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NVIDIA GTC CFP Speaking Session Analysis: Emphasizing Solutions Over “Things”

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NVIDIA’s GPU Technology Conference (GTC) just wrapped up in Paris, and GTC is often dubbed the “Super Bowl of AI”. I was fortunate enough to speak virtually on the topic of Explainable AI and attend the North American version in San Jose this year. I was impressed by the quality of the sessions and the breadth of subject matter covered. You can geek out on the cutting edge of AI techniques to discover some of the amazing things happening today, such as AI-assisted healthcare.

NVIDIA GTC Logo

Part of my day-to-day job is thinking about new interesting ideas and topics to present at AI/ML conferences like GTC. Because of this, I thought it would be worthwhile to examine what the NVIDIA GTC selection committee considers talks worthy of being presented in-person on stage. GTC might be hosted by the world’s leading GPU maker, but you’d be mistaken to think that it’s just about the hardware they build. In reality, the overwhelming majority of sessions focus on software, AI solutions, and what you can do with NVIDIA’s technology… not the chips themselves. This isn’t just my opinion; it’s supported by the numbers and the way GTC is organized. That’s the focus of this blog post here… let’s dive right in.

GTC Is About What You Can Do, Not Just What You Have

NVIDIA is no longer just a chipmaker cranking out GPUs. The company has been transforming into a full-stack AI platform provider. As one observer put it, NVIDIA went “from being a hardware company” to also wanting to be an AI software company. This shift is evident at GTC. The conference features hundreds of sessions across AI domains from computer vision and generative AI to robotics and healthcare… showcasing real-world applications and breakthroughs. In fact, GTC 2025 boasted over 1,000 sessions with 2,000+ speakers (in-person and virtual), covering everything from large language models to cloud computing and scientific discovery. The focus is clear: it’s on how NVIDIA’s platform is used to solve tough problems and transform industries, rather than on plain product pitches.

Problem Solving

This approach makes sense. People attend GTC to learn and be inspired by what’s possible with AI. They want to hear how a researcher used GPUs to decode the human genomes faster, or how a startup is deploying AI at the edge in hospitals… not a sales presentation about specs or cloud capabilities. NVIDIA’s own messaging around GTC highlights “breakthroughs happening now” and how AI is “powering the everyday brands that shape people’s lives” across various industries. The underlying hardware is crucial, but it’s the enabler, not the star of the show.

By the Numbers: Software and Solutions Dominate GTC Sessions

I took a deep dive into the latest GTC session catalog in Paris (this was also true of San Jose) and categorized the typical speaking sessions (excluding workshops, trainings, casual chats, etc). There were 485 in-person talks at the GTC event in this European event last month, spanning multiple topic tracks. The breakdown by topic highlights that software, AI applications, and data science topics dominate the agenda, whereas discussions on pure hardware are relatively few. Here’s a snapshot:

  • Generative AI: 81 talks (only 9 were sponsored slots, ~11% “paid”)

  • Simulation / Modeling / Design: 67 talks (2 sponsored, ~3%)

  • Data Science: 25 talks (0 sponsored, 0%)

  • Computer Vision: 21 talks (all content, 0% sponsored)

  • Edge Computing: 16 talks (0 sponsored, 0%)

  • MLOps (AI Deployment): 10 talks (all content, 0% sponsored)

  • Natural Language Processing: 6 talks (all content, 0% sponsored)

  • Tooling (Dev Tools, Frameworks): 33 talks (1 sponsored, ~3%)

  • Cloud Services and Infrastructure: 55 talks (27 sponsored, ~49%)

  • Hardware (GPUs & chips): 25 talks (16 sponsored, ~64%)

In plain terms, the “Generative AI” track alone had over 80 sessions, reflecting the huge interest in what people are building with large language models and AI creativity. “Simulation/Modeling/Design” was another big track with dozens of sessions. Traditional AI application areas, such as vision, NLP, and data science, collectively accounted for many talks as well. These are the kinds of sessions where speakers share research results, developer tips, and success stories using NVIDIA’s software stacks and GPUs.

Pay-to-Play

Notably, none of the talks in Data Science, Vision, NLP, Edge, or MLOps were paid talks… they were all merit-based content. The same was true for the vast majority of Generative AI and Simulation track talks (over 90% of those were non-sponsored content). This means the speakers earned their spot by having something interesting to say, not by buying a slot.

Contrast that with the Cloud and Hardware categories. The Cloud track (covering topics like cloud GPU services, data centers, and infrastructure) had about half of its talks come from sponsored sessions. The Hardware track, sessions about new chips, systems, and so on, was even more skewed: nearly two-thirds were “paid talks.” In other words, many of the sessions about cloud or hardware were more or less vendor presentations that likely came as part of a sponsorship package. This wasn’t a fluke or an oversight by NVIDIA; it appears to be by design.

Why Some Tracks Have So Many Sponsored Talks

If a conference track has a high percentage of sponsored talks, it’s a sign that the organizers expect those proposals could be salesy or promotional in nature. NVIDIA knows that a talk titled “Buy Our Cloud GPU Service!” isn’t what most attendees are eager to hear in any kind of session. The non-tech equivalent of this would be walking into a room only to find out you are trapped listening to a pitch to buy a timeshare in the Bahamas or something.

Not a Timeshare

By comparison, a talk titled “How We Optimized Healthcare NLP Models on GPUs in the Cloud” is much more appealing… if it focuses on the solution and not just a particular product. (Big if… if you ask me) GTC organizers seem to allow the more promotional talks a space at the table only if the presenters pay for the privilege (through sponsorship). This keeps the main content tracks filled with talks that are interesting to the audience on technical or innovative merits, and pushes anything overtly commercial into clearly marked slots.

Think about it: Hardware and Cloud are inherently areas where NVIDIA’s partners (or NVIDIA itself) might be tempted to pitch their latest and greatest. It’s not that hardware advances aren’t important… after all, NVIDIA is one of these hardware chip makers. GTC always has some exciting new GPU or system announcements, which are typically covered in keynotes or select sessions. But an hour-long breakout session that is essentially a product advertisement is not what GTC is curated for (by design).

Therefore, if Cloud providers or hardware vendors wish to present their offerings, they often appear as sponsors (the data indicates that 49-64% of those sessions were paid). This is a strong hint of what NVIDIA is looking for (and not looking for) in the session selection process.

What GTC Wants: Inspiring Use-Cases, Open Sharing, and Real Techniques

NVIDIA GTC sessions are meant to educate, inspire, and enable the audience of developers, researchers, and tech leaders. The best sessions tell a story of solving a real problem using AI and GPUs. They often involve sharing code, frameworks, or lessons learned that others can apply. In fact, open-source tools and projects are frequently front and center. From NVIDIA’s own perspective, many of their recent announcements have incorporated open-source elements; for instance, open-source foundation models for robotics were highlighted at GTC 2025. This open theme extends to the sessions, where speakers frequently discuss using open-source libraries (such as PyTorch and CUDA libraries), contributing to them, or building solutions based on open standards.

This alignment with open source is no coincidence. The open source community thrives on knowledge-sharing and collaboration, not on sales pitches. GTC, in spirit, tries to cultivate that same vibe. Attendees should walk away with new ideas, sample code, or a GitHub repo to check out, and a sense of possibility.

Knowledge Sharing

NVIDIA’s own conference description emphasizes how AI is transforming everything from healthcare to automotive to finance… again focusing on applications and impact. As a GTC attendee myself, I’ve noticed that the energy is much higher in sessions where the speaker is teaching something or demoing a breakthrough. To be fair, even the sponsored talks can contain useful info… but only if the speaker remembers to focus on how to solve problems using their tech, rather than just what their tech does.

Another trend… GTC content has become increasingly practical and concrete over the years. One conference veteran noted that the 2024 GTC was about “what if” future possibilities, while 2025 shifted to “what is”, thereby focusing on how to advance current AI projects to the next level. That tells us NVIDIA is curating sessions that speak to today’s challenges and solutions, not just wild future speculation. If you’re proposing a talk, you’re more likely to be selected if you can demonstrate tangible results or techniques that attendees can use now (or very soon), as opposed to just theoretical ideas.

How to Get Your Talk Selected for AI/ML Conferences

From the data and observations above, we can distill a few clear guidelines for anyone hoping to speak at NVIDIA GTC (or really any top-tier AI/ML conference):

  • Focus on Solving a Problem: Frame your talk around a compelling problem or use case you tackled with AI. For example, instead of a generic “About Our GPU Product,” present “How We Reduced Training Time for Medical Imaging Models by 80%”. Show the audience how you did it, and share numbers, insights, or code repo. GTC selectors appreciate real-world applications that have a tangible impact.

  • Keep It Technical and Educational: Contrary to popular belief, GTC is a developer-centric conference. Attendees appreciate code snippets, demos, benchmarks, and concrete tips. Don’t shy away from the technical aspect. Explain how you achieved your results (e.g., which SDKs, which algorithms, which optimization tricks). Make it a learning experience for the audience.

  • Avoid Marketing Hype: Steer clear of marketing fluff or self-promotion in your proposal and content, but above all, talk about something people actually want to hear. If you wouldn’t sit in on a session, odds are others won’t want to either. Phrases like “revolutionary platform” or slides of your product catalog are red flags, NVIDIA’s organizers can sniff out a sales pitch a mile away… and they’ll route those to sponsored slots (or reject them). Be honest and straightforward about what the audience will get from your talk.

  • Highlight Open-Source and Community Angle: If your solution utilizes open-source tools or you’re open-sourcing your work, mention that. Talks that share code or frameworks with the community inherently feel more like knowledge-sharing (which is exactly the spirit GTC wants).

  • Showcase NVIDIA Tech in Context: Since this is NVIDIA’s conference, it doesn’t hurt (as in you should at least mention) that your solution leverages their technology, but do it in a way that feels natural. It’s fine (and even expected) to use CUDA, RTX, or NVIDIA SDKs in your project; just don’t make the discussion about those tools themselves a product. Instead, make it about what they enabled.

  • Keep the Audience in Mind: Ask yourself, “What will someone watching my talk learn or be inspired to do?” GTC is meant to spark new ideas. If your talk proposal answers that question with something concrete, you’re aligning well with what GTC wants.

Wrapping It All Up!

To sum it up, NVIDIA is seeking talks that ignite curiosity and demonstrate to attendees how to achieve great things with AI, utilizing the hardware and software as tools. The hardware announcements will always have their place on the big stage (keynotes and a few deep-dive sessions), but the heart of GTC is all the amazing stuff people are doing with that hardware/platform. By structuring the conference this way, NVIDIA keeps GTC valuable and authentic for its audience. As an AI/ML developer and data scientist myself, it’s the only way to run a conference or get me to sit through a session at one.

The Full Stop Thought… if you want to speak at GTC (or any AI/ML conference for that matter), bring real substance. Tell a story of innovation or problem-solving, backed by data and live demos. Align with the interests of the community (open science, open source, and cutting-edge applications). NVIDIA’s selection trends show they favor the inspiring engineer or researcher over the slick salesperson; I have definitely noticed this myself while entering this very data science driven world. The GPUs and cloud instances are just means to an end… what matters is the awesome things you accomplished with them. Keep that focus, and you’ll not only increase your chances of getting a CFP accepted but also deliver a talk that resonates with one of the largest AI audiences in the world. And that’s a win-win for everyone.

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.

When did killers get popular?

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.