Before we get started - I’m working on a large paper on the current hype-state of AI, what’s actually real, and what’s hubris along with the slow steady march of progress that is actually happening. This is an extract from that work.

It’s also a bit of a “Star Wars: Episode 4” moment - I’m publishing the middle first - 7 predictions (that might age badly), but I think they’re interesting enough to publish before I finished the rest of the paper. The full thing takes a deep and introspective look at both the state, and the ethical problems we have with the current wave of AI. So if you’re about to “well actually” about some ethics thing or another, it’s cool, hold your breath, that’s part 3.

AI people, we really need to talk

I am not an “AI builder”, I’m a systems builder, and a programmer first and foremost – and I’m explicitly an “AI moderate”.

What I mean by “AI moderate” is that I think AI is simultaneously one of the most interesting and exciting things that’s happened in my technical career, but equally one of the most overhyped, misdescribed, poorly marketed and generally misunderstood pieces of technology.

AI is difficult to talk about because it’s become such a poorly debated, thoroughly misunderstood and quickly changing space that it makes understanding what is real and what is hubris hard to grasp.

It’s in the best interest of the people trying to sell you AI to over-hype it’s real capabilities, but dismissing it outright is a foolish thing because despite the ghouls that chase the tail of technology trends (Blockchain, NFTs, et al) being a large cohort of the people that are chasing this trend it’s truly not the same thing.

That’s easy to see when you look at the people really involved in building out and betting big on this technology. Neural Nets are not new, ML models are not new, transformer models are a little bit newer. Exceptionally smart people have been working to this point for a long time – this isn’t a get rich quick scheme – it’s decades of research and a huge amount of investment that’s been slowly rolling onwards for the last two decades in its current form.

This is all set against the controversial backdrop of the training data that’s processed by large organisations to produce vast general-purpose models that pushes against the edges of existing laws around fair use, people’s personal ethics and challenges human exceptionalism. I’m explicitly omitting the ethical discussion from the first half of this piece so I can dedicate the whole second half to it. Discussion of AI cannot exist in a social vacuum, but it also shouldn’t overshadow factual discussion.

This is my attempt to try summarising where we are, where we’re going next, and hopefully sift through what is real and what isn’t about the AI-hype.

There’s plenty of bad faith critique and evangelism in this space, so I’m going to try and neatly side-step both of those things in the following ways:

1. I have nothing to sell you.
2. I have no vested interest in AI other than having to hold the pen on a platform strategy that has to exist in the same universe as it.

But here’s what I think is going on in the industry and why.

7 Predictions - What’s next?

Let’s start with the big claim – there’s not going to be “AGI” – artificial general intelligence – in my lifetime.

The Star Trek myth of the sentient computer with the personality, the ghost in the machine, the AI of sci-fi which we have ethical quandaries about because it might be alive? That doesn’t exist.

It’s science fiction given what we’re currently working with, and each time you see another click-baiting post towards that thing its people clowning themselves. Obviously “never say never”, but that’s not where we are, and not what we’re building right now.

On the other hand, it’s exceptionally likely that we’ll have a collection of technologies and systems that integrate in such a way that if you squint at them might look, to the amateur, like we’re progressing towards that thing. People will absolutely be selling you “AGI” sooner than you think, but it’s going to be far more traditional than you expect.

I want to share my 7 predictions about what the next 5 years of “AI” looks like in practice:

1. The Future of AI is LLMs on the Edge, blended with traditional systems integration
2. The Future of Language Models is “Small Language Model Expert Systems”
3. This approach will lead to a renaissance in standards-based RESTful online services and model integration technologies
4. Websites and apps will decline in lieu of “Assistant Computing”
5. The building of those traditional systems will be AI assisted
6. Large models either have, or will soon plateau and won’t get drastically better
7. Software development jobs will change, but aren’t going anywhere

I think that until such a point where we start to have something “more” than three LLMs in a trench-coat, that a more honest name for what’s currently happening is “Model-Assisted Computing” and we should have probably used that rather than the hubristic “AI” and “AGI” naming. Hell, we could have even called it “MAC” for short.

I think if you start to draw a through line from Web 2.0 through smartphones and to the current rising tide of model-assisted computing, then you’ll realise that this is where we’ve been going the entire time and it’s mostly just a continuation of the vision of the web.

Here’s how it’ll happen

The existing wave of LLMs have shown that they’re exceptionally good at fuzzy matching human input. They’re statistical transformer models that predict output given an input, making them pretty good at what you’d traditionally associate with “Q&A” based on a training set. They’re really the next evolution of Google Assistant, Cortana, Siri and Alexa – the thing on the edge that can turn natural language questions into commands that need to be fulfilled.

As an industry, assistant-lead compute has been a thing since 2011 when Siri launched but was popularised arguably by Amazon’s Alexa in 2014. We’re 15-years into this and LLMs have given us a fuzz-matching technique that’s just plain better than defining platform-specific “skills” (to use Amazons terminology) for systems integration.

These large models will marginally improve over time, but they won’t be good at doing any hard or detailed work at all because they are purely statistically models. Most of the ignorant critique of LLMs focuses heavily on this point – that the models are “wrong” – because they’re not even trying to be right. Over the past 16 months we’ve seen the rise of Retrieval-Augmented Generation – a technique that interpolates data from data sources (frequently vector databases) into the outputs of LLMs so that they can source factual data.

RAG was the first step, followed swiftly by plugin models in GPT, but both of those things are rapidly giving way to two standard protocols – MCP – the Model Context Protocol, and A2A – the Agent-to-Agent protocol. Both protocols go some way to systemising RAG, exposing tools and resources for models to call out to, and wrapping models in web-standards for authentication and discovery.

When we get this right, the accuracy problem is solved – language models are used to interface with humans, and protocols revert to traditional systems integration techniques to perform operations and source facts, effectively giving us the best of both worlds. This isn’t speculative, both protocols have been in rapid development and adoption over the last 6 months and are probably the future of “agentic computing on the open web”.

What does this mean for builders? Back to web standards we go. The easier it is for the thing you do to be described as APIs, and metadata, and commands, the easier it will be to context shift into MCP and A2A workflows that interact with large models that by default will be enabled on everyone’s pocket devices. We’re going to be here in the next 6 months.

Layered on top of that, A2A offers some /.well-known style service discovery for agents – a place for you to define the operations that your top-level domains can provide. It’s an incredibly small leap from here to realise that this will eventually evolve into something close to a DNS registry of things that the runtimes that host large models have access to, which in the most open-minded place we can be is a great thing for services on the web (“hey Siri, check my bank balance for me”) and in the darkest places provides the platform operators of those large models a vehicle to deeply integrate, but also probably levy an app-store style tax on your systems from the outer edge. Still, a globally discoverable, automatically integrate-able set of commands across the whole internet is a wonderful enabling technology.

When we get there, people will start trying to sell you this as “AGI”. Probably. Because to the amateur eye, it might kind-of look like it.

The second order effects? The read-only portion of most webapps will sink under the substrate of agent-computing. Within a few years, it’s unlikely people will be opening your app or webpage to check on data. They’ll obviously still come to rich experiences for content (the web or apps aren’t going anywhere) but purely transactional things – “buy me that cinema ticket”, “check my bank balance”, “do the simple X” – anything that can be wrapped in a one-time step up auth flow, probably will diminish in importance and only rich interactive content will survive on the screens.

That’s going to be the consumer experience. It’s easy to doubt this now because plenty of the existing implementations of these things are rough approximations that absolutely suck (everyone can make fun of googles bad AI search, for example), but this isn’t that thing. This isn’t “can you work out how to sift through this data”, this is “I’ve told you exactly how to do this, follow this well-known protocol to do this well-known integration”. It’s how all your apps work today, wrapped in a thin veneer of language models to protocol shift your requests.

But it’s coming, because the technology mostly works now and the user-interaction is the place we’ve been trying to get for decades. Frankly, also, once you step back, it’s also good computing – computing that slips under the substrate of all the technology and returns to a place of magic. It’s good UI design – no UI.

What does this mean for technology vendors?

Just keep on keeping on, in a sense. Since Web 2.0 and the mobile app revolution, services that don’t provide APIs to deeply integrate have been F-tier ghetto services that people hate using. Unless you get better at systemising your… systems and meeting the market where it’s at with good machine-to-machine APIs, your business will eventually die.

But the reality is that most technology businesses are already living that experience in real-time. This isn’t new. What is new is that we’re going to see a cottage industry of language models and agents trained on proprietary in-house data sets wrapped up in APIs and sold as agents that the assistance-driven compute services can interact with like a marketplace (you can hear the mega corps salivating at taking their 15% already).

Organisations rich in data will realise that the same expertise they used to sell with humans can be synthesized and sold as commands and tools for these models to interact with, scaling their business.

From an engineering perspective? There are tonnes of APIs to be built. You’ll probably be using Copilots to help build them quickly, but you’ll still require more engineers than ever to operationalise them and make them work. This mirrors the last decade of real-world challenges in operationalising data engineering and machine learning. It’s not easy, it’s buggy and requires a lot of focus. The AI revolution won’t replace programming jobs, but it might take some of the repetitive work around the edges away.

We’ll see a rise in organisations operationalising “Small Models”, trained on their own data, and exposed as agents. We’re also going to see the platform vendors of LLMs training specialised small language models that can be run on local compute for domain specific tasks. This will partly be in response to the market asks (more secure, domain specific), but also as a loss leader into their platforms.

This is based on the fact that today, large language model vendors have working proof that they can train smaller targeted models from synthesised data that are as effective as LLMs in a lot of domains.

The large models will plateau – we’re already seeing this now. Compared to traditional software development, model development is a lot less deterministic. Successor models aren’t always strictly “what we had before but more”, and the steeper the climb becomes, the more we will rely on LLMs connecting to constellations of specialised models and tools to do detailed work. There’s a good chance we’ve already hit near the ceiling here given the current struggles to get the next-generation models out of the door. The cynics all seem to think this is “the end of GenAI”, but what it actually is the general point of utility where the model we have today become operationalised in more interesting ways.

Finally?

Well, engineering jobs will change. People are going to get over the hiring malaise (“surely we don’t need these noisy nerds anymore!”) and realise that the biggest challenge in software isn’t writing it, it’s operating and maintaining it. Software organisations will reach the conclusion that using the models to help maintain, remove, reduce and optimise code is a saner and more sustainable path than just pouring more code onto the tyre fire.

Just having more code doesn’t help anyone.

I think this is the obvious through-line, the predictable end of the path that draws together what we dreamt of for the internet (connected services exchanging structured data), the smartphone era (everything is an app), and the frontier AI fever dreams (we’ll make a sentient machine!) into something that’s both obvious and almost real today.

And everyone will say we’ve got AGI, and it’ll still be bullshit.

Footnote

For those about to reach for the comments section, the full paper looks like this:

The State of “AI”

• I am an AI Moderate
• Bad Faith Critique
• The Precision Problem
• Current AI tools are a better hammer
• 7 AI Predictions – what’s next?
• What does this mean for programming?

Ethics and the Adoption of AI

• Technology is a labour concern
• AI is incapable of art
• Reactions to your understanding of “value”
• Capitalism and AI
• Open-Source and AI
• The real-world costs of training and operating models
• A model trained on the web should be given to the web

Thinking Machines

• What if intelligence is a latent quality of data?
• Discovering a general-purpose computing approach

So do wait for the rest :)