The Technomist on AI: Volume 2024 - 2025 Outlook
Sifting Through 2024, Gazing at 2025
Hey there, thank you for tuning in. Before we close out this eventful year, I have summarized my most notable writings of 2024. So, let’s start!
Understand the Foundation: AI's Historical Context and the Lessons of Non-Consumption
“One age cannot be completely understood if all the others are not understood. The song of history can only be sung as a whole” – José Ortega y Gasset
We started with history. It's important to note that AI has gone through phases of progress and stagnation, commonly known as "AI Winters". In my view, a key cause of these winters was non-consumption, a concept popularized by Clayton Christensen, which describes how promising tech struggles to achieve widespread acceptance (and consumption)
Why did AI Non-Consumption Occur in the past?
AI non-consumption occurs when potential users are unable to effectively utilize AI technologies due to various limitations. Some of the limitations we had in the since the dawn of AI (50s,60s,80s,…) are:
Accessibility Issues: AI technologies, especially in their early stages, have not been widely accessible, often limited to a select group of researchers or well-funded institutions. A lack of internet access (DARPA was special) and infrastructure restricted the reach of AI, and made it unavailable to a large portion of the population.
Data was scarce, limited the ability to train effective AI models and validate neural ideas :)
Compute was, well, slow. Also, compared to today, there was a lack of hardware and computing infrastructure, which also limited accessibility.
Unsuitable Offerings: AI products, such as expert systems in the past, have sometimes failed to meet the needs of users, either by being too complex (and expensive) or not being able to handle novel situations (they were largely rule-based).
Negative Consumption Gap: Expectations of what AI can do exceeded what was delivered. This led to periods of disillusionment, aka AI winters.
It’s “Spring” but let’s not Jinx it!
Luckily, we're in a new "AI Spring" driven by pervasive internet access, big data, training/inference innovations, and powerful hardware. Unlike previous AI booms, we're seeing the evolution of language models, multi-modality, and their incarnations as agents, which are driving practical applications across industries with tangible productivity gains across different use-cases (e.g., coding, creative, etc.). That said, challenges remain:
Compute Access: Training advanced AI is expensive, concentrating power in the hands of a few.
AI Winter Risk: Overhype could lead to disillusionment and a market downturn.
Non-consumption: AI remains inaccessible or unaffordable in many areas.
Despite progress, we should continue to heed and learn from history and ensure AI becomes more accessible (e.g., via open-source AI) to unlock its full potential.
Obsess with your Customer
To truly unlock AI's potential and avoid the pitfalls of the past, customer obsession, as championed by Jeff Bezos, can be a guiding principle (amongst many). This means prioritizing customer needs and pain points above all else. We must build with and for the customer at every stage.
Key Tenets of Customer Obsession:
Technology as an Enabler: Employ technology strategically to solve customer problems and deliver innovative solutions, not as an end in itself.
Relentless Execution: Build the necessary infrastructure and teams to deliver on the customer-centric vision effectively.
Long-Term Vision: Cultivate customer loyalty and trust by consistently delivering value and balancing long-term goals with short-term gains.
Proactive Problem Solving: Anticipate needs and exceed expectations.
Focus on Experiences and Systems NOT on Tech Standalone!
By adopting a customer-first mindset, we recognize that a successful AI product is more than just a powerful model. It requires a holistic systems approach that considers the entire user experience.
A. Apply Adapted Whole Product Framework:
Inspired by Geoffrey Moore's "Crossing the Chasm" this framework emphasizes building comprehensive AI solutions that are not just technically sound, but also user-friendly and well-supported:
Core/Generic Product: The fundamental technology (e.g., an LLM).
Enablers: Additional components that make the core product functional and valuable (e.g., data pipelines, user interfaces, APIs). Represented as modular, petal-like structures.
Differentiated Product Layer: Unique value propositions that set the product apart (e.g., strong community, unique data, strategic partnerships). These form the product's "Compound MOATs".
Constraints: Customer/use-case specific needs that influence the prioritization of enablers and differentiators (e.g., enterprise customers prioritizing security).
B. Think about your AI as a System
To deliver on customer needs, we will have to utilize available tools (the best for the job) which would involve not just the standalone models (e.g., the LLMs), but interconnected (compound) AI systems. These "compound" AI systems may comprise of:
Data Pipelines: For acquiring, processing, and storing the data that powers the AI.
Knowledge Bases: Curated datasets providing context and enriching the AI's understanding.
Retrieval patterns/techniques (e.g., RAG): Enhancing context for language models, allowing them to access and utilize relevant information.
Intelligent Agents: Autonomous systems for complex tasks, extending the capabilities of the AI beyond simple input-output interactions.
User Interfaces: Intuitive interfaces that make the power of AI accessible and easy to use, bridging the gap between complex technology and user needs.
Robust Infrastructure: For model training, deployment, and operation, ensuring reliability and scalability.
Operations: To monitor, maintain, and secure the system, guaranteeing its continued performance and security.
...
C. Define Your Constraints
With a clear understanding of the customer and the need for a holistic approach (and the tech-stack), we can now turn our attention to the design process itself. Constraints are the guiding dimensions for building toward your customers and use case. They provide focus and direction, helping us to channel our efforts toward what truly matters. Try to answer the following questions:
What problem are you really trying to solve? Understand the root cause and the specific pain points.
What are the non-negotiables? Identify performance requirements, budget limitations, or compliance issues.
What matters most to your target audience? Are you optimizing for safety, speed, cost, or sustainability?
When you define/contemplate your constraints, you acquire clarity on which core product features, enablers, and differentiators to prioritize.
Dig Deeper and Optimize When you Have to!
Of course, the underlying technology still matters. An understanding of model training, architecture, and optimization is important for building tailored and effective AI systems that can deliver on the promises made to the customer.
Pre-training: Initial training on massive datasets to provide a broad foundation.
Optimizations:
Prompt Engineering/RAG: remember the model standalone will have its limits, know them and build around it.
Fine-tuning: adapt pre-trained models to specific tasks/constraints, employ available techniques e.g., LoRA for efficiency.
Efficiency Optimization: Employing methods like quantization to reduce operational costs and environmental impact.
…
Play Strategically, Understand the Market
With a solid product foundation, it's time to consider the broader market landscape. The AI market can be understood through two key dimensions:
Open vs. Closed Models: Publicly accessible and modifiable (open) versus proprietary and controlled (closed).
Direct vs. Indirect Business Models: AI as the primary product (direct) versus AI enhancing existing products and services (indirect).
Key Market Players:
New Entrants (e.g., Mistral, OpenAI, Anthropic): Mostly (with some exceptions) pursuing direct sales of their advanced AI models (now systems), often through APIs (subscriptions, platforming).
Incumbents (e.g., Meta, Google, Microsoft): Mostly (with some exceptions) leveraging AI to augment and enhance their existing, vast product portfolios (office 365, bing, search, you name it).
Understand these dynamics, position yourself where it makes sense.
Build Strong Foundations with a Solid Platform
In this dynamic market, platform engineering still plays an important role in accelerating the development and deployment of AI systems. Well-designed platforms provide the foundation for building good to great AI products, a dimension that is underrated by many.
Key Considerations for building a great AI Platform:
Purposeful Design: Align the platform with core business objectives and delivering specific value (yep I keep repeating this).
User-Focused Design: prioritize simplicity, ease of use, and incorporate continuous feedback.
Well-defined APIs: expose platform functionality through APIs tailored to specific AI personas.
Platform vs. Product: Don’t get lost in designing the perfect the platform, keep the core value in mind (what your shipping not only what you are shipping with)
Keep the Offence, but think about Defense (and MOATs)
As the AI market matures, you should start thinking about your competitive advantage, i.e., what’s your "moat"?. This will require deep thought about how you could differentiate. Here are a few ideas and strategies for building your “moat”:
Strong Community & User Engagement: Foster a loyal and engaged user base to create powerful network effects.
Purpose-Built Applications: Focus on solving specific problems and addressing niche needs to achieve natural differentiation.
Value Beyond the Model: Invest in research, build proprietary datasets ("data moats"), and continue to refine the product based on user feedback.
Differentiate at Multiple Layers: Be it building superior AI models, building highly efficient infrastructure, or crafting the “best” interfaces.
Compound MOATs: Combine many of those to build strong moats
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Build Responsibly, Safely, and Securely
Finally, as you build powerful AI systems, you’ll have to consider the safety/ethical implications. Strive to build RGAF AI:
Transparent: Understandable and explainable in its decision-making processes.
Fair: Free from bias and discrimination, ensuring equitable outcomes.
Accountable: With clear lines of responsibility for its actions and impact.
Beneficial: Designed to promote human well-being and societal good.
…
There is also great potential (read the full post above) for using AI to better safety/security. It’s always a double-edged sword make sure to weild it responsible (and gain from it at the same time!)
Outlook for 2025, Sayonara 2024!
That’s it for 2024; it was an exciting year for AI and for myself. Alot is going on, and alot will go on in 2025. Here are some 2025ish thoughts:
More Agentic: we probably will see more pragmatic use-cases here and new patterns that make agentic easier to use/integrate in various products and be applicable to many more use-cases. This will drive efficiency up and (hopefully) help reduce costs while keeping services up and running longer (without human intervention).
More RAG: Hallucinations, content freshness, accuracy, etc., are not solved natively (yet), so we will still need RAG (and fine-tuning) to make AI systems more usable/practical.
More Inference (and reasoning): Training might still wear the crown, but inference optimizations (and test-time compute) are coming for training in 2025.
More Multimodal AI: Maturing modalities of not just vision/images but also videos and faster transitions between all modalities.
Hyper-Personalization: We will probably see AI being used to build customized experiences for all of us. For businesses, this might translate to customer loyalty, higher conversions, and potentially a newly-found competitive edge (by just reusing existing data).
AI Productization/Operations: AI models/agents/systems, are all the “North” layers, we will see folks get more serious about the practical aspects of deploying, managing, and scaling AI systems in real-world environments (the “south” layers). .
AI at the Edge, On-Device, and Distributed Learning: we probably will see more progress toward federated learning, which allows AI models to be trained across multiple devices without sharing sensitive data.
Responsible and Ethical AI: In 2024, we saw trustyworthy/responsible AI blends, in 2025 we will potentially see more applications of it (if you are interested see this post which I authored through the LF).
Multimodality to Commands, more iRobots: better perception of the world through modalities, better action execution, equal smarter more usable real-world robots that can DO THINGs!
Quantum AI? Well, I’ll stop here, but I’d expect big announcements/discoveries in that space (like willow) getting us a step closer to seriously impactful applications of AI.
With all that musing above, do stay tuned for more writing and reach out if you’d like to collaborate (or want to tailor my writing to a specific topic) 🙂
That’s it! If you want to collaborate, co-write, or chat, reach out via subscriber chat or simply on LinkedIn. I look forward to hearing from you!