Forget AGI, Let's Aim for General-Purpose AI Instead
A level-headed alternative to the grand narrative of Silicon Valley
Unless you've been living under a digital rock for the last three years, you've almost certainly heard the whispers, or perhaps the shouts, that Artificial General Intelligence (AGI) is almost here. This idealized form of AI, often pitched as the ultimate problem-solver, promises to revolutionize everything —from curing diseases to managing global economies, while also improving your coffee and doing your dishes—making our current AI advancements look like child's play. While predictions on its exact arrival diverge wildly, there's a palpable feeling that we might be on the brink of a major milestone for humanity: the emergence of truly intelligent AIs that can perform anywhere humans can, equally or even better than human experts.
And while those grand visions have their place in sparking imagination, I want to propose a more grounded, perhaps more pragmatic, path forward. My thesis here is simple: while the pursuit of AGI remains a fascinating and worthy long-term aspiration, a clearer, more achievable, and ultimately more impactful goal for our immediate future lies in what I call "General-Purpose AI" (GPAI). Think of GPAI not as the ultimate destination, but as a vital compass, guiding us toward meaningful AI development and real-world impact right now.
In this article, I'll delve into why the singular focus on AGI can be a distraction, define what I mean by General-Purpose AI, break down its essential components, and outline a realistic roadmap for how we can get there. My aim is to shift our collective gaze towards a more actionable and universally beneficial future for AI.
Challenges in Defining and Achieving AGI
Let's be honest, the concept of AGI, as it's often discussed, is at least somewhat problematic.
One of the biggest hurdles for achieving AGI is its sheer vagueness. What does it really mean to achieve "human-level intelligence across all relevant domains"? The definitions are often slippery, lacking the clarity needed for a concrete engineering goal. It frequently leans into those fantastical, almost mystical, connotations of sentience, consciousness, and an all-encompassing intelligence. Because of this, proving definitively that AGI has been "achieved" becomes incredibly difficult, almost unquantifiable.
Furthermore, a significant challenge lies in the lack of clear measurability. How would we even know when we've truly arrived at AGI? What are the metrics, the benchmarks that would declare its presence? This contrasts sharply with traditional software development, where goals are typically practical and eminently measurable. Without clear, agreed-upon ways to measure progress or success, AGI risks remaining an elusive phantom.
Most critically, the singular focus on AGI can inadvertently pull our resources and attention away from the tangible. It can divert brilliant minds and significant investment from impactful AI applications that are not only achievable but are already solving real problems today. This pursuit of a distant ideal risks creating unrealistic expectations and, potentially, widespread disillusionment if those grand promises don't materialize in the near term.
For me, the emphasis should be on useful AI, the kind that drives genuine innovation and addresses pressing challenges in our world right now. This is where GPAI offers a more grounded and actionable target. It's about building tools that work, that empower, and that deliver measurable value, without getting lost in the theoretical infinite.
What is General-Purpose AI (GPAI)?
So, if AGI is the abstract ideal, what then is GPAI?
The core idea behind GPAI is a fundamental shift in how we perceive AI. Instead of seeing AI primarily as a finished, consumer-facing application, I view it as a foundational set of tools, capabilities, and infrastructure. Think of it like electricity or the internet, not an end in itself, but a pervasive utility that empowers countless other creations and innovations. This infrastructural view brings immense benefits like scalability, ubiquity, and incredibly broad applicability across diverse domains.
Crucially, GPAI isn't primarily designed for the end-user. Its true audience is the developer. GPAI is about empowering developers to effortlessly integrate sophisticated AI capabilities into their own applications. This democratizes AI, enabling a new generation of problem-solvers to leverage powerful AI tools without necessarily needing to be deep AI experts themselves. It's about bringing AI out of the specialist's lab and into the everyday toolkit of every software engineer.
Let me try then to define GPAI more concretely:
GPAI is a state of the AI infrastructure, tools, and techniques, that enables any developer to incorporate narrow AI for any specific problem on any specific domain, within any reasonable set of infrastructure constraints.
To put it more succinctly, GPAI aims for "anything, anywhere, anytime," in contrast to AGI's often implied "everything, everywhere, all at once". It's about practical, pervasive utility rather than an all-encompassing, perhaps unattainable, general intelligence.
Unpacking GPAI
So, what are the core ingredients that I think will make GPAI a reality?
1- Foundation Models
At the heart of GPAI are what we call Foundation Models. These are vast collections of pre-trained models, already capable of understanding and generating content across various modalities. They are essential because they provide powerful, ready-to-use intelligence, significantly reducing the need for developers to train models from scratch for every new application.
We're already seeing this with text, audio, and image models. But for true GPAI, we need a crucial extension into more challenging modalities: think 3D scenes with realistic physical interactions, complex tabular and relational data, time series, and general graph-like structures. Expanding foundational models into these areas will enable developers to tackle an even more diverse set of problems without requiring deep, specialized AI expertise for each domain.
2- Comprehensive Interoperability
For AI to become truly infrastructural, it needs to speak the same language as everything else in our digital world. This is where standardized communication methods, like MCP and alternatives, become critical. These protocols will allow AI systems to connect seamlessly with traditional software components.
This is about breaking down the silos that currently exist between AI and our existing IT infrastructure. It's about enabling AI features to be integrated just as easily into legacy systems as they are into cutting-edge modern applications. Imagine AI that can effortlessly plug into databases, operating systems, file systems, and web browsers. It also means ensuring different types of AI models—generative, discriminative, pre-trained, custom-trained—can work together harmoniously.
3- Simplified AutoML
Building custom AI solutions often requires specialized knowledge in machine learning. GPAI will change that by making the training and fine-tuning of narrow, task-specific ML models astonishingly simple, directly integrated into standard development environments.
The goal here is to drastically reduce the complexity and specialized knowledge typically required for model development. We want to make custom AI solutions accessible to a vastly wider base of developers. Picture configuring a machine learning model with the same ease as setting up a database or a Docker runtime. This will involve heavy integration of AutoML techniques —automated model selection, hyperparameter tuning, and the like— directly into our IDEs. The aspiration is to shift ML from a niche, specialist domain to a standard, everyday development task for any programmer.
4- Accessible GOFAI
While modern machine learning is transformative, we shouldn't forget the rich history of AI. GPAI will also include tools that expose classic symbolic AI methods, often referred to as Good Old-Fashioned AI (GOFAI), such as constraint satisfaction and search algorithms, all with modern ease of use.
Many real-world problems—think scheduling, logistics, or complex planning—are remarkably well-suited for these time-tested techniques. Providing these simply and effectively adds a powerful, complementary set of tools to the developer's arsenal. It’s about integrating these methods with the same configuration simplicity as other components, ensuring developers have a comprehensive toolkit that combines the best of both modern and classical AI approaches.
5- Pervasive Hardware/OS Support
Finally, for AI to truly be "anywhere, anytime," GPAI systems must be incredibly robust operationally. This means they need to run effectively under a vast array of circumstances, even offline or on mobile devices.
This implies robust hardware support for in-service training and inference of traditional models directly on devices, and at least the ability to perform inference of sufficiently good generative models on those devices. This on-device capability is absolutely essential for AI to become a truly ubiquitous utility, woven into the fabric of our digital lives.
How We Get There
Achieving GPAI won't be a single, monolithic event. It will be a journey with several overlapping phases.
It's important to understand that these phases won't unfold one after another in a strict sequence. Instead, they will run concurrently, with progress in each area accelerating at varying speeds. We should also acknowledge upfront that some challenges, particularly in extending foundation models to very specific and complex modalities, will naturally take longer to solve than others.
Phase 1: Pervasive Protocol Integration and Tooling Development: This initial phase focuses on laying the foundational plumbing. We'll see the widespread development of protocols like MCP designed to seamlessly integrate AI systems with all the traditional boundaries of software—databases, operating systems, file systems, and web browsers. This is about creating the conduits through which AI can flow freely. This is ongoing as we speak.
Phase 2: Comprehensive Framework and Tooling Accessibility: Concurrent with the plumbing, this phase is about creating the robust AI frameworks and tools themselves. These will be accessible across most programming languages, encompassing both modern machine learning techniques and the classical GOFAI methods like constraint satisfaction and search. The emphasis here is on simplifying complex AI tasks, making them approachable for the broader developer community. This is also ongoing in some domains, but we still lack developer-friendly tooling for the most common GOFAI techniques and traditional ML.
Phase 3: Extension of Foundation Models to Challenging Modalities: As the frameworks mature, we'll see a significant push to advance foundation models beyond common text and image data. This phase will focus on effectively processing and reasoning with more complex data types such as 3D spaces, tabular data, time series, and graphs. This tackles the "less-solved" domains, opening up new frontiers for AI application. We’re seeing right now an increased interested in World Models that can reason in terms of the physical properties of objects and better predict their interactions. We also need more work in models that can understand heavily structured data.
Phase 4: Mainstreaming of AutoML Techniques: This phase will mark the maturation of AutoML. These techniques, currently more prevalent in academic or specialized domains, will become sufficiently developed and integrated to move into widespread, mainstream development practices. This means that optimizing and selecting machine learning models will become a standard, almost automated, part of the development workflow. We already have pretty strong AutoML frameworks, but rarely any developer knows them, and they are lagging behind in integration (think AutoML running in your CI/CD) and deployment.
Phase 5: Ubiquitous On-Device AI Execution: Finally, a truly pervasive GPAI requires that AI can run almost anywhere. This phase is about ensuring that virtually any device can execute a "good enough" foundation model. Crucially, this will involve establishing robust operating system-level support for on-device AI inference, making local AI development and deployment a standard, seamless experience. This is already happening, but I think we need some breakthrough in hardware capabilities to make running a somewhat capable model, similar to what a 24B MoE can deliver today, natively in Android or iOS.
Given these phases and the inherent complexities, I believe we're looking at a realistic timeframe of 5 to 10 years for GPAI to reach a point where any developer on Earth can easily integrate AI into any reasonably complex application, much like they currently add a database or a graphical user interface today.
It believe this isn't science fiction. It’s ambitious, yes, but also a pragmatic and achievable goal.
Final Thoughts
Focusing on GPAI offers immense, tangible benefits: it promises genuine, measurable progress, the democratization of AI development, and ultimately, a much wider societal impact. It grounds our ambitions in what is actionable and truly useful, contrasting sharply with the often abstract and distant nature of AGI.
This isn't to say that the pursuit of AGI isn't worthy – it absolutely is, as a grand, long-term scientific endeavor. But while AGI might be the distant star we navigate by, GPAI is an immediate, well-lit path beneath our feet, a compass guiding us to real-world impact.
I'm personally committed to these ideas and to helping develop this vision. If you're interested in collaborating, please feel free to send me a direct message or leave a comment below. Let's build this future together.