opportunity: Crowdsourced or specialised annotation platforms can serve high-volume or niche needs.
Example: Appen provides crowdsourced annotation and labelling services.AI-Powered Customer Service
The Need: Companies want to provide rapid, intelligent support without relying solely on human agents.
Opportunity: AI chatbots and virtual assistants improve response times and learning from interactions.
Example: Ada powers AI chatbots for customer support.Fraud Detection
The Need: Financial transactions and networks are vulnerable to fraud and cyber threats.
Opportunity: AI systems that detect anomalies quickly are highly valuable in banking, insurance, and e-commerce.
Example: Darktrace uses AI to detect cyber threats and fraud.AI-Driven Market Research
The Need: Extracting insights from vast amounts of consumer or market data is challenging manually.
Opportunity: AI can identify trends, sentiment, and opportunities faster than traditional methods.
Example: Crimson Hexagon provides AI-driven consumer insights.AI-Powered Recruitment
The Need: Screening and evaluating candidates efficiently is complex and time-consuming.
Opportunity: AI can streamline candidate assessment, improve hiring decisions, and reduce bias.
Example: HireVue uses AI to evaluate candidate interviews.AI in Logistics
The Need: Supply chains are complex, with fluctuating demand, routes, and inventory needs.
Opportunity: AI can optimise routing, inventory management, and delivery efficiency.
Example: Llamasoft provides AI-driven supply chain optimisation.AI for Healthcare Operations
The Need: Hospitals and clinics face administrative bottlenecks and patient care inefficiencies.
Opportunity: AI can predict patient needs, automate tasks, and improve operational efficiency.
Example: Olive automates healthcare administrative tasks.AI-Powered Personalisation
The Need: Consumers expect customised experiences across digital platforms.
Opportunity: AI-driven personalisation increases engagement, conversion, and retention.
Example: Dynamic Yield provides AI personalisation for e-commerce.AI for Legal Workflows
The Need: Law firms and in-house teams handle vast amounts of documents and case data.
Opportunity: AI can assist with research, contract analysis, and case prediction.
Example: Casetext provides AI legal research tools.AI in Real Estate & Property Management
The Need: Property valuation, occupancy management, and market analysis are complex and data-heavy.
Opportunity: AI can provide predictive insights, streamline management, and optimise listings.
Example: Reonomy leverages AI for commercial real estate insights.AI for Energy & Sustainability
The Need: Optimising energy use and predicting equipment failure is critical for efficiency and cost reduction.
Opportunity: AI solutions can monitor consumption, improve maintenance schedules, and reduce waste.
Example: Uplight provides AI-based energy management solutions.AI in Manufacturing
The Need: Manufacturing processes face inefficiencies, defects, and equipment downtime.
Opportunity: Predictive maintenance, quality control, and process automation via AI reduce costs and improve output.
Example: Seebo provides AI process optimisation for manufacturers.AI for Cybersecurity
The Need: Cyber threats evolve rapidly, often outpacing human detection.
Opportunity: AI can detect, prevent, and respond to attacks faster and more accurately than manual processes.
Example: Cylance uses AI to prevent cyber attacks.AI-Powered Education Tools
The Need: Education systems struggle to provide personalised learning at scale.
Opportunity: Adaptive AI platforms can deliver customised learning experiences for students and professionals.
Example: Squirrel AI provides AI-driven personalised learning.AI in Finance & Accounting
The Need: Financial operations involve complex data analysis, auditing, and forecasting.
Opportunity: AI can automate reconciliations, detect anomalies, and generate predictive insights.
Example: Kensho provides AI analytics for finance.AI for Governance & Compliance
The Need: Keeping up with regulatory changes is challenging, especially in financial and highly regulated industries.
Opportunity: AI solutions can track compliance requirements, flag risks, and automate reporting.
Example: Compliance.ai helps financial firms stay compliant using AI.
Summary
The AI landscape is moving fast, and the lesson from history is clear: the biggest opportunities often aren’t in striking gold yourself—they’re in giving others the tools to succeed. Early founders, builders, and operators don’t need to be the next OpenAI or Nvidia to make a meaningful impact. There are countless “shovel” opportunities—places where AI is critical, boring, or invisible, but indispensable—waiting to be discovered and built. By focusing on these foundational needs, you position yourself at the center of the AI boom, providing real value and generating sustainable growth.
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by
Glen Smale
Founder, Software Engineer, Techpreneur & Curator of The Growth Framework
A tech media platform uncovering frameworks for growth and success in the real world
www.thegrowthframework.com