Beyond the Resume: Speculative Hiring Trends in an AI World

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I was recently at a conference where I started chatting with a computer science graduate about job hunting in the world today. We discussed the job market landscape as it exists today and all of the economic influences, disruptive technology (cough, AI, cough), and competition out there.

Old School Cell Phone

When I first jumped into my first professional job out of college, the world was entirely a different place. Social media was still an infant, we were rapidly approaching the dot-com bubble, and we were a few years away from (real) smartphones becoming available. In this conversation, I reflected on what challenges I faced then and wondered how I would react to the demands and problems faced in the current technology climate.

Having been on both sides of the aisle when it comes to the interview process as an interviewee and building one of the best teams as an interviewer, I thought it might be good to share the conversation I had and also expand on it a little further having had a little more time to think about it. Let’s dive into it…

How LinkedIn Connects Candidates to Employers

LinkedIn has become the go-to platform for recruiters seeking top talent, but it’s evolving beyond a simple job board. The platform has adjusted its algorithm to prioritize actively engaged candidates – those who post updates, comment on other people’s posts, and interact with their network. Simply having a profile and being logged in isn’t enough anymore. Recruiters want to connect with individuals who use the platform daily in the event an opportunity finds its way into your inbox, and, second, that you demonstrate interest and expertise in their field through their activity.

Look at this from the recruiter’s and LinkedIn’s point of view. If you are paying buckets of cash for LinkedIn Hire to find a candidate for an open position, you, as a recruiter, ideally want a response to each message sent. Also, LinkedIn doesn’t want to connect individuals to a recruiter who might not respond. This is the summary of the interaction right here… full stop. This shift means job seekers need to rethink their approach.

Be Active On Social Media

Staying visible requires active participation, from sharing industry insights to engaging with thought leaders. Those who embrace this shift can significantly increase their chances of being noticed and approached for opportunities. In contrast, passive candidates who only update their profiles when job hunting may find themselves overlooked. Being “active” on any job platform (especially LinkedIn) usually means you will reply to an inquiry for an open job.

AI Will Kill the Resume

The rise of AI tools has transformed the job application process, making it easier than ever for candidates to create tailored resumes that align perfectly with job descriptions. Tools like ChatGPT can generate highly customized resumes that match job listings with striking accuracy. I haven’t done this myself because I am very selective of the positions that I am seeking. Still, as someone more open to different types of work, using a prompt that mashes your resume and the job description together, I am guessing it might put you at the top of the list.

Be Active On Social Media

However, this has created a significant challenge for recruiters… many candidates look great on paper but lack the actual skills needed for the job. This trend has led to an increase in candidates getting through the initial screening, only to falter during technical interviews or practical assessments. I see a lot of chatter on subreddits where it’s been very difficult to land a job, let alone get a call back after the first interview. As AI-driven resume generation becomes more common, companies will need to adopt new strategies to verify a candidate’s true abilities before moving forward in the hiring process.

As someone who has helped build teams, it’s VERY time consuming hiring people. The time spent on the interview process is time spent away from doing my actual day-to-day tasks; unfortunately, that work doesn’t stop just because I am interviewing candidates. Even back then, I was very selective about the individuals who got an email for an interview.

How Do You Prove Competence?

With AI making it easier to “embellish” resumes, the challenge for employers is determining whether a candidate truly possesses the skills they claim. Just as students can use AI to complete homework without fully understanding the material, job seekers can list expertise they don’t genuinely have or may just have passing knowledge in. This presents a costly dilemma for businesses… how do they identify qualified individuals without wasting resources on lengthy interview processes?

Be Active On Social Media

Organizations are adopting different screening mechanisms, such as skill assessments, project-based evaluations, and real-world problem-solving tests. Conducting multiple rounds of interviews can be expensive and inefficient, so refining the process to quickly filter out candidates that may not be a good match is crucial to maintaining productivity and hiring success. I think we are in the middle of this shift right now.

Having said that, I hope this isn’t a new era of “Interview 2.0” questions because you know… all software engineers need to be able to tell you how to get 4 gallons of water using only a 3 and 5-gallon jug or to estimate the number of trees in Central Park. Although, I would rather do that than have a week-long programming assignment to prove I know how to program. Trust me, I have declined many of those because it’s like I have an infinite amount of time in my day and love doing work for free.

Public Speaking and Open Source May Hold the Answer

So what do we do about this particular problem?

To address the challenge of validating skills without extensive in-person interviews, companies/interviewers may want to turn to alternative proof of competencies, such as public speaking engagements and, for the tech world, their open-source contributions. Reviewing a candidate’s GitHub activity, technical blog posts, or recorded presentations can provide valuable insights into their expertise and problem-solving abilities.

GitHub Contributions

Although I never really looked at user content 7-10 years ago, I did look at GitHub and open source contributions on other platforms. With AI being able to generate code in any language these days, there is something to be said about supporting a product or an open source project. When a user/customer reports an issue, the project maintainer must triage, root cause the problem, and interact with another human being. This speaks volumes.

GitHub Contributions

Similarly, public speaking appearances or videos posted on social media platforms like LinkedIn, YouTube, etc, at industry events or webinars allow recruiters to see how well candidates can articulate complex concepts. At the end of presentations, there is inevitably a Q&A session where they aren’t going to be able to use ChatGPT to answer a question live in-person. These in-person examples or recorded sessions provide a more authentic measure of skill and commitment than a polished resume ever could.

The Full Stop Thought

So, where do we go from here? We are seeing some of these changes happen in recruiting today. I have heard of interviews where a link kicks off a recorded session, and you, as the interviewer, are presented with questions to answer on video for review later. I don’t know how effective this is, but I have heard of this happening. Is this a good solution? It sounds horrible if you ask me, but change is happening.

As someone who has been on both sides of the fence, the challenges in hiring today are interesting and unique, to say the least. However, there is something to be said for verifiable contributions, like GitHub or posted videos. As someone who thinks social media has done a number on society and who only has socials for work-related purposes only, I came to one possible answer… this content can provide a window into someone’s vested interest in topics they chose and how they demonstrate understanding of that topic.

Until next time!

Will AI Replace Mid-Level Engineers by 2025? Not So Fast, Mark Zuckerberg

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It’s hard to ignore the growing buzz around artificial intelligence (AI) and its potential impact on various industries. Recently, Mark Zuckerberg predicted on Joe Rogan’s podcast that AI could replace mid-level engineers by 2025. While it’s a compelling narrative, it misses the mark for several reasons. Let’s unpack why this prediction is more hype than reality.

Companies Aren’t Fully Using Their Own AI Tools

Take Salesforce as a prime example. The company has heavily promoted its AI-powered sales agents, touting them as the future of sales. Yet, if you look at Salesforce’s own career page, approximately 75% of their job postings (775 out of 1035 as of Jan 16, 2025) are for sales roles. If their AI tools were truly ready to replace human salespeople, why wouldn’t Salesforce “dogfood” their own product, slash sales jobs, and reap massive savings?

Dogfooding Gone Wrong

This disconnect isn’t unique to Salesforce. Many companies pushing AI solutions still rely heavily on human expertise to deliver the results they promise. It’s one thing to sell the dream of AI-driven automation, but it’s another to trust your core operations to it. If organizations like Salesforce, which stand to gain the most from successful AI adoption, aren’t betting the farm on their own tools, why should we believe AI will displace engineers en masse at other companies?

AI-Generated Code Still Needs Maintenance

Even if AI can write functional code, that doesn’t eliminate the need for mid-level engineers. All code, no matter how well-written, eventually requires updates. Security vulnerabilities need patching, APIs evolve, dependencies get deprecated, and business requirements change. Who’s going to handle these inevitable maintenance tasks? AI might be able to assist, but it can’t completely replace the nuanced understanding of a system that a human engineer provides.

Consider the metaphor of AI as a power tool for software development. It can make some tasks faster and easier, but someone still needs to wield the tool, know how to use it safely, and fix the mess when something goes wrong. Far from making engineers obsolete, AI tools are likely to amplify their productivity—and perhaps even increase demand for engineers who can effectively integrate these tools into their workflows.

AI Generated Code

If companies like Meta actually moved forward with replacing most of their mid-level engineers, they’d quickly find themselves in a “foot-and-gun” scenario. Without a robust team of engineers to maintain and adapt AI-generated code, systems would break down, product development would stall, and customer trust would erode. It’s a short-sighted strategy that prioritizes immediate cost savings over long-term resilience.

Selling the Promise of AI Is in Their Interest

It’s no secret that tech giants have a vested interest in promoting AI as the next big thing. AI and machine learning are lucrative business lines, and hyping up their potential is a great way to attract investment, sell products, and capture headlines. By framing AI as a technology capable of replacing entire swaths of the workforce, these companies generate excitement and urgency around adopting their solutions.

Heck, I am an AI/ML Engineer… I am in the space promoting the same thing, but my views on AI/ML is that they are HIGHLY strategic tools to be used by people. Replacing mid-level engineers isn’t just a technical challenge; it’s a strategic one. Engineering teams don’t just write code—they collaborate, solve complex problems, and adapt systems to changing business needs. These human-centric tasks are not easily outsourced to AI, no matter how advanced it becomes.

AI Generated Code

At the end of the day, humans consume the products that these companies produce. Until that changes, people will make the decision on what to buy and companies need to persuade those people to choose to buy their products. AI/ML systems don’t understand why things go viral, why we collectively like what we do, and why things like Hawk Tuah or Luigi Mangione captured our collective attention. Would AI have predicted that a good number of people would rally around someone killing another person? I think not.

The Full Stop Thought

AI is undoubtedly transforming how we work, and some jobs will inevitably be impacted. However, the idea that AI will replace most mid-level engineers at companies like Meta by 2025 is far-fetched. The reality is that AI tools are most effective as complements to human expertise, not replacements for it. Companies still need skilled engineers to maintain systems, adapt to changes, and ensure the quality of their products—and that’s not going to change anytime soon.

Here is the final thought… Currently, all AI systems today start with a user prompt. The keyword here is the user. Humans drive the direction of the work an AI system does because they aren’t self-aware of their environment. They don’t know what’s happening outside the digital world and the little box they live in. Until AI systems interfaces become a simple power switch without requiring a user prompt, these systems will need humans to direct what they produce. Period.

Voice Cloning: The Text-to-Speech Feature You Never Knew You Needed And Why It Matters

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Over the holiday break, I started experimenting with cloning my voice for reasons I will get to later in this blog post. As I walked down the list of Voice Cloning providers out there and began to evaluate them using my cost-to-benefit ratio scale, a set of requirements and must-have capabilities emerged.

In this blog post, we will cover what those required features are, why they are essential for my scenario, why I feel those reasons will transcend into the general use case, and, ultimately, what it means for text-to-speech providers moving forward.

First Some Background

I have been in the Natural Language Processing (NLP) space for over 3 years. In that time, as most people do, I started looking to obtain accurate transcription from speech and then moved into trying to digest conversation to create “computer-generated” interactions. Large Language Models (LLMs) dramatically accelerated the accessibility and, quite frankly, the ability to do so in a meaningful way without a lot of effort.

After comprehension, most individuals move into increasing the level of interaction by being able to interface with these systems using humans’ other amazing tool.. Hearing. As humans, we don’t want to talk into a device and then have to read its output. I mean, heck, most people find subtitled movies beyond annoying if those subtitles drag out for anything more than a few minutes. Here, we start to see the need for text-to-speech, but what kind of voice should we use?

How I Tried Automating Myself

That voice depends on the use case. More to the point, that voice depends on how familiar you are with the “thing” you are interacting with. I use “thing” as this catch-all, but in reality, it’s some device you are conversing with. Moreover, depending on what that device is and what our connection with said device is, the voice used makes all the difference in the world in the experience of that interaction.

Let’s consider these scenarios:

Siri, Alexa, or Google

These devices are simple. You say a command, and Siri, Alexa, or Google (hopefully) give you a meaningful answer. You don’t place much weight on what kind of voice it replies with. Sure, it’s cute if it replies in an accent or if it can reply in Snoop Dogg’s voice, but in the end, it doesn’t really matter all that much for that interaction.

Call Center, Tech Support, etc

The next wave of voice interactions is replacing humans with voice automation systems. This is where most companies are today in this evolution. There are a ton of companies trying to do this for a variety of reasons, usually led by decreasing labor costs.

The most common use cases are replacing customer support staff with these automated systems. Today, this usually entails using Speech-to-Text to transcribe what someone on the phone is saying, transcribing that text to pass it off to a Large Language Model (LLM) or, more correctly, a Retrieval-Augmented Generation (RAG) system for better context, and then taking the output and passing it through Text-to-Speech to generate a human-like voice to feedback to the listener on the other end of the phone.

That human-like voice is essential for many reasons. It turns out that when people on the phone hear a computer voice made by Felix the Cat from the 60s, they are more likely to hang up the phone because no one wants to deal with a computer unless it is important enough to stay on the line. That last statement is very true. If I really, really need something, then I am going to endure this computer-based interaction by not hanging up.

It all comes down to companies (and the people in the next section) wanting to keep engagement (i.e., not hanging up the phone) as high as possible because they get something out of that interaction.

Content Creator to Mimic Myself

For this last use case, not only do we want the voice to be indistinguishable from a human, but we also want that voice to sound EXACTLY like me. This is the use case I was exploring. I want that voice to sound personalized because that voice will be associated with my brand and, more importantly, a level of personalization and relatability to my content. That is done by creating content or using a voice that is me.
Why was I interested in this use case? In this age of social media, there has been a huge emphasis on creating more meaningful content. For those that do this for a living, creating content in the form of audio (i.e., Podcasts, etc.) and specially recorded video (i.e., Vlogs, TikToks, etc.) is extremely time consuming. So, wouldn’t it be great if there was a way to offload some lower-value voice work to voice cloning? That’s the problem I was trying to solve.

If you are looking to tackle this use case, then based on the Call Center use cases, having your real voice intermixed with an AI clone of your voice that is just slightly off will likely be off-putting. In the worst case, your listeners might just “hang up the phone” on your content. This is why the quality, intonation, pauses, etc, in voice cloning, will make or break the platforms that offer voice cloning. If it doesn’t sound like you, you risk alienating your audience.

Why Voice Cloning Is Important

For Text-to-Speech platforms out there, voice cloning will be a huge deal, but the mainstream is not there yet… This is not because the technology doesn’t exist (it does) but because corporations are still the primary users by volume in Text-to-Speech (for now). They are busy trying to automate jobs away to replace them with AI systems.

In my opinion, there is already a bunch of social media content being generated with human-like voices; case in point, the annoying voice in the video below. Just spend 5 minutes on TikTok. I think once people start to realize the value of automating their own personal brand/content on social media and it’s accessible enough for creators, you are going to see an explosion of growth on the platforms that provide voice cloning.

Those platforms that don’t offer voice cloning will need to at some point or die. Why? Why pay for two subscriptions where one platform provides human-like voice for the Call Center use case and pay another subscription for a platform that provides pre-canned human-like voice but also allows you to clone your voice for social media (that could also be used to create your own set of pre-canned voices)? The answer is you don’t.

Where To Go From Here

In this quest to clone my voice, I tried a bunch of platforms out there, and I found one that works the best for me, taking things like price and intonation into account. I may have a follow-up blog post about the journey and process I used to select and compare all the services. If those are interested, a behind-the-scenes of what I will use voice cloning for might interest people reading this post.

Until then, I hope you found this analysis interesting and the breakdown for the various use cases enlightening. Until the next time… happy hacking! If you like what you read, check out my other stuff at: https://linktr.ee/davidvonthenen.