If you have been paying attention, then you have heard about the USD 12 Billion that Jeff Bezos and Vik Bajaj raised to develop an AI general engineer. One that can do what any other engineer can do – so it should be much like a global engineer. Read more here if you are not aware.
If possible, then what would that mean for professional engineers like us?
Bezos thinks this will help augment engineers and give them more time. Others think that all engineers will eventually be replaced. And what has been achieved thus far is not yet in the public domain.
In this article, I am not going to talk specifically about what Prometheus will or will not be able to do. Instead, I am going to use this as an opportunity to talk about the following:

1. What would be needed to train such an AI system?
2. What that means for the nature of such an AI system?
3. What would that mean for engineers working with such an AI system?
4. Should we even expect such an AI system to think the same way as human engineers?

I am not going to assume that I will know exactly what is being done on Project Prometheus. Nor that I will have the exact answers, but this conversation will help you and me think more about what’s needed to make an AI engineer and what it is to be an engineer in general.
What would be needed to train an AI engineer?
In my book on being a global engineer I noted what has been found about the way the best engineers think. In summary, they do 3 things really well:

1. They frame problems in a way that makes them easier to solve. They do not accept the problem as is. They look at it from different angles to find another way to define it – and then find an angle that makes the solution obvious and easier to implement.
2. They think systemically. They do not focus on just the challenge in front of them. They look at all peripheral elements for both challenges in accomplishing their goal and opportunities to help achieve those goals.
3. They use first principles. They do not make arbitrary decisions about the values of key parameters. They calculate the optimum value based on theory. They also use theory to understand the nature of any challenge they face.

Would an AI engineer explicitly be trained on these three attributes?
If so, then would we explicitly state these as needs or would we train the system to engage in these actions by default?
Not only that, but would we consider other, very human aspect of engineers?
One example is fixation. Where someone, in this case an engineer, stays focused on an idea that has come to them. It might be because it is the first one that showed promise, something they are excited about, or something that seems obvious because they have been working in a certain field for so long.
Fixation can, at first, seem like a bad thing. However, it has also been found to provide drive – and engineers have developed remarkable innovations by overcoming the challenges caused by this fixation. Sometimes better than what would be expected by someone objective who could see that fixation taking effect.
Would we want the AI engineer to show this tendency toward fixation to explore ideas fully?
Which brings us to another attribute: co-evolution
Many engineers talk about the need to iterate a design. I agree with the need, but I do not think the word “iterate” explains the depth of what is going on. The word “co-evolution”, I think, elicits the true deeper meaning of what is happening. As a solution is implemented in some sort of trial, new information is generated about the problem. So as we generate the solution, we better understand the problem. The solution and the problem evolve together.
As we co-evolve both the solution and the problem, we expand our understanding of the solution and problem space. It is like exploring a new land – but one where the terrain is the challenge we are solving. Some solutions find a path through rocky terrain that opens new areas to explore that we never knew about.
Do we need to find a way to train an AI engineer to explore like this?
If so, then is it all through simulation or do we need to run the physical tests under its instructions and then report back (expecting questions about how well we ran the tests)?
What does this mean for the nature of such an AI system?
Considering the above, do you feel that there is data enough to train an AI system to be a general engineer?
Personally, while I do think AI engineers will eventually be a thing – and eventually be better than us, I am not sure it will happen as a result of ingesting large amounts of data.
I think there will at least need to be some kind of reinforcement learning. Where the AI will adjust through trial and error to provide better outcomes.
That means the system will need some kind of objective measure of success. It will need to be able to assess how well an idea has performed. Maybe based on a numerical goal. Maybe on some other system that is more “reflective” so it can ponder how it could have done better – comparing what it actually did with other things it could have done.
This is something that many engineers do. You have possibly thought back to things you did years ago and thought “now why didn’t I do it like this?” I know I have done that.
So the AI engineer would need to have some kind of inner monologue. Is that possible?
Maybe it will actually need to be a team of AI engineers – each with different parameters from their own training – interacting with each other. Comparing the performance of each other’s ideas and then adjusting their own parameters to think in a better way.
So it might be that we never have a single AI engineer in the true sense, but a collection of AI engineers working together and then presenting output as if it is from a single AI engineer.
This considers the “cognitive” aspects. But what about the physical or real-world execution?

• Will the AI engineer call up suppliers to confirm current capabilities and use that as an input?
• Will it have hyper-generalised transducer (maybe a humanoid robot) that allows it to build and run prototype tests?
• It will likely be able to connect to rapid prototype machines, but will humans (with their nimble hands) still be the ones putting the parts together and finding (with their comprehensive senses) the ideal place to run the assembled product?
• What is the most economic execution of the above actions at this time, and what does that mean the AI engineer should be like?

The above does seem to point to the notion put forward by Bezos that the generalised AI engineer will be working with other engineers to increase productivity and free up the time for humans. For now, at least.
What does this mean for engineers?
If you do ever find yourself working with a general AI engineer, then it would seem likely that you will be using it to do the things that would normally be time consuming for you. Explore numerous ideas, source the appropriate first principles, conduct simulations and calculations, generate test plans, come up with questions to better refine the problem statement.
You could then share your own ideas to help move things along.
At first that seems like you get to enjoy the fun parts more. And have more free time.
 
However, in the continued pursuit of profits, I am sure there would eventually be fewer engineers working for the same amount of time. History just shows that’s how things go.
But how will you interact with the AI engineer?
Again, in my book on being a global engineer I covered how words are not enough to explain engineering concepts. How engineering is very much a visual thing – even though other senses can help better understand any challenge you are facing. How do you convey what’s in your mind’s eye to the AI engineer?
Engineers are either going to have to improve their CAD skills to quickly convey their ideas or work on their ability to sketch. You might be able to use words to ask for an initial concept, but sooner or later you will need to edit some sort of visual to convey what’s in your mind.
But will the AI engineer visualise the way we do?
Should we even expect such an AI system to think the same way as human engineers?
We have developed our engineering skills by working with the brain that biological evolution gave us. This does not mean it is the only way invention can come about.
There are numerous examples in nature of animals showing the action of invention. Sometimes it is thought to be a result of instinct from evolution and sometimes from actual intent. But, as we learn more, the definition of intelligence and creativity seems to broaden.
Such ideas have also been explored in literature.
In the Children of Time series, Adrian Tchaicovsky explores how creatures with multiple brains or limited memory capacity could evolve inventiveness. In some, there is a separate brain that takes the problem as given by the main brain and then returns a solution some time after processing. Others have an ability to install and uninstall knowledge so they can use what is needed for the respective task.
In the West of Eden series, Harry Harrisson explores the nature of intelligence and inventiveness of an evolved reptilian brain. Where all technological advancement is an iteration on previous efforts with no major leaps – keeping them away from metal working and machinery, but phenomenal selective breeding.
Given the diversity of thought that we have found in nature and what we can conceive when we put the effort in, we can expect the possibility of a general AI engineer that thinks very differently from how we do while still generating excellent solutions.
Will we be able to work with such an engineer? Will we automatically think it inferior? Will we try emulating it ourselves after it reveals new ways of thinking to solve engineering problems?
In this article, I have only covered the major and some select aspects of engineering cognition. I have also only covered a select number of engineering activities associated with the full implementation of any engineering solution. I have also only alluded to the importance of engineering teams. And the diverse nature of the potential forms of intelligence was given only a cursory coverage. To deal with them properly would require an entire book.
That means there is much more to explore and consider. I currently can’t make any solid predictions about how this will all end up.
However, I do now feel convinced that the effort to make this general AI engineer will reveal much more about what it is to be an engineer. Even if it fails. So I plan on following it in detail, and I think you should too.
As you think about it now, what challenges and opportunities do you see? What would you want a general AI engineer to be like?
Share your thoughts – this is a conversation I would like to extend so we can all learn more about what it is to be an engineer.

Here is a scenario you have likely been involved with if you have had even a short amount of engineering experience.
Someone in a meeting suggests something that is unorthodox. Others dismiss it – maybe even derisively. But the one who put it forward continues to think it is a perfectly good, and logical, idea to pursue further. Neither side agrees with the other nor even understands the other.
How can engineers, who should be using sound and rational reasoning to assess all ideas, disagree like this? Surely, they should be able to explain their point in sufficient detail to ensure clarity – and not degrade into unfounded disagreement that seems more like personal preference.
The reason for this is that at least one party (probably all) is relying on their instinctive response and they don’t actually understand that response.
If you take a moment now, then you can probably recall a time when someone presented you with an idea to which your initial response was just discomfort. You did not know exactly why you felt uneasy with the idea. You might have even acknowledged that their explanation as to why it was a good idea made sense. But still, you simply did not like it.
This was your instinct.
It might have been right. But if you can’t explain it, then you will not have agreement. And just so you know, engineers can’t say things like “It just doesn't feel right to me” and expect that to be argument enough.
For a global engineer, this can be more extreme. If your instincts are based on experience in one context, then those instincts could be perfectly correct for that same context. But in another context, which is more likely to be experienced by a global engineer, the likelihood of it being corrects is much less.
You therefore need to dig deeper into your feelings, and turn them into sound logic.
Such feelings come from your unconscious, which holds numerous memories. One of these memories was triggered by the idea put to you. Something you experienced in some way suggests this idea would not work.
So your job is to delve deeper into your mind and find what it is that generates this feeling.
This is much easier said than done. You will likely need to sleep on it before you realise what caused this feeling. However, as you continue to put effort into accepting the feeling for what it is and then trying to extract what caused the feeling, you will get better at this. Eventually, you will find that you can extract this information in moments and with little effort – because you have created the mental paths that allow for easy flow.
The other side of this same story is when you come up with an idea, but others just don’t get it. And you don’t know how to explain it well enough – because it is instinct, and not clear thought.
This time, the idea came from the same place as did your unease with other people’s ideas. And without those mental pathways, you just can’t get the important knowledge out to create the argument you need to explain what you are thinking.
Once again, you need to work at it – until it eventually becomes something that happens in the moment.
Once you do this, you will:

1. Better explain your ideas – releasing the genius within.
2. Only critique ideas that have real issues so that good ideas are not dismissed – controlling the idiot within.

So next time you have that instinctive feeling, dig into it. And reveal the genius you have within or foolishness you need to excise. 

This is the first engineering product review article I have done for this series. It’s a bit like the retro engineering articles, where we look at a previous engineering system to find examples of engineering expertise, but this time we take a look at a current product.
In this article it is the Henson Razor.
Summary of the Henson Razor
The Henson razor is designed around the precision mounting of a standard flexible blade. Further, the head is designed to create an intuitive alignment to guide the user. By mounting the blade precisely and guiding the user with alignment, the razor will shave the hair away without excessively scraping the skin.
I have used this razor myself, after being impressed by their explanations of the design and demonstrations, and agree with others who have reviewed it highly – it is an excellent razor.
So how did they create such a razor?
When you read over their website and watch their videos, you see numerous examples of great engineering practice.
Outcome-Driven Innovation
They definitely showed a Jobs To Be Done perspective and started with the user. They were able to pick their design goals:

• Minimise cuts on the skin
• Minimise the length of hair left after a pass of the razor
• Minimise waste and cost – not like plastic razors
• Minimise build-up of lather and hair to minimise the need to clean the razor while shaving

First principles
To minimise cuts and minimise the length of hair left after a pass of the razor, the designers knew that a well-controlled blade gap (the space between the blade edge and the leading surface of the head) and well-controlled blade exposure (how much the blade protrudes from the surface of the head that contacts the face) were essential. Not just nominally, but along the length of the blade.
Good control of location of the blade means precise manufacturing methods like machining.
Further, a bent blade is more rigid than a flat one. Therefore, if the blade is bent, then it will have more inherent rigidity.
In addition, a 30° cutting angle has been found to be the most comfortable for use.
Skin is more compliant than metal so it can be expected that the skin being shaved can align, at least to some extent, with the shaver surface.
Framing
In this instance, the framing became apparent given the above: design well-controlled machined parts that securely connect, and control the blade location so that it is bent to 30° at the cutting edge.
This does not make for a stereotypically ideal product – it is not well aligned with mass production methods for low cost – but it is well aligned with reality. Both commercial (what users want) and physical (how razors perform).
Systemic thinking
The designers noted that standard razor blades are ubiquitous and well controlled in their dimensions. Thus, there is an opportunity to leverage these in the design and there is no need to design or produce the actual cutting edge.
The head of the razor was designed to mount the blade as well as let the hair and lather flow through – achieving two goals with the one part.
Goal analysis
The above shows the core attributes of the expert engineer (first principles, framing and systemic thinking) at play. However, as the product (including the commercial aspects) was developed, other opportunities presented:

1. There was the chance to augment the prestige of the product by noting it is made to the same standards as aeronautical parts.
2. Anodization allows for a range of colours, which suits a market that likes customisation.
3. While the razor is expensive, it will last longer and uses cheaper blades so the lower long-term cost is now a feature.
4. This also allows for the eco-friendly nature of the product to be a feature as well – less waste (especially plastic).

Things to note
While I would argue that the Outcome-Driven Innovation was the start of the journey here (even if the engineers themselves do not know this term), I would not argue that the first principles, framing, and systemic thinking occurred in the order I covered them. It is more likely that these actions coalesced – along with the goal analysis.
Because the process started with the needs of the user, it became very easy to sell the product based on its features. This is a lesson for all engineers. If you are clear on what needs to be done, then it is very easy to explain the benefits of your proposed solution.
I have covered this product because I think it is a great piece of engineering. I have no links to the company – apart from being a customer – and I received no income for writing this article. I would recommend the product, but I would first recommend that you take a look at their website yourself to see how they developed this razor.

The importance of politeness for a global engineer might seem apparent. In different cultures there are different types of etiquette and thus different definitions of being polite. However, you might be surprised by its importance in engineering in general and how it can be a double-edged sword.
In this edition, I am going to explain first of all why politeness is important for all engineers, how it can also be problematic, and how to manage both extremes within a global engineering context.

First the good: So why is politeness important for engineering?
When we are polite, we ensure we act in a manner that keeps others engaged. Think about times when someone has been impolite to you. Even if they did not mean it. You felt less inclined to engage with that person. That in turn means you are less likely to share your ideas with them to gain input (preventing the identification of issues or the generation of new ideas). You are also less likely to contribute to anything they are working on (meaning they could encounter issues that would otherwise be noted earlier, or they will miss out on other ideas that could have been generated).
In short, politeness allows you to engage better with others so that you can improve your broader understanding of the engineering challenges you face. This is very important for concurrent engineering and shared situational awareness – both of which are related to systemic thinking. But it can also help with leveraging other people’s understanding of first principles and considering new frames.

Now the bad: And how can politeness be a problem in engineering?
As important as politeness is to good engineering practice, the success of any engineering system depends upon the fundamentals of reality.
The general goal of politeness and manners is to help us get along with each other so that everything is better ordered and more effective. That’s why we can feel that raising issues will disrupt the order of things.
But if you do not call out every issue you can, then there is a greater chance of an engineering system failure. Sometimes our politeness (sometimes in the guise of etiquette) will dissuade us from saying anything. It does not feel right to point out an issue in something with which everyone seems happy, or at least not too unhappy; even though we know the implications.

So what do we do about this in the global engineering context?
The most impolite thing you can do to someone is to not involve them. So get into the habit of thinking about who should be involved in the development and implementation of whatever engineering system upon which you are working. And then, of course, involve them.
How you involve them, however, can be very culturally dependent. Some cultures prefer you talk to the person’s manager to gain permission first. Some cultures have very clear “rules” on when you should and should not talk to someone – maybe you can talk to them during lunch or maybe you can’t. These are nuances you will need to manage on a case-by-case basis. However, it helps to err on the side of caution – you do not upset people by being too polite.
The next most impolite thing you can do is involve people who do not need to be involved. So also ask yourself if someone really needs to be involved or if you are insulting them because they have bigger issues to deal with. The CEO wants to know that welds are being properly checked, but they likely don’t want to spend their time reviewing the top 5 non-destructive testing machine technologies you have selected.
A system that can help with this is RACI. You might have heard of this – it is fairly common – but don’t worry if you have not – it is a simple system that is ideal for formalising this type of politeness. RACI is where you determine and document: who should be Responsible, who should be Accountable, who should be Consulted, and who should be Informed. Once you take this step, this system will compel you to think more deeply about how to best work with your colleagues and compel you to keep everyone informed. No-one feels left out, and no-one is bogged down in insulting minutiae.
Also, if you do this right (so take your time with it), then those who might otherwise feel they can’t speak out will be actively consulted. By having the RACI documented and shared, people know that they are expected to give their thoughts – and maybe even be accountable if they do not. This helps give them the confidence to speak up without feeling impolite.

Many engineers hope that they will come up with an invention that will be the foundation of a successful business. A business that will lead to wealth and their own legacy. However, this does not happen that often. And I know engineers who have tried taking this path, but failed, despite their excellent engineering ability.
What went wrong for them?
They had a good idea. A clever idea. One that only few could come up with. But it was not something that was needed – it was not the foundation of a business.
In this article, I am going to explain the difference between a clever idea and something that is a worthy invention. And from that, the mindset you need to ensure any idea you pursue will have a much greater chance of success.
The first problem is that people can get caught up in their invention. They like it, and they assume (or hope) that others will like it too. All they need to do is explain it to everyone – then everyone will want it.
But that’s not how it works. No matter how clever the idea is, others will not care about it unless it makes their life easier.
So the question is not:
Is this a good idea?
The question to ask is:
Who and how does this help?
And then, even more importantly:
How much does it help them?
If you can’t provide a solid answer to the last two questions, then your invention is just a clever idea – not something that will be the foundation of a business.
Don’t start with the clever idea. Don’t think that if you just keep on thinking, you will eventually come up with a great idea for an invention that everyone will want.
Instead, start watching the world around you and asking:

• What are people struggling with?
• What do people keep complaining about?
• What takes too long?
• What costs too much?
• What needs too many people?
• Where are resources wasted?
• What breaks too often?
• What is done badly because everyone has simply accepted that this is the way it is done?
• What makes people worry?

With this focus, you will have a genuine issue that you can focus on.
If you have read my earlier articles, then you will note how this is similar to outcome driven innovation, as developed by Anthony Ulwick. You don’t start by asking what product you can invent. You start by understanding what people are trying to get done. Then you understand where they are struggling to get it done.
That is much more powerful.
Because then the invention has a job.
It is not just a clever thing looking for somewhere to belong.
Have you ever seen the movie Robots? It’s a good movie so check it out when you get the chance. There is a successful inventor character called Bigweld who has the phrase:
See a need, fill a need.
That saying practically summarises this whole article in one sentence.
This mindset also changes how you should think about patents. Another trap for the engineering looking to invent.
A patent can be very useful. I am not dismissing them. If you have something valuable, and someone else could copy it, then protecting it can matter.
But a patent does not create value.
And it is not evidence that you have a good idea.
I know people who have made this mistake. They had an idea. They thought it was a clever idea. They took it to an investor. The investor said that they would not consider it unless it has a patent.
So they got a patent. They spent money. A lot of money.
And then it all failed – the investor was not interested once they heard about it.
Because it did not solve a problem.
If someone asks you for a patent first, then do not consider them a suitable investor. Instead look for someone who asks:

• What problem does this solve?
• Who has this problem?
• How often do they have it?
• What does it cost them?
• What are they doing now to deal with it?
• Why is the current approach not good enough?

And then asks:

• How does your invention make that better?
• Does it save time?
• Does it save money?
• Does it save resources?
• Does it reduce risk?
• Does it remove effort?
• Does it make something easier?

That last one is probably the simplest way to think about it. Most inventions that matter make something easier in some way. Not always easier for everyone. But easier for someone who cares enough.
And that is where a business can start.
There is another useful point here. If you really do have a good idea, you should be able to get interest from people who fund ideas.
I have heard this from multiple people on the financing side of inventions. If the value is really there, funding can be found. If the idea clearly solves a valuable problem, then people with money will be interested.
Investors will not always be right, but they see a lot of ideas. They see a lot of people who are convinced they have something. They are trained, or at least experienced, in looking past the excitement and asking whether there is a commercial reason for the thing to exist.
That means their response can tell you something.
If a professional investor can quickly understand the value, that is a good sign.
If several of them cannot, that is also a sign. Either this is not going to succeed, or you have not explained how your invention saves people time, money, resources, etc.
And you need to be open to either possibility.
Do you need to refine your presentation, or, do you need to move on to another idea?
This is difficult because your invention can become personal. You have spent time on it. You have thought deeply about it. You might have imagined the business it will spawn. You might have imagined the success. You might have imagined the legacy. And you think nothing need change – the path to success is clear and obvious.
But it’s not going to be like that.
So you need to change something. Maybe the way you explain it. Maybe the whole idea.
Either way, be like Bigweld – looking for problems first -– see a need fill a need.