Practical AI for Everyone

In every “gold rush”, the biggest winners weren’t digging for gold but selling shovels. Today’s AI boom follows the same pattern. Yes, there’s money, momentum and excitement. But under all the hype, a lot doesn’t work. Those gaps seem like roadblocks – but they’re openings. Especially for startups who can move fast while others wait.

I’ll walk you through a few of these early-stage opportunities here. These insights come from a mix of firsthand experience in the AI space, personal interest and clear signals I’ve been tracking. These signals include rising investments, startup activity, tightening regulations, emerging tech, market gaps – wind of change.

Whether you’re building something new, developing ideas inside a large company or scouting as an investor (#NoFinancialAdvice), there are shovel-worthy opportunities here. No hype – just real friction in the “system” that’s waiting to be solved. Focus on the “dirt” around the “AI gold” and you’ll see where the value is…

Why the emphasis on startups? Because they tend to act faster and with more conviction than traditional players. They don’t carry the same “baggage” – technical, political or image. That gives them room to build what’s needed and, if needed, disrupt the status quo – at the pace AI demands.

Table of Contents

Cybersecurity: Defending Against the Dark Side of AI

AI-driven attacks are rising sharply. Annual global cybercrime losses are estimated at $12 trillion. Deepfake scams, automated phishing and ransomware now evolve too fast for older security systems to keep up. Many companies still rely on reactive tools that only step in after the damage is done.

Current solutions struggle with how quickly and subtly these new threats shift. That leaves vulnerabilities for data leaks, financial losses and reputational damage. The need for change is urgent. Enterprises are already ramping up cybersecurity spending significantly to address such threats.

There’s clear demand. But organizations aren’t ready yet – all around the world: Over 3.5 million security jobs are currently expected to go unfilled… Meanwhile, privacy regulations and complex integration means slower adoption of new tools. So even though need and intent are strong, adoption still lags.

Corporates, governments, smaller businesses and individuals all face these rising threats. But the tools available are fragmented. Many were built for threats of the past and can’t keep up with AI’s pace. This gap opens the door for nimble startups to build what’s missing – facing the dark arts of AI.

Exemplary “search fields” for startup ideas:

• Defense instruments: Tools or APIs for deepfake detection, firewalls against prompt injections automated anomaly detection and red-team simulators for LLMs
• Integrated security platforms: Suites combining the above isolated features for more holistic solutions
• Authenticity & identity layers: Systems to verify content origin or user identity where it really matters (maybe a fresh use case for blockchain tech?)

Guardrails for Careless AI Use: “Vibe Coding” & Oversharing

AI tools now help users code and write faster – but they also make it easier to cut corners. Developers often rely on automated coding tools like Cursor AI or Wispr Flow’s speech-to-text to produce quick results. This style, often called “vibe coding”, tends to skip over (sanity and) security checks. At the same time, many users feed (highly) sensitive info into chatbots or (over-)share on social media, without thinking twice.

These convenient habits might save time in the moment. But they’re already causing trouble. Enterprises are now learning the hard way about the consequences of “careless” AI use. For example, in one widely reported case, Samsung engineers accidentally exposed proprietary source code while debugging with ChatGPT. That’s just the beginning.

With trends like “vibe coding” (and compulsive oversharing), technical debt will rack up soon. (At least there will be demand for experts to fix this, right?) Also, stricter rules – like the EU AI Act and Data Governance Act – force change from the top. But the “human factor” also has to catch up: “Speed-first mindsets” and a lack of simple safeguards make risky behavior the default.

Many organizations (and people) use generative AI tools almost daily. Yet few are equipped to manage these risks. Still, most current solutions only rely on simple checklists, generic “policies” etc. What’s missing are effective systems that guide better decisions right when it matters.

Possible approaches to explore:

• AI & Social media “trainings”: Friendly, low-barrier lessons or “nudges” for everyday users to spot risks, avoid oversharing and use AI tools more safely
• “Vibe Coding” controls: Developer tools, add-ons etc. that catch and flag unsafe coding practices instantly, with suggested fixes or human-in-the-loop reviews
• Oversharing prevention: “Technical helpers” (e.g. privacy-friendly plugins) that spot sensitive info mid-typing and ask users to pause before sending

AI for Everyone: Bridging the Digital Divide

AI is moving fast, but many are being left behind. (Yes, not everyone is in the hype bubble.) Seniors, people with disabilities or low digital skills often struggle to keep up. Interfaces are hard to use, content doesn’t reflect their lives, education is limited etc. On top of that, these groups face greater risks from scams, misinformation or deepfakes.

Almost 3 billion people still face digital exclusion. Especially in developing regions, they often don’t have regular internet access due to high costs or poor infrastructure. That’s a huge market for business – and a social issue worth solving. Giving these groups access to AI, esp. in areas like education, changes lives. For example, the World Bank “From Chalkboards to Chatbots” program in Nigeria brought GenAI into after-school learning. The early results speak for themselves.

Technological progress – esp. in voice interfaces – plus ESG pressure and new accessibility laws support solutions for underserved groups. Demand is strong and growing. Around 20% of people worldwide live with a disability. Millions more lack digital skills, esp. in the growing aging “silver society”. Many of these people are eager to use relevant AI solutions. But trust gaps, steep learning curves and limited supporting infrastructure slow adoption.

Established players (Big Tech and traditional educators) do contribute to AI accessibility. But most of their solutions assume a baseline of digital literacy. That’s where startups can stand out. They can take a “Zebra” approach – balancing social impact and business viability – which larger companies often struggle to pull off (authentically).

Areas for promising startup ideas include e.g.:

• Inclusive AI assistants with accessibility-first design (e.g. voice control) and localized content, tailored for elderly or digitally unfamiliar users
• Protective AI filters that shield vulnerable users from scams, misinformation or unsafe content, based on their specific risks
• Digital and AI literacy initiatives that teach basic concepts and safety in a personal, hands-on way – possibly through playful, “gamified” formats

EU Digital Platforms: Answering the Call for Local Alternatives

Geopolitical tension and rising privacy concerns are changing what Europeans expect from digital platforms. Europe’s Digital Markets Act (DMA) now pushes back against the dominance of non-EU platforms like Meta and Amazon. Today, the vast majority of Europeans are seriously concerned about how their data is handled there. That signals clear demand for trustworthy, local alternatives.

Still, credible options are limited. Most users rely on platforms that expose them to potential privacy risks, vague moderation or influence from outside the EU. The EU has roughly 450 million citizens. Almost €100 billion is spent each year on digital ads alone. The majority of this spending occurs on non-EU platforms, unsurprisingly. There’s a strong case for keeping more of that value closer to home…

Policy momentum is strong. Alongside GDPR and the AI Act, the mentioned DMA supports local innovation and offers fresh funding. EU leaders have already announced hundreds of billions in planned AI investments at the recent AI Action Summit. Still, big platforms dominate (thanks to powerful network effects). Compliance is complex. Cultural needs differ across countries. That makes it hard to build one-size-fits-all solutions for a diverse continent.

Demand is clearly there. Just look at the “BuyFromEU” subreddit which grew from almost nothing to hundreds of thousands of users in just a few weeks. The topic is trending for a reason. People across Europe want more secure, locally accountable platforms – tech that reflects their values, culture (and languages).

So far, few successful European digital platforms exist. Even the better-known names like Threema (messaging), Zalando (fashion) or Mastodon (social) face challenges with scale, user experience or monetization. So there’s still open ground to innovate, esp. for newcomers with a clean slate.

“Startup-y opportunities” include:

• Privacy-centric social networks and messaging: GDPR-compliant and secure by design, with transparent algorithms and clear data control (AI Act compliant)
• Regional e-commerce marketplace/s: Sustainable local goods, ethical business practices, transparent logistics and strong data protection
• “Control layers” for non-EU tools: Middleware that adds EU-compliant safeguards between EU-based systems or users and popular non-EU platforms (e.g. “GAFAM”)

Making AI Systems Talk: “Skills-as-a-Service” for AI Agents

AI agents are gaining traction fast. These systems can perform tasks on their own by using external tools. But right now, they’re still “wobbly”. They often fail to work consistently because there’s no clear standard for how they interact with those tools. Even powerful models like GPT-4.5 stay too general. Without deep domain expertise, they struggle with precise tasks that businesses actually care about.

Businesses urgently need specialized AI to solve complex problems in finance, healthcare, scientific research or design. That’s where MCPs come in. Think of them as plug-and-play modules that let AI systems and tools talk to each other in a universal language. For example, with this technology you can connect Claude to tools like Blender to design a living room.

Tailwinds are strong. The MCP standard is spreading fast. Demand for specialized AI capabilities keeps growing. Emerging MCP marketplaces, like “Glama AI”, show strong demand from enterprises, developers and individuals. But there are still blockers to wider adoption: early-stage bugs, fragmentation, complexity and limited trust around quality and data sharing.

As this is a (swiftly) emerging field, right now, very few players offer such integrations. Most still stick to rather generic agentic skills, leaving gaps for domain-specific depth. This creates a (temporary) window of opportunity for focused startups to step in and stand out.

Potential approaches for startups are:

• Offer specialized MCP servers: Wrap valuable, unique data or tooling into interfaces that (third-party) agents can call “on demand” (e.g., scientific datasets).
• MCP marketplace platforms: They basically work like app stores, making it easy to discover and integrate relevant new AI agent skills
• Simplified integration tools or services: Help organizations or individuals connect to relevant MCPs. Or certification and QA services (so that MCPs meet reliability standards) etc.

Innovating AI Hardware: Better, Efficient & Greener Compute

AI’s growth comes with a heavy compute bill. Demand for GPUs has surged massively since ChatGPT’s launch. By 2030, the global AI chip market could exceed $300 billion. But today’s GPU scarcity and rising costs already slow down many teams. Data centers powering all this AI already use around 2% of global electricity – fueling both expense and climate concerns.

Better access to GPUs would let smaller firms and underserved regions fully take part in AI development. More efficient compute also brings down costs, which matters in markets sensitive to infrastructure spending. On both fronts – access and efficiency – there’s real space for fresh ideas.

More AI adoption, public pressure for greener tech and government incentives (not everywhere…) give solid tailwind to innovations. But monopolistic tendencies in the infrastructure business are a big hurdle. For example, NVIDIA owns over 80% of the AI GPU market (!), making it hard for newcomers to break in.

The need spans multiple groups – researchers, data center operators, startups, large firms etc. Gartner projects global spending on data centers – the hardware powering modern AI infrastructure – will grow by 23.2% to $405.5 billion this year. So this big cake is ripe for inventions that make compute more affordable and sustainable.

The giants – NVIDIA, AMD and the big cloud providers – set the pace today. But challengers like “Groq” are already exploring bold, new chip designs. Startups can’t outspend Big Tech. But they can spot the gaps – and move faster and with focused conviction to close them.

Potential startup ideas include:

• GPU Marketplaces & Circular Economy: Platforms for renting, sharing or recycling GPUs to ease shortages
• AI Inference Optimization: New model or chip architectures and approaches that improve speed and cut compute or energy use
• Sustainable Data Centers: Tech to reduce energy needs, like advanced cooling or smarter energy management

Wrap-up: The AI Gold Rush is ON…

…but the real fortunes still flow to those selling shovels – or durable denim, if you think like Levi Strauss. I won’t be surprised if some of the next big unicorns come straight out of the above spaces. If you want a wider view of what’s ahead, my annual AI trend forecast is a good place to start.

But let’s be clear: these aren’t “get rich quick” schemes. Spotting the “gap” – even if it’s backed by promising signals – is just step one. Whether a startup succeeds also depends on what you (and your network) bring to the table: mindset, strategy, execution. Timing, product–market fit, clear differentiation and real user adoption matter just as much. I’ve laid out a practical way to plan your AI ventures around these success factors in this article.

So – if any of these opportunities sparked something in you, follow it. Found your “dirty niche” and want to dig deeper? Then check out this guide to “unearth” high-impact AI use cases. Want to chat about an idea? Feel free to reach out, leave a comment and spread the word.

Cheers,
John