Five Levels of AI Coding and how to migrate existing codebase up this pyramid

Dan Shapiro (Serial entrepreneur, CEO of Photobucket and Senior Research Fellow in Wharton's Generative AI Lab) has determined that there are Five Levels of the Use of AI in Code Development (mirroring the Five Levels of Driving Automation)

Level	Summary	Description
0	Spicy Auto-complete	The Engineer writes all the code, but uses AI for auto complete of the next variable_name or guessing the next couple of lines of code. Tab to accept.
1	Coding Intern	AI writes boilerplate code for standard procedures with well-documented instructions
2	Junior Developer	Human writes detailed specifications for requirements that may span with multiple modules. You don't specify how it should do it, but what it should do. The AI cranks it out. You still review it.
3	Developer	Human manages agents. Human feeds in specs, agents push code back to Human for review and approval.
4	Engineering Team	Human writes product requirements and test thresholds and Human only spots check output when system tests have passed. Human doesn't care too much about what the underlying code does - as long as it works AND doesn't fail.
5	Dark Software factory	No human writes the code or tests it - indeed it would be suicidal if they did. Nate Jones (see below) makes the point that the code generation doesn't know the test criteria during development (so not like XProgramming): the AI has to develop its own unit and system tests. Code-Gen submits it to a separate testing entity - a scenario that I envisaged this time last year. No-one is here yet (really).

How to apply to existing software

Here is Nate Jones on the migration path for existing software houses. This is an excellent video (and you should view the rest of it), but I jump to his commentary on how existing software providers need to implement this on their existing code base and development practices.

1. First use you use AI Level 1 & 2 to refactor existing code.
2. Use AI to reverse engineer your code to product specs that should have been written first time around (and maintained - haha - product managers know that specs are never maintained once the code gets into the hands of the engineer!)
3. Then re-engineer your development process to be AI first: new testing workflow (testing definition up front - how about that?!?) and different review processes. 
4. Next, new development uses autonomous agent development (and maintain the old code base using the former methodology).
5. Finally, cast off the old way of doing thing, restructure your organisation to solely adopt these new techniques.

I think organisational behaviour of existing software houses will be a HUGE barrier to adoption and there will be pitched battles when AI-gen and human-gen which may well rip apart companies.

Does this mean that only start-ups can get to Level 4 and Level 5? Yes, I think so.

How AI changes prices and competitive forces

Nate Jones (about, YouTube channel) is a well-regarded commentator on AI and pontificates daily - do bookmark!

Yesertday's analysis is a whopper - it's a must-listen. 

I agree on lots and lots of points, far too many to mention:

• Per-seat pricing model struggles when an AI-first world is fully embedded - it's not here now, but it will be. 
• The threat of AI is enough to make people renegotiate deals (KPMG is the example given)
• The value of having someone on a retainer to fix 'stuff' when it goes wrong is worth a lot

I disagree on one minor point: that organisations want to have software built that orientates around them and their processes. 

NO! If the business process that the software is managing is NOT a differentiating feature to the organisation, then it wants to adopt moderately good practice (not leading or bleeding edge, just moderately good). 

Why? Because they don't want the risk - middle of the road is just fine. 

So they are quite happy to give up their existing practices and adopt the processes dictated by the software because they know that others have adopted it too. 

No-one ever got fired for buying IBM

Herding vs being a lone sheep is worth a lot - an awful lot - the same amount that got wiped off stocks today? No, much more.

This story continues apace..... 

Pricing of AI

 This topic has been a real head scratcher – how do you charge for AI?

• By consumption (eg by tokens used or by transaction) – and the value of a token isn’t too transparent either
• By conversation

Salesforce CEO Marc Benioff made a pronouncement in December: per-seat with some fair use limitations (see The Register). 

AI is a significant plank in Salesforce’s strategy. It has made lots of investment in the tech and running AI has costs too of course, so it needs to evidence some return. 

Given that AI is touted to reduce headcount, the fact that Benioff is charging by per-seat might be surprising. However Forrester reports 

55 percent of employers regret laying off workers because of AI. More people in charge of AI investment expect it to increase headcount (57 percent) than to decrease it (15 percent) over the next year.

So Salesforce is signalling that AI is an enabler for employees rather than a separate profit centre. This decision is a balance between: 

• User-based pricing. AI is likely to loved (and heavily-used) by a minority
• Consumption-based pricing. This is kinda an open-book charging mechanism and the boys in Finance have no control on how big a bill employees might be running up.

In the end Salesforce believe understandable pricing outweighs matching cost to benefit, meaning that they’ll make some profit out of some customers and a loss on others. Forrester agrees – see Outcome-Based Pricing Is Coming — Be Ready For It 

Gartner is supremely optimistic about agentic AI in enterprise software applications

Gartner’s best case scenario projection predicts that agentic AI could drive approximately 30% of enterprise application software revenue by 2035, surpassing $450 billion, up from 2% in 2025.

Gartner sees five stages of Agentic AI evolution

This diagram needs some explanation - here's my understanding from Gartner's press release in August 2025.

Stage 1: AI Assistant

Gartner's prediction	By the end of 2025 most enterprise applications will have embedded assistants.
Gartner's explanation	They simplify tasks and interactions for users but depend on human input and do not operate independently.
Arthur's interpretation	Perhaps the application interface permits a user to type in an instruction into a bot interface and the app responds, rather than clicking through multiple pages eg 'Show me an exception report for all grocery orders received since 5pm yesterday.'
Arthur's assessment	'Most'?? This definitely didn't happen! Perhaps 10% by the end of 2026??

Stage 2: Task-Specific Agent Applications

Gartner's prediction	Up to 40% of enterprise applications will include integrated task-specific agents by 2026, up from less than 5% today.
Gartner's explanation	These AI agents have the capacity to operate and perform complex, end-to-end tasks. An example is an AI-driven cybersecurity threat response agent that scans network traffic, system logs and user behavior patterns in real time. The agent then assesses and initiates a response as appropriate.
Arthur's interpretation	Applications (perhaps from a single vendor?) can perform multi-step operations autonomously. What's the difference from today? Applications can follow a rules-defined process flow today. I think the new shiny thing is that the app can use AI to determine the process flow??
Arthur's assessment	Perhaps 10% by the end of 2026?? And it's likely that it is the same 10% as have achieved Stage 1.

Stage 3: Collaborative AI Agents Within an Application

Gartner's prediction	By 2027, Gartner predicts one-third of agentic AI implementations will combine agents with different skills to manage complex tasks within application and data environments.
Gartner's explanation	Collaborative agents will offer more adaptable and scalable solutions by learning from real-time data and adjusting to new conditions.
Arthur's interpretation	Some predictive or forecasting capabilities that use Machine Learning
Arthur's assessment	Err, I think this is an inevitable benefit from Stage 2 implementation??

Stage 4: AI Agent Ecosystems Across Applications

Gartner's prediction	By 2028, AI agent ecosystems will enable networks of specialized agents to dynamically collaborate across multiple applications and multiple business functions, allowing users to achieve goals without interacting with each application individually.
Gartner's explanation
Arthur's interpretation	There will be an approved list of AI Agents (from multiple vendors) that are permitted to collaborate together. Most likely I envisage a marketplace where vendors list their agents and their capabilities and customers pick and choose which can Apps can play together and in what circumstances.
Arthur's assessment	Actually, I think there will be a completely new breed of ecosystems that are native agentic AI first. (Indeed as a founding member of the management team at Fetch.AI, I would say that these ecosystems exist already.) Here's an announcement about the formation of the Agentic AI Foundation (AAIF) in December 2025 to do just that: Here's TechCrunch's description: Anthropic is donating its MCP (Model Context Protocol), a standard way to connect models and agents to tools and data; Block is contributing Goose, its open source agent framework; and OpenAI is bringing AGENTS.md to the table, its simple instruction file developers can add to a repository to tell AI coding tools how to behave. You can think of these tools as the basic plumbing of the agent era. The key question is how will successful will this new approach be and how will existing Enterprise Software providers respond? Students of disruptive innovation theory (like me!) are watching with great interest!

Stage 5: The “New Normal” for Democratized Enterprise Apps

Gartner's prediction	Gartner predicts that by 2029, at least 50% of knowledge workers will develop new skills to work with, govern or create AI agents on demand for complex tasks.
Gartner's explanation	As agentic AI matures, standardized protocols and frameworks will enable seamless interoperability, allowing agents to sense their environments, orchestrate projects and support a wide range of business scenarios Agents will be created on the fly by humans and humans and AI will collaborate in new ways.
Arthur's interpretation	I'm not sure what to think about this: I do think that most knowledge workers will use an AI agents to help them with complex tasks on a one-off basis. Whether many enterprise employees will enable an AI agent to exist permanently to achieve an objective (an objective that goes beyond improving their personal productivity) I struggle with for reasons of control, security, liability, cost. As a result, I think creating enterprise AI agents will be a specialist role within an enterprise.
Arthur's assessment	Nope, I don't think that this will happen in the way that Gartner articulates.