Gen AI for Business Newsletter # 36: Special Edition: 12 Biggest Generative AI Stories of 2024

Gen AI for Business # 36 Holiday Edition: 12 Biggest Gen AI Stories in 2024

Grab your favorite drink, get comfy, and let’s dive into the key Gen AI stories that didn’t just define 2024; they set 2025 in motion.

Thanks for reading,

Eugina

# 1: OpenAI: the trailblazer that refuses to rest

Let’s be clear: OpenAI’s spot on this list isn’t because of all the drama (seriously, we’ve had enough popcorn this year). It’s here because, despite the noise, OpenAI continues to define what it means to lead in generative AI. GPT-4 Turbo stormed in, slashing costs and turbocharging performance because why settle for one when you can have both? Fine-tuning and memory? Delivered. Now, businesses can craft AI that doesn’t just play along but performs like it’s been part of the team forever. Multimodal capabilities? Nailed them. Their models now juggle text, images, and code like a multitasking wizard hopped up on espresso. And let’s not forget the ChatGPT app ecosystem, which exploded with third-party tools that practically scream, "We’re here to make your job easier!"

But here’s the thing: innovation never sleeps, and neither do competitors. Companies like Anthropic, Google DeepMind, and even Meta are throwing money like confetti and pumping out new models to steal the spotlight. Sure, being first to market is key, but staying there? That’s a whole different ball game, and OpenAI knows it. While others overspend and overrun, OpenAI keeps doubling down on what matters: constant, relentless innovation. Their AGI roadmap this year was as ambitious as it was terrifying, laying the foundation for a future where AI doesn’t just assist; it autonomously solves cross-domain problems.

What’s next? OpenAI’s next chapter isn’t just about turbo-charged models, it’s about their bold AGI moonshot. Will they make it? Or will AGI stay science fiction for another decade? Either way, buckle up because when they move, the whole industry shifts.

# 2 NVIDIA: The Unsung Hero Powering the AI Revolution (and Making Everyone Else Sweat)

Let’s face it: NVIDIA is the Gen AI MVP of 2024. Their GPUs are practically AI royalty, and their partnerships? Chef’s kiss. This year, NVIDIA teamed up with Microsoft to turbocharge Azure AI, giving enterprises faster, more cost-effective ways to scale. Then there’s Google Cloud, which leaned on NVIDIA’s H100 GPUs to run, you guessed it, everything. Oh, and don’t forget their collabs with startups, like Cohere and Hugging Face, providing cutting-edge hardware and software that makes deploying AI models as easy as ordering takeout.

Deployments? Everywhere. Healthcare embraced NVIDIA’s Clara, revolutionizing diagnostics and research. Financial institutions leaned on NVIDIA AI for fraud detection and risk management. And in manufacturing, NVIDIA’s Omniverse hit the factory floor, blending AI with digital twins to fine-tune production lines like it’s science fiction. They’re not just in tech; they are tech.

And now, the competition. AMD tried to flex with their MI300X chips, and Intel rolled out Gaudi 3, both claiming they’ve “caught up.” Cute. But NVIDIA’s head start with CUDA, a sprawling developer ecosystem, and partnerships no one else can touch means they’re still the heavyweight champ.?

What’s next? Unless someone cracks the GPU/software synergy code, NVIDIA’s dominance isn’t going anywhere. In 2025, the only question is: how far can they pull ahead?

# 3 Show Me the Money: ROI Becomes the Gen AI Litmus Test of 2024

If 2023 was the year of “just try AI,” 2024 became the year of “but does it pay off?” Enterprises worldwide tightened their belts and demanded results. According to a McKinsey report, 60% of executives cited ROI as their primary concern for Gen AI adoption this year—up from 42% last year. The hype is over; it’s time for AI to prove itself.

Key pilots are setting the stage for what “AI success” looks like. Coca-Cola’s AI-driven marketing campaigns reportedly boosted engagement by 30% in certain demographics, showing how hyper-personalization can pay dividends. Meanwhile, financial giant JPMorgan implemented AI in fraud detection, slashing false positives by 45% and recovering millions in fraudulent transactions. Even retail giant Walmart got in on the action, using AI-powered inventory management to reduce excess stock by 15% while keeping shelves stocked—talk about hitting the sweet spot.

But the reality is stark: many pilots didn’t scale because the cost-benefit math didn’t check out. A report from Gartner found that nearly 40% of companies piloting AI projects shelved them due to unclear value metrics or ballooning implementation costs. Lesson learned? ROI isn’t just a buzzword; it’s the barrier between Gen AI and mainstream enterprise adoption.

What’s next? 2024 proved that money talks, and in the Gen AI race, only the projects with clear, measurable impact will stick around to see 2026.

# 4 Big AI, Bigger Power Bills: The Energy Dilemma of Generative AI

Generative AI isn’t just hungry for data—it’s ravenous for power. Training large models like GPT-4 or Claude doesn’t just cost millions of dollars; it guzzles electricity like a server farm at an all-you-can-eat energy buffet. According to a 2024 study by the International Energy Agency (IEA), AI now accounts for nearly 2% of global electricity consumption—a number that’s climbing faster than your utility bill in a heatwave.

To keep up, tech giants are building new data centers at breakneck speed. Microsoft opened three major centers this year in Sweden, powered by 100% renewable energy, while Google invested in nuclear-powered data facilities in Ohio. And it’s not just the usual suspects; Saudi Arabia is constructing what they claim will be the largest AI-optimized data center in the Middle East, running on solar and natural gas. Meanwhile, Japan and South Korea are ramping up nuclear tech to support their growing AI economies, betting big on small modular reactors (SMRs) as a cleaner, scalable solution.

What’s next? Looking ahead, AI’s energy demands will shape global energy policy. Expect more partnerships between tech companies and energy providers, breakthroughs in nuclear fusion, and maybe even AI models designed to optimize their own power consumption. With energy-efficient chips on the horizon and nuclear partnerships gaining steam, 2025 could be the year AI powers up without powering us down. The bottom line? AI’s future isn’t just about smarter algorithms—it’s about smarter energy.?

# 5 Running on Empty: The Inevitable AI Data Shortage Crisis

If AI is the new gold rush, then data is the gold—and 2024 made it clear we’re running out of it. Training large models requires mountains of data, but the public internet is starting to look like a picked-over mine.?

Synthetic data emerged as the hero we didn’t know we needed—until we saw its flaws. While it helps fill gaps, Gartner reports that 40% of companies using synthetic data found it introduced biases, making AI models less reliable. Oops. And while we’re on the subject of bias, OpenAI’s use of synthetic data for GPT-4.5 training raised eyebrows for potentially amplifying existing issues. So, synthetic isn’t the silver bullet it seemed to be.

Now here’s the kicker: we’re sitting on a trove of untapped enterprise data—think customer interactions, supply chain logs, and all those emails no one wants to read. But unlocking it? That’s tricky. According to a McKinsey report, only 20% of enterprise data is properly labeled for AI use. Most of it is unstructured, siloed, and, frankly, a mess. Salesforce’s AI Cloud is attempting to tackle this with built-in tools for enterprise data labeling, and Databricks’ Lakehouse is helping companies centralize and clean their messy datasets. But adoption is slow.

So, what’s the future? Experts predict a massive shift toward data-sharing frameworks and marketplaces where enterprises can securely exchange anonymized datasets. IBM, for instance, is working on decentralized data networks using blockchain tech to ensure privacy and provenance. Meanwhile, Europe’s Gaia-X initiative is creating a federated data infrastructure for businesses to pool resources without compromising security.

The bottom line? We’re not just running out of data; we’re running out of good data. The next frontier isn’t about scraping harder or faking it better—it’s about mining the data we already have, ethically and effectively. Because, in the end, AI’s future doesn’t just depend on smarter algorithms; it depends on smarter data strategies.

What’s next? In 2025, the real power will lie in who figures out how to unlock enterprise silos ethically. Watch this space because data isn’t just the king—it’s the emperor.

# 6 Big Brother’s Playbook: The AI Regulation Tsunami of 2024

If 2023 was all about AI innovation, 2024 turned into the year of AI regulation—and it’s hitting harder than a GDPR compliance audit. The U.S. dropped some big moves with the Blueprint for an AI Bill of Rights, aiming to prevent AI-driven bias and protect consumers. Meanwhile, China doubled down on its AI regulations, adding stricter measures for generative AI models, with mandates on transparency and “patriotic data use.” And in Canada, the Artificial Intelligence and Data Act (AIDA) finally found its legs, requiring companies to audit and explain their AI systems.

But the biggest wave is about to crash in the EU. The AI Act, set to roll out in 2025. High-risk systems (think medical AI or self-driving cars) will require rigorous testing and certification, while generative AI models like GPT will need disclaimers for synthetic content and detailed training transparency. Oh, and if you’re thinking of deploying a “black box” AI system in the EU—good luck with that. The fines for non-compliance? Up to €30 million or 6% of global revenue, whichever stings more.

Here’s the kicker: while Big Tech is bracing for impact, startups are already sweating bullets. Compliance costs for small players could make the barrier to entry sky-high. But the silver lining? Regulatory clarity might finally weed out bad actors, leaving the playing field a little less chaotic.

AI and copyright, you might ask? By the time anyone noticed their work was being scraped by AI models, it was already too late. Generative AI tools trained on mountains of copyrighted content sparked lawsuits, debates, and hand-wringing, but the ship had already sailed. Now, it’s less about the what happened and more about the what’s next: regulating how AI scrapes, learns, and generates. That’s why copyright didn’t get a solo feature in my list – it’s baked into the broader story of regulation. AI isn’t unlearning what it’s already scraped, so the real focus now is on building guardrails for how it’s used going forward. Welcome to the "it happened, now deal with it" chapter of AI. Even newcomers like Perplexity.ai are switching gears, focusing on partnerships with publishers rather than relying on the wild west of web scraping.?

What’s next? Expect a global domino effect. The EU’s AI Act will likely inspire similar legislation in other regions, and countries like India and Brazil are already drafting their own frameworks. The era of “move fast and break things” is officially over. Now it’s “move smart and play nice”—or pay the price.

# 7 Geopolitics and Generative AI: The New Cold War Goes Digital

AI isn’t just shaping the future of business—it’s reshaping global power dynamics. In 2024, generative AI became a high-stakes chess piece in geopolitics, with nations racing to dominate the technology while corporations scrambled to stay neutral.?

Take Meta, for example. This year, its open-source LLaMA model was reportedly leveraged by Chinese military researchers to simulate battle strategies, according to leaked reports (The Washington Post). The backlash was immediate. To save face and keep the home team happy, Meta quietly worked with U.S. defense contractors to ensure the model could be adapted for American military use. The lesson? Open models are great for collaboration … until someone uses them for combat.

And it’s not just Meta. OpenAI faced scrutiny when reports surfaced that its API access was being geofenced to prevent certain countries from using its models for sensitive applications. Meanwhile, NVIDIA, the darling of AI hardware, had to dance carefully around export restrictions. After the U.S. tightened rules on chip sales to China, NVIDIA rolled out a “China-safe” version of its H100 GPUs. The result? A diplomatic tightrope walk between profit and policy.

On the corporate side, some companies are actively trying to sidestep the geopolitical minefield. Anthropic, known for its emphasis on AI safety, has limited its partnerships to "aligned" countries and explicitly avoids markets with regulatory gray zones. Even smaller players are feeling the heat: startups relying on cloud providers like Azure or AWS often find themselves caught in the crossfire of government directives.

Looking at the bigger picture, geopolitics isn’t just shaping AI – it’s being shaped by it. The U.S. is leading the charge with strategic investments in “tech alliances,” like the Quad AI partnership with India, Japan, and Australia. Meanwhile, China doubled down on its national AI strategy, pouring billions into homegrown models and hardware to reduce reliance on Western tech.

What’s next? In 2025, the geopolitical AI arms race will only escalate. Expect tighter regulations on AI exports, more nations developing homegrown LLMs, and strategic alliances shaping the global AI landscape. With the EU’s AI Act kicking in and the U.S. investing billions in AI R&D, the real question isn’t who’s participating—it’s who will lead.

# 8 Big Bets, Big Burn: The AI Investment Fever Dream of 2024

If you thought 2024 was the year to jump into AI, think again – unless you’ve got pockets deep enough to rival Scrooge McDuck’s money bin. Sure, global AI funding hit a jaw-dropping $135 billion (because why not throw cash at the future?), but here’s the rub: a lot of that money isn’t building empires, it’s going up in flames.

Take OpenAI, for example. Sure, they snagged a record-breaking $10 billion from Microsoft, but they’re also reportedly burning through $540 million a year just to keep the lights (and GPUs) on. Anthropic? They’re cozying up to Amazon with a $1.25 billion check and another $2.75 billion in potential funding. If money talks, AI startups are screaming.

And now, for the flaming dumpster fires. According to CB Insights, 70% of AI startups funded in the last three years are operating at a loss. Nearly 30% have already burned through their Series A funding, leaving them with nothing but a fancy logo and some regret.?

Why is everyone sweating? Because AI isn’t just expensive, it’s a black hole for cash. Training a model like GPT-4 costs tens of millions, and scaling it? That’s a whole other fortune. Deloitte estimates that AI startups spend 70% of their budgets on infrastructure alone, so unless you’re backed by a trillion-dollar tech giant, good luck staying afloat.

Even the big-pocket investors are catching their breath. VC funding for early-stage AI startups dipped 15% this year as firms realized that “hype” doesn’t pay the bills.?

What’s next? Expect cash-rich giants to scoop up struggling startups faster than a flash sale. And don’t be surprised if even some well-funded players quietly pivot—or disappear altogether.

# 9: Hold Our Beer: How Finance and Healthcare Are Owning Gen AI in 2024

While some industries are still stuck in a boardroom existential crisis about whether to train their own large language models (LLMs), finance and healthcare are out here saying, “Step aside, amateurs. We’ve got this.” In 2024, these two sectors not only embraced generative AI—they redefined how it can revolutionize industries.

Yes, marketing was one of Gen AI’s first playgrounds – personalized campaigns and automated copy were easy wins. But let’s call that what it was: low-hanging fruit. In healthcare, AI didn’t just pick the easy targets, it climbed the whole tree. Google’s DeepMind Health is spotting diabetic retinopathy at 90% accuracy, making preventive care look like a walk in the park. Meanwhile, Pfizer is letting AI take the wheel in drug development, predicting interactions and designing compounds like it’s auditioning for a Nobel Prize, and slashing R&D timelines by 40% in the process. Even hospitals are leveling up, using AI to optimize patient flow and cut ER wait times by 25%. Forget waiting rooms; this is the future we’ve been begging for.

In finance, JPMorgan kicked things off, unleashing AI to hunt down fraud with almost psychic precision. The result? False positives slashed by 45%, and they clawed back over $2 billion in fraudulent transactions. That’s not just ROI—it’s a mic drop. Over at Goldman Sachs, they handed LLMs the keys to their investment strategies, freeing up analysts from spreadsheet purgatory and gifting them 30% more time to focus on the big bucks. And retail banking? They’re not playing spectator. AI-driven chatbots are handling 80% of routine customer queries—cheaper, faster, and far less likely to roll their virtual eyes. (McKinsey)

Why are these two industries crushing it? They’re swimming in data—clean, regulated, and ready to feed hungry algorithms. Finance has decades of compliance-driven datasets, while healthcare is rolling in electronic health records, clinical trials, and imaging data. They didn’t sit around wondering if AI was worth it; they grabbed the tools and started solving problems that mattered.

What’s next? In 2025, finance and healthcare are taking AI dominance to the next level. Finance will see autonomous trading strategies that make Wall Street look slow and personalized banking plans crafted in real-time. Meanwhile, healthcare is turning predictive, using wearables to flag health issues before they happen and delivering DNA-tailored treatments. These two industries aren’t holding their beer anymore—they’re running the whole bar.

# 10: Gen AI Goes Local: The Rise of Domain-Specific LLMs (and Multilingual Powerhouses)

While GPTs, Claude, and LLaMAs dominate the headlines, 2024 was the year of the niche models—the domain-specific and multilingual LLMs that are quietly rewriting the rules. These hyper-focused powerhouses don’t try to do it all. Instead, they excel in their specialized corners of the world, outperforming their general-purpose siblings by up to 40% in accuracy and efficiency.

Let’s start with the industries. AgriAI became a farmer’s best friend, using decades of crop data, weather patterns, and soil metrics to predict yields with spooky precision. Midwestern farmers cut fertilizer costs by 15% and boosted yields by 10% during pilot programs (source: USDA). In law, JusticeAI is tearing through case law and drafting contracts 30% faster than legal teams, turning hours of drudgery into minutes of efficiency. And in hospitality? HostAI made waves by revolutionizing guest experiences. Marriott’s European hotels reported a 20% jump in guest satisfaction scores after the AI started personalizing everything from breakfast menus to sightseeing tours.

Now for the real show-stealers: the multilingual, local LLMs. India made a massive splash with BharatGPT, an AI that supports over 22 regional languages and is empowering rural banking, e-governance, and even localized healthcare solutions. In Japan, KawaruAI took the lead, excelling in kanji-heavy workflows like publishing and education while increasing productivity by 25% for businesses already adopting it.

Why are these models dominating? Because they’re built to serve their users. Unlike general LLMs that need layers of fine-tuning to understand niche industries or non-English languages, these models arrive ready to go—speaking the language and knowing the culture, inside out. It’s the difference between hiring a local expert and a foreign consultant who needs weeks to learn the basics.

And this trend is just getting started. Gartner predicts that by 2025, 60% of new AI models will be domain-specific or multilingual.?

What’s next? In 2025, domain-specific and multilingual LLMs will stop being niche players and start running the show. Expect tailored models to go deeper into industries—think AgriAI predicting not just crop yields but market prices or JusticeAI drafting airtight contracts while flagging legal risks in real-time. On the multilingual front, models like BharatGPT will scale to bridge digital divides while new players emerge in underserved regions, unlocking local innovation.?

# 11: On-Device AI: Small, Mighty, and Ready to Take Over 2025

Apple flexed with its A17 Pro chip this year, giving iPhones the ability to do real-time video editing, predictive text that finally gets your slang, and autocorrect that won’t embarrass you in group chats (about time, right?). Not to be outdone, Qualcomm dropped its Snapdragon 8 Gen 3, running AI models with up to 10 billion parameters right on your phone. Yes, billion. Google’s Pixel 8 said, “Hold my Tensor G3 chip,” and delivered offline transcription, real-time translations, and AI photo edits so slick that even your grandma’s blurry wedding photos look Insta-ready.

But wait—there’s more. Laptops are jumping on the bandwagon, too. Intel’s Meteor Lake chips turned ultrabooks into AI workhorses, with noise cancellation that drowns out your dog barking in the background and video conferencing features that somehow make you look like you slept. Lenovo’s ThinkPads now predict your next calendar move—because, apparently, they’ve been studying your habits better than your mom.

And then there’s IoT. Samsung’s smart fridges now suggest recipes based on what’s inside (who knew you could make gourmet meals from ketchup and kale?), while their TVs listen better than your teenager ever will. On-device AI is even popping up in wearables, with fitness trackers that can run health models on your wrist, predicting when you’ll need to hydrate or take a breather—no cloud connection required.

Companies like Apple are shouting, “What happens on your device stays on your device,” keeping your data out of the cloud’s clutches. And with zero latency, everything just works faster—because who has time to wait for the cloud to catch up? Plus, these AI chips are sipping battery life like a fine wine.

What’s next? Looking ahead, 2025 is about to turn the heat up even more. Gartner predicts a 30% jump in edge AI adoption, with on-device AI spreading to wearables, autonomous vehicles, and even smart home gadgets you didn’t know you needed. Think health monitors on your wrist predicting early signs of illness or cars that map out traffic without ever touching the cloud.

# 12: "Humans + AI: The Frenemy Power Duo of 2024"

Will AI take our jobs? Ah, the debate that just won’t quit. In 2024, the answer wasn’t “yes” or “no,” but more like, “It depends—are you paying attention?” Sure, AI automated many tasks, but it also created new opportunities. It’s not AI or humans; it’s AI with humans. And this year was all about figuring out how to play nice together—or at least coexist without flipping tables.

McKinsey reported that 20% of companies replaced admin tasks with AI tools in 2024. But here’s the twist: for every job AI replaced, it created 1.4 new ones. Think AI trainers, data whisperers, and creative collaborators—roles that didn’t exist five years ago. And companies are throwing serious cash at this transition, with $1.5 billion spent this year alone on reskilling employees to work with AI instead of against it. Who’s designing the AI workflows? Humans. Who’s interpreting the data? Humans.?

Startups focused on human-AI symbiosis pulled in a cool $8 billion in funding this year ( PitchBook). The message? Investors aren’t betting on AI in isolation—they’re betting on how AI amplifies us.

And let’s not forget innovation, the real star of 2024. The best breakthroughs weren’t AI working solo—they were humans and AI teaming up. Pfizer’s AI-assisted drug designs didn’t just save time; they uncovered three breakthrough therapies. NASA’s space missions combined AI predictions with human ingenuity for next-level discoveries. Even the arts got a boost, with human-AI collaborations creating new music genres and visual art styles we didn’t know we needed.

The winners in 2024 weren’t the ones picking sides; they were the ones figuring out how to make the relationship work.

What’s next? And next year? It’s not about “AI taking over.” It’s about who can harness it to amplify what humans do best. So buckle up, because 2025 is looking like the year of power partnerships. Who’s ready to thrive?

And There You Have It: The Biggest Gen AI Stories of 2024

Now, let’s address the elephant in the room: Why aren’t agents and “AI replacing everything” on this list? Easy. They’re not there yet. Sure, agents sound cool, and they’ve got potential, but for now, they’re still in their awkward teenage phase. The trends that will reshape the future? You’ll have to wait for the 2025 Holiday Edition to dive into those. Trust me, it’ll be worth it.

For now, let’s keep it real: 2024 wasn’t about empty hype; it was about tangible wins, major lessons, and groundwork for what’s next. AI didn’t just talk the talk this year – it walked, ran, and sometimes tripped, but it kept moving forward.

So, what hit the mark for you? Did we nail the stories that mattered, or did we miss a big one? What made you cheer, roll your eyes, or laugh out loud? Hit reply and tell me: I’m all ears, and your feedback will make next year’s edition even better.?

Because if 2024 taught us anything, it’s this: the future doesn’t wait for anyone, so neither should we.