The Code More Traveled – Understanding The Two Sides Of AI-Driven Email Coding

Leading brands are investing in Artificial Intelligence (AI) like never before. It’s the talk of the town — AI-generated talk of the human-generated town. AI email campaigns can be easily generated online, and naturally brands are making the most of it. 

In March 2023, Microsoft announced the integration of the AI technology behind ChatGPT with one of their in-vogue tools, the Power Platform. Microsoft claimed that the integration would help users with little to no coding experience to develop sophisticated applications. 

Again, Google’s ChatGPT-powered AI Email Writer, though not related to coding, enables users (such as professionals, translators, non-indigenous English speakers, etc.) to generate quick, and human-sounding emails. 

AI is now part of the email industry as well. In fact, according to Litmus’s latest report, 41% of marketers say that AI email marketing generates higher returns.

Specifically, email coding in AI is quite the contentious bone. Some approve of it, others don’t. In this post, we will try to take an objective view of AI-backed email coding. Let’s get started!

How AI Generates Code

Before we go on to the nuances of AI email marketing, let’s be clear about one thing: AI does not intrinsically know anything about coding per se. What it does is generate relevant responses to prompts. 

AI relies on its own data depository, and combines machine learning (ML) and natural language processing (NLP) to discern patterns in a code, identify potential bugs, and suggest improvements. 

Take an AI tool like GitHub Copilot. The tool translates natural-language prompts into corresponding codes. Which means, even if you are not an expert/professional coder, you can feed instructions in English, and Copilot will churn out the right coding suggestions for you. 

Of course, you can feed prompts using codes as well. Notably, Copilot can generate code across multiple programming languages. 

Now, let’s look at the two sides of email coding with artificial intelligence.

The Bright Side: 

Increased Productivity

Not just coding, but AI email marketing at large turns to AI for increased productivity. Efficiency has always been one of the key promises of AI. 

This is good news especially for email designers. Not all designers are familiar with coding. Having designed an email, they must hand it over to the development team. In light of the speed at which AI-motivated businesses aspire to work, that’s one step too many. 

Tools like Sketch2Code can turbocharge the process. Microsoft’s Sketch2Code can crank out HTML codes from hand-drawn sketches. It relies on computer vision to do that. 

Suppose you want to design a sign-up form. First, create an initial layout (digitally or manually), then upload it on Sketch2Code. The tool will generate a web page-optimized view of your sketch. You can then download the HTML code, edit it (or run it by a professional coder), share it with other HTML codes, and run it on Microsoft Edge. 

So, AI-powered email marketing can come in handy for designers as well. 

Enhanced Accuracy

To err is human, to forgive artificial. 

Professional coders can now avail of AI tools that de-flaw codes by analyzing code length, variable names, backup code, interface syntaxes, etc. 

One such tool is DeepCode, which can analyze your code and alert you to inconspicuous errors. Many other tools come equipped with smart code editors, debugger integration functionality, and options for IDE optimization. 

What’s more, developers can also use AI tools to test the performance of their applications across a variety of platforms. 

Improved Scalability

Coding scalability refers to the capacity of a software to manage growth, say, in the number of users, complexity of operations, or data. Usually, the extent to which a software can productively cope with growth depends on the availability of physical memory storage space. 

With the advent of AI, however, the demand for memory storage has increased since machine learning models use excessive amounts of data which must be stored efficiently. Traditional memory storage systems cannot store so much data. 

Luckily for AI email marketing, there have been striking advances in data storage technology. 

“…Solid State Drives (SSDs) and NVMe (Non-Volatile Memory Express) technologies has revolutionized data storage and access speeds, offering significant performance improvements over traditional Hard Disk Drives (HDDs),” asserts Michael Roth, junior software engineer at Honeywell, and cofounder of Play and Plate. 

“In addition, emerging technologies such as 3D XPoint and Storage Class Memory (SCM) promise to bridge the gap between volatile and non-volatile memory, offering the speed of RAM with the persistent storage capability of SSDs,” Roth adds. 

The Dull Side: 

Stunted Creativity

One of the immediate drawbacks of email coding in AI is lack of creativity. It’s one thing to spin data to crank out unique content, quite another to “transcend” available data to produce sophisticated content, which can only be done by the human mind. 

As far as coding is concerned, it is a creative enterprise. One of the distinguishing features of a good programmer is their ability to synchronize seemingly unrelated things. One must find a creative solution according to a highly formal set of requirements. 

Interestingly, in his 1969 volume, Heuristic Programming: Ill-Structured Programs, American computer science researcher Allen Newell identified programming as a “design problem.” 

Meaning that programming is architectural pattern building. Like any creative work, it relies on the maximum use of our apophenic (pattern-making) tendencies, something which AI cannot boast of as yet. The human mind goes beyond mere permutations and combinations.

Even generally speaking, problem solving is essentially a creative enterprise, involving the human will. 

Elegant coding (human-dependent) is way more valuable than correct coding (AI-generated.) After all, the success of AI-based email marketing is not something solely determined by crude algorithms, is it?

Protracted Code Review

Codes can get pretty messy when one is reusing templates or refurbishing old emails. Hence the need for code reviewing. While handing messy codes over to AI may be useful, it is equally important to remember that AI-assisted coding is accurate only 37% of the time. 

To a particularly conscientious developer, constantly checking AI-generated code is frustrating. Besides, knowledge of complex codebases, patented libraries, and project-specific layouts is important to flag quality issues within the code, something which AI might overlook, accruing technical debt, thereby limiting the potential of email marketing with AI. 

Because AI is fundamentally trained on context-independent data, it might flag valid codes that may only be contextually relevant. 

This is the problem of false positives. AI is yet to be able to review codes in accordance with a programmer’s specific intent. Tools only operate on the basis of an axiomatic set of coding rules. Similarly, the problem of false negatives (missing actual errors) is equally serious.  

Ethical Concerns 

Email marketing with AI must grapple with certain ethical concerns. All AI tools have been trained on open-source software. Which means AI uses coding written by other developers in order to generate its own. 

So, the question arises: is generative AI plagiarism? While some contend it’s not, others insist it is. The former group argues that unacknowledged collaboration alone is plagiarism. So, if a developer has acknowledged openly the assistance of AI in their project, it’s not ethically questionable.

The other group maintains that it is nothing but plagiarism. Significantly, using AI is not plagiarism in the traditional sense of the term. Paradoxically, it is original and unique plagiarism. Here, it becomes crucial to distinguish between “original” and “unique.”

Etymologically speaking, “original” implies “primitive/native,” whereas “unique” means “one of a kind.” Let’s say that original content is ultimately independent of external resources. Unique content is dependent on a first series of rigorously shuffled data points, the unjumbling of which is not impossible.

Plagiarism in the AI era may be unique without necessarily being original, which is what makes it vulnerable to detection by MOSS (Measure of Software Similarity). MOSS can easily detect patterns in AI-generated coding. 

That being said, the definition of plagiarism is still being broadened to include new advancements in AI. According to a recent study, AI systems can escape MOSS detection, and be used to complete introductory-level programming courses. 

In light of that, is generative AI plagiarism? Is AI-based email marketing fundamentally unconscionable? The jury is still out. 

Wrapping Up!

Generative AI is a highly debated topic these days, covering a sweeping range of concerns, from technical to ethical. Generally experts maintain that until more advanced tools are developed and made commercially available, the future lies in an integrated approach to business solutions, that is, combining human creativity with AI efficiency. 

And yet, as we saw, it’s not a level road to walk on. However, one thing is certain: professional expertise and native intelligence are far from being endangered.