AI with ALLIE

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Friends, before you read on, please note I wrote the majority of this while in bed with the flu (it’s sadly going around). What does that mean for the writing? It’ll be more off the cuff, may have typos, and will almost certainly have terrible analogies that a more conscious Allie would simply not allow for.

Here is my annual recap of the biggest moments and movements in AI—2024 winner list and 2025 predictions will be coming in a separate email. This is my 7th year recapping the top happenings in AI throughout the year, and I think my most “whoa, that was this year??” feeling. 2023 was more stressful, but at least the pre-ChatGPT and post-ChatGPT timeline felt more clear. I found myself multiple times thinking that something that happened in April of this year was from the 1800s. Time moves fast in the AI age, and it’s writing, and perchance reading, emails like these that make you feel that deeply.

The year of multimodality

This year, multimodality was everything. Boring text-only chat is dead, and every new release gave me head-to-toe goosebumps. Before I get into features, a lot of these capabilities only became possible when models like GPT-4o and Llama 3.2 were trained as multimodal from the start (which is why ChatGPT Advanced Voice Mode takes a breath when it’s counting fast from 1 to 50).

I dictate to Claude and ChatGPT dozens of times a day. I created a new AI morning routine for months where I talked to ChatGPT. Google Astra and ChatGPT with Vision means AIs that can see your screen and surroundings. NotebookLM can create a free natural-sounding podcast just by uploading a few documents, text, and links. I upload video files to Google Gemini and got it to label a cassowary based on a video frame of a cassowary’s foot. Nvidia even got into the voice game with Fugatto.

Search is fundamentally different now. LLM-based search systems will be vector-first and more multimodal (ex: search by video). Vectors can now act as a filtering function—your voice can act as a restriction lens, and just from voice input, your search results will be tuned to you.

We’re still not deep in 3D, but that always comes last and will be on my 2025 predictions list.

The year that AI agents knocked on the door

I’m sorry but the vast majority of people claiming that they use AI agents in their daily life are actually using AI workflows. The year of AI agents is 2025, not 2024—we did, however, catch those early glimpses (and I’ve been testing some early products not yet released, so I feel confident on that prediction). If you want a one-hour Introduction to AI Agents so you go into 2025 sounding smart, I am going to urge you to watch my live agents session here so you can also learn the difference between AI automations and AI agents and my predictions for AI agents in 2025.

Keep an eye out for Crew AI, ReWorkd, and other startups listed here as well as mega startup and big tech moves like Project Mariner from Google and Claude Computer Use, plus agent ops startups like Payman AI for agent payments.

The year that AI hardware failed miserably

It brings me very little pleasure to talk about failures, but we can’t talk about 2024 without touching on the painful plummets of AI hardware. That includes the cutely designed Rabbit AI whom The Verge called “unfinished” and “unhelpful”. I actually rerouted a flight so that I could go to the launch party in person—people rarely cheered during the speech and about 30 minutes in, folks were yawning—and still have never taken mine out of the box. Other hardware-related failures this year include the heavier-than-it-looks $700-plus-$24-monthly-subscription Humane AI pin, which The Verge described as “not even close” and which Marques Brownlee famously posted a video on titled “The Worst Product I've Ever Reviewed... For Now”.

We can add in the $3499 Apple Vision Pro (“a little too far out” said Wired, and “magic until it’s not” said The Verge), Limitless/Rewind, and O1 (which pivoted to software and refunded everyone’s purchase, including mine). We could even throw in the oh-so disappointing Apple Intelligence (which I’ll touch more on later) and the $800 children’s toy Moxie that shut down a few weeks ago. We saw many highlights in existing tech hardware providers like Samsung Galaxy, Microsoft Surface Pro, and Dell computer.

The one exception here, where a brand-new tech hardware offering is selling and selling well, is the Meta Raybans. Now, friends, I have tested these (thank you, Bilawal, for letting them steal yours), and wow. Fantastic audio quality and despite the audio being a targeted beam to your ear and not bone conduction, it’s actually really hard for someone near you to hear what you’re listening to. Despite this, I haven’t bought a pair.

Why? Well, first, I live in a small home in NYC and don’t have a ton of extra space for hardware. Second, I’m happier to be an early adopter when it’s a $25/mo subscription, less so on a $3499 headset. And third, it’s because it’s not the form factor I know I’ll use. As much as I love my Warby Parker glasses and my Epøkhe sunglasses, I don’t want to have to wear glasses every day.

Because, if I had to put my money on something, if the Monopoly man himself asked me to place a bet, it’s not on glasses at all. I’ve spent the last year going all out on voice dictation and speech, and my money is on Airpod-like AI hardware. Time will tell.

[I’m not even counting Meta Orion because those won’t be able to be productionized at that level for years. It’s more like an R&D project that reminds us that Meta is going to continue to invest billions in the AI revolution.]

The year of slow AI and reasoning

I believe the future of AI is three Ps—personalized, predictive, and proactive. In order to hit that, we need improved reasoning. I predicted a year ago that we would see the rise of system 1 and system 2 thinking in AI in 2024, and that is actually what we saw. Fast AI responses are important, but it’s not always the most important vector. In 2024, we really saw the concept of ‘test time’ or ‘inference time’ come out. Said another way, an AI that spends more time (even hours) ‘thinking’ of an answer.

We got o1 from OpenAI and an o3 teaser in the last few days of 2024—o3 scored 87.5% on ARC-AGI (the human threshold is 85%). In October, we also got Claude 3.5 Sonnet v2, which many people fight me on and say it’s not “slow” AI, but I would argue it includes nascent ‘reasoning’ because of a feature I call ‘next best action’ that came out with only the version 2 of the model. Anthropic got sneaky with this one. Vigilant explorers will have noticed that Claude attempts to predict your likely next request and proactively provides it or suggests alternate paths.

The year of AI-generated noise and truth questioning

Holy hell, enough with the bots. As someone with a large social media following, I have a front-row seat to the absolutely despicable madness on X and LinkedIn. It’s all become too much and depending on the algorithm, I’m hit with a barrage of “this new Microsoft offering surely sounds like it’s both new and from Microsoft!” drivel. Research estimates that we will run out of human-generated data between 2026 and 2032, and honestly, with the amount of bots, I’m guessing on the earlier side of that split. All the more reason to invest in synthetic data startups like Tonic AI, Mostly AI, and Gretel AI.

There is a lot more that can be mentioned here like fake Instagram Reels, faceless boring YouTube videos, low-quality blogs… just inauthentic human connection. Not to mention deepfake celebrity sex videos.

We’re seeing early signs of truth questioning (though I was expecting more during the election cycle and was happy to find it to be not as bad). I posted an AI avatar video earlier this year that received 20M+ views across platforms—despite mentioning in 5 places that it was AI-generated, I had several folks yell at me telling me I was just ‘acting’ and pretending it was AI. I assure you, friends, I’m not that good of an actress.

The year robotics just kept going

There were many people on social hyping up robotics left and right this year, I was not one of them (at least, not to that extent). It felt like a year of incremental improvement—we saw things like Nvidia’s Project GROOT, Waymo self-driving cars (important to note that my friends in SF now heavily prefer Waymo over traditional Ubers), Tesla Optimus (though maybe the demo was a tad faked), Physical Intelligence (a robotics startup that Bezos and OpenAI both invested in) folding laundry and enterprise robots like Dusty Robotics, which replaces people and measuring tape with a Roomba that can paint architect schematics with its butt, Agility Robotics for supply chain, and Berkshire Grey which literally drops FedEx packages down a tunnel to better manage packaging.

The year of AI automation workflows

2014 (10 years ago) to maybe 2016 felt like the RPA hype cycle peak. This past year, we have really moved into “APA” or AI Process Automation with tools like Gumloop, Make dot com, Zapier, Slack integrations, Siri shortcuts, and more.

My team held free live sessions on individual productivity and team productivity this year, and a lot of what we’ve incorporated and rely on as a team these days is AI workflows. One such example: every morning, the top AI news stories are perfectly searched, grabbed, de-duped, ranked, and summarized for us, along with a short podcast discussing all of the top stories.

The year of AI acceptance

I really don’t have a perfect news citation for this one; my source is reading hours of social media feeds and comments every day (a sad but true statement, as it’s how I understand society’s feelings, preferences, fears, and purchase patterns). 2023 felt like the year of AI anxiety and 2024 just sort of felt like…acceptance? I’m not saying we all have AI husbands or wives or therapists, but it really felt like people got more comfortable with the topic. People (especially gen z) started asking ChatGPT for insights about themselves, people (especially gen z and OpenAI employees) started calling ChatGPT just ‘Chat’, people (especially techie men) quite casually discussing AGI. I don’t want us to relax our shoulders too much; these AI systems, as we know, still have issues galore.

The year of AI and code

Pretty exciting year for both software developers and non-coders. Out the gate was Devin AI and Replit had a strong showing, but by the end of the year, Cursor AI took the AI with ALLIE crown with a rumored $50MM ARR after their first year in business. I even required everyone on my team to take Cursor training (cost a few hundred dollars and proactively blocked calendars to complete it and discuss it). Other coding tools that people seem to dig are v0 and Bolt. Claude 3.5 sonnet, Gemini 2.0 Flash, o1 pro, and presumably o3 in the future are also loved by coders.

For all you non-coders out there, please take immediate action and go to Lovable and set aside 2 hours to hack on something in English. It was one of the loudest “WOW” moments I had all year. That, and Claude Artifacts, which also is rooted in code generation.

AI and code are a match made in heaven because (1) devs have a much higher preference for AI than say your Legal department and (2) code and math can be more easily verified which means you can more easily perform reinforcement learning on it. For math, it either adds up or it doesn’t. For code, it either compiles or it doesn’t. I don’t think we’ve hit our peak on coding and math at all. And just remember, similar to Claude Artifacts and Lovable, not all of the use cases are for engineers, some may be abstracted away.

I feel like we also got the year of the “partial builder” in products like Uizard/Framer AI for websites, Claude Artifacts for apps, and Tome/Gamma/Beautiful for PowerPoints. The reality is that none of these are really ready for full enterprise use or get you promoted. My recommendation: use them for fast testing, like building out rough POCs during a team call.

The year of energy constraints and chip heat

The GPU shortage that's been haunting us since 2020 shows no signs of letting up, Nvidia's Blackwell chips are sitting at a 12-month backorder, and social media comments are lighting up with concerns about AI's water and electricity consumption. Microsoft signed a deal to revive the Three Mile Island nuclear power plant in Pennsylvania (committing to purchase its entire output for up to 20 years), and The Washington Post cited that data centers are projected to consume 17% of all U.S. electricity by 2030. Also, an interesting note, but many of these new energy sources won't even connect to existing power grids—they're being built exclusively for data centers, creating what amounts to private power networks presumably immune to potential grid-wide cyberterrorist attacks.

The chip situation is particularly nerve-wracking to AI providers. U.S. chip sovereignty isn't expected until 2030 at the earliest, and with Trump set to take office in a month, the fate of the CHIPS Act hangs in the balance (though interestingly, his pick for Energy Secretary, Chris Wright, has backed Fervo, a company using fracking techniques for geothermal energy—signaling some potential directions for energy innovation—this is not my expertise, but I suggest following Dr. Kimberley Miner’s work).

Despite (or perhaps because of?) these constraints, we've seen big investment heat in the chip and AI cloud space. Sam Altman put his money into Rain AI, Lambda Labs has been landing huge deals (congrats to many of my ex-AWS buddies!), and companies like Foundry and CoreWeave are snatching up top talent (including CoreWeave's brilliant new Chief Product Officer, also a former collaborator of mine from AWS). Energy is becoming a primary battleground for AI's future.

The year of AI advertising hell

I’m losing steam as I write this, but between the Google Olympics ad (they have since rebounded…a lot), the Apple Intelligence ad that CNET reviewed as “you’d have to be an idiot to like Apple Intelligence”, the Coca-Cola AI ad that tried to reinact a beloved spot from decades ago, and ToysRUs with an OpenAI Sora mess. To the marketers reading this I say, “DO NOT ADVERTISE IN A WAY THAT STRIPS HUMANS OF THEIR HUMANITY.” I’m available for advising and would have rejected all of them (maybe allowed the Coca-Cola one with a tweak or two). People may get more used to using AI in the ad process, but they won’t like being called un-intelligent in the process.

The year of more AI gatherings

If you haven’t yet gone to an AI conference in person, please make this the year you go. These convention centers and hotel lobbies are brimming with energy, and who knows, maybe you’ll see me on stage (I want to say I gave close to 50 talks in 20 cities on 3 continents…which means I’m two continents away from a very exciting goal of mine). Check out events like Cerebral Valley (SF), Slush (Finland), research-focused events (like NeurIPS, CVPR, ICML), HumanX, Ai4, The AI Conference, World AI Summit, or AI Summits in London/NY/SF. And if my team can really crack the code this year, maybe an AI with ALLIE event.

The year of model generations

Many thought in 2023 that OpenAI had an untouchable lead with GPT-4, but maybe there is no secret LLM sauce after all? Meta's Llama models, Mistral, Anthropic's Claude, and Elon's Grok have all caught up in capabilities—and perhaps more surprisingly, so did smaller models. Certainly also seeing increase in accessibility, as we now have GPT-4 level capabilities running locally thanks to breakthrough small models like Llama 3.3, Google's Gemma (released February 2024), and Microsoft's Phi. The combination of small models with techniques like LoRA (which can extract useful capabilities from larger models for specific tasks) changed the game.

A heated battle in tech circles is how fast we will hit our LLM peak—and while I don’t think we’re close, we may be hitting fundamental limits in certain areas of AI progress. OpenAI co-founder and former chief scientist Ilya Sutskever said this month at NeurIPS: "Pre-training as we know it will unquestionably end."

Price is also plummeting—GPT-4's cost has dropped ~90% since launch. Scale economics are kicking in hard. Can any one company maintain a technological moat in this space?

The year most people forgot about world models

Everyone has gotten pretty starry-eyed over AI-generated video models like OpenAI’s Sora and Google’s Veo (having tested both, Veo’s respect for physics and gravity coefficients is superior). Google DeepMind also showcased SIMA in March 2024, a generalist AI agent for 3D virtual environments, followed by Genie 2 in December which can turn single images into playable games. Yeah, just take a photo and make it a video game.

BUT everyone’s obsessing over metaverse demos and video generation, and they seem to have forgotten why AI labs are building these things, and it’s world models. It’s about creating 3D representations of everything around us. Having worked on CoreML at IBM Watson and 3D point clouds at AWS, I can tell you: 3D is incredibly hard and always comes last in AI innovation. Fei-Fei Li’s new venture “World Labs” is taking on this challenge, though current LLMs still struggle with basic spatial understanding (I asked ChatGPT for restaurant recommendations near an office I work from sometimes, and it suggested a place that was a 1h19m walk away, even though there are over 20 restaurants rated 4.5+ within a 10min walk of the place). So next time you see impressive 3D or video demos, think less Super Bowl ads and more modeling and predicting real-world actions and interactions.

The year the labor market began to shift

Most businesses haven't processed what's happening yet, but one of the biggest Silicon Valley conversation topics is that 2024 marked the beginning of an entirely new economic model: paying for outcomes rather than labor hours. When OpenAI's CFO Sarah Friar hinted at this shift a few months ago in a Bloomberg interview about accountants, many brushed it off. But then we saw the evidence: OpenAI's $200/month pro mode offerings, Devin AI launching at $500/month, and ultimately the o3 release demonstrating capabilities that make traditional labor models start to look antiquated.

I expect this newsletter section to get pushback, but the reality is that AI SaaS products are already commanding thousands of dollars per seat because they're being priced on outcomes, not compute time or labor hours. This is a fundamental restructuring of how we value and pay for work. To my finance and MBA friends especially: pay very close attention to this shift. The businesses that understand and adapt to outcome-based pricing will have a significant advantage over those still stuck in the movie Office Space. We're witnessing the early days of the death of traditional labor markets as we know them.

The year of early AGI signals

AGI seems as good a topic as any to end on and one that I ask almost every AI person I speak with.

First, everyone seems to have different definitions of AGI (smarter than all humans? smarter than a standard human? able to produce economic output? able to produce a specific economic output?). But here is a helpful 5-level AGI guide purportedly from OpenAI via Bloomberg sources (1: conversational, 2: reasoning, 3: autonomous, 4: innovating, 5: organizational). Also, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work” and says “the timeline to AGI remains uncertain”. Business Insider and The Information reported, however, that OpenAI and Microsoft signed an agreement that defines AGI as a system that can generate $100B in profit.

Second, nearly everyone in their predictions will flag potential obstacles like geopolitical issues and GPU production capacity. So these are the predictions based on most low friction from outside forces.

Third, most people miss the important piece: TIMELINE. Timeline is what I want to recap here. And specifically, we want to look for: (1) information alpha (who has access to research and early products and who’s just guessing), (2) low to medium incentives for speeding up the timeline, (3) evaluating the change in predictions over time. Everyone always forgets that third one. If you only evaluate predictions at one point in time, you’re missing the real picture. If instead you realize that 95% of the biggest names in AI have all sped up their predictions in the last 2 years, you might feel differently.

Before jumping into it, here are some of my own thoughts, based on my own proprietary knowledge and conversations with some of the top AI founders, builders, and researchers this year as well as VCs.

A big thank you to Kevin Mise on Reddit for doing an insane amount of heavylifting on this. Here is the original post in r/singularity so you can double check his half of the stats.

• Sam Altman - 2 years ago said 2027-2032, now says as early as 2026

• Dario Amodei - a year ago said 2-3 years (2025-2026), now says 2026-2027

• Mustafa Suleyman - a year ago said 3-5 years, now 5-7 (“plausible at some point in the next two to five generations. I don’t want to say I think it’s a high probability that it’s two years away, but I think within the next five to seven years since each generation takes 18 to 24 months now. So, five generations could be up to 10 years away depending on how things go.”)

• Elon Musk - May 2022 said “I’d be surprised if we don’t have AGI by [2029]”, a year ago said less than 3 years to AGI (by 2026), April 2024 said 2025-2026

• Geordie Rose - 6 years ago said 2025-2026

• Cathie Wood - in May 2022 said 6-12 years

• Geoff Hinton - 2020 thought 20-50 years, May 2023 on X said “I now predict 5 to 20 years but without much confidence”

• Mira Murati - said in Dec 2024 that it could take decades but “right now, it feels quite achievable”
  Yoshua Bengio - pre-ChatGPT thought decades to centuries, a year ago think 5 to 20 years with 90% confidence

• Ray Kurzweil - in April 2022 said singularity before 2029, in June 2024 says the same (by 2029)

• Mo Gawdat - in 2021 said by 2029, now says no later than 2027

• Ilya Sutskever - 3 years ago said 10-15 years (2031-2036)

• Fei-Fei Li - in November 2024 declined to answer saying "I don't spend too much time thinking about these words because I think there's so many important things to do"

• Jensen Huang - a year ago said within 5 years (by 2028)

• Eric Schmidt - 3 years ago said 2031-2041

• Yann LeCun - June 2022 said 2032-2037

• Meredith Whittaker - in Jan 2024 called AGI “a marketing term overlaid w quasi religious symbolism”

• Peter Welinder (VP of Product at OpenAI) - a year ago said before 2030 (also gave a definition: “we sort of have a definition at OpenAI around AGI which is like autonomous systems that can do economically valuable work at the level of humans”)

• Demis Hassabis - 2 years ago said “the next decade or two”

• Masayoshi Son (Softbank CEO) - 1 year ago said within 10 years (by 2033)

• Bill Gates - in March 2023 said 10-100 years away

• Adam D’Angelo - July 2024 said 5-15 years (2029-2039)

Many of these folks have sped up their predictions, some have slowed down, but one thing is clear: we should focus less on specific dates and more on understanding why most of these experts keep moving their timelines forward.

I hope this was useful. If you enjoyed this reflection on 2024, please share it with others who might find it valuable. Here's to an exciting 2025—one that will undoubtedly bring even more surprises, breakthroughs, and probably a few more hardware flops.

Make sure to catch my 2025 predictions post in the new year. Until then, back to watching The Crystal Maze with a wet washcloth on my head.

Stay curious,

Allie

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