Lessons from Atlassian: AI is Like Your Dog, It Needs a Firm Master

AI, just like your dog, will eagerly try to please you but its assistance can sometimes be anything but helpful. AI, just like your dog, needs a firm master, warned principal developer advocate at Atlassian Dugald Morrow.

As one of the first speakers at the Shift conference in Kuala Lumpur, he delivered a clear message: use AI wisely, not blindly. 

AI needs guidance and supervision

Morrow illustrated how much AI needs guidance and supervision by dividing AI tasks into three levels.

• On the first level, AI performs well.

This is when it’s dealing with simple, well-defined tasks such as searching for information or processing raw data. 

• On the second level, AI can still perform, but often inconsistently.

This occurs when tasks grow more complex and require understanding the relationships between pieces of data – in other words, when analysis demands some degree of knowledge.

• On the third and most challenging level, AI fails.

This stage involves deep reasoning, judgment, and the creation of entirely new content – and here, the house of cards begins to fall apart. Yet our expectations for its ability to generate content remain enormous.

Despite appearing capable, AI lacks true wisdom

Although it appears capable, AI lacks the necessary wisdom, it works well within known data but struggles to go beyond it, Morrow explains. He even offers a diagnosis of AI: 

AI actually suffers from the Dunning-Kruger effect. It’s always willing to answer a question, but sometimes it’s not quite capable of doing so. 

He even goes a step further comparing AI to a dog: 

It’s got boundless enthusiasm, gleefully doing whatever you ask, though the results are often questionable or sometimes brilliant. We all know dogs have incredible senses – some can even sniff out cancer. Yet they can also do very silly things, just like AI can hallucinate or miss obvious facts.

What’s the cost of delegating everything to AI? 

This comparison works pretty well, although we could just as easily say that we’re the lazy dogs, demanding AI to do things instead of us.  

Morrow, who has a very interesting CV, having helped build submarine combat systems (for Collins-class submarines) and later an air traffic control system for Canada, in his exposé posed a question we’ve all asked ourselves: What’s the cost of delegating everything to AI? 

He offered a few answers to that, and they are not flattering.

While using AI in generating content you don’t become as familiar with that content as you would have if you generated the content manually. And you can’t evaluate if your content is of a high quality or not, he said. 

He warned the developers in the room that in their code may be hidden bugs or performance issues that they would have thought about if they had created it alone. 

Also he pointed out that overuse of AI erodes our skills to create: 

We all heard the practice makes perfect. You can’t learn tennis by just watching it or asking someone else to do it for you.  

96% of companies haven’t seen major AI gains yet

It’s tempting to use AI, but it’s really not necessary everywhere. How addicted we’ve become to LLMs and how often they fail to meet our expectations is shown in a recent Atlassian report. 

Although 62% of executives say that AI makes them more efficient, 37% say that it has wasted their teams’ time. Among the 12,000 people who participated in the research, 42% admitted that they trust AI too much. Perhaps the most striking finding from the report is that 96% of companies haven’t seen any dramatic improvements from AI yet. 

Therefore, Morrow proposes a managed AI approach, which means using artificial intelligence with the right level of human oversight, depending on the complexity and importance of the task.  

As we all know, not every task should be handed over entirely to AI.  

However, some rudimentary ones, like extending existing code patterns or fixing small bugs can be. In those cases, AI can safely work almost independently, since it’s simply repeating known patterns.  

He gave example of his own presentation which he made using Atlassian’s cloudapp development platform: 

The “spot image” didn’t display and showed a 404, so I used Robodev. It didn’t create a new image—just found the correct one and updated the code instantly. Since it was a simple, repetitive task, I trusted AI to handle it reliably.

When AI needs a firm master

But if AI has a task to build something entirely new, master has to be there. In this case Morrow advises breaking the work into smaller parts or defining a detailed specification before asking AI to generate code. That way, human stay in control of structure and intent. 

This is even more important when AI is asked to do complex algorithms.  

Developers should first define and document the algorithm in detail (inputs, outputs, logic) and then let AI complete it, suggests Morrow because that’s how AI has enough context to succeed. Otherwise, he claims, the good output is highly unlikely. 

He showed the code for his presentation and explained how he used AI to extract parts of a large SVG file for different slides, providing detailed descriptions and documentation for the extraction algorithm. 

To conclude, everything he said makes sense. The managed AI approach he advocates is essentially about balancing automation with human judgment. Rather than blindly delegating tasks to AI, developers should determine the appropriate level of guidance and oversight for each situation. As Morrow puts it: 

AI is like your dog. It’s a wonderful tool, but you need to manage it.