The Ai Pin is officially dead. Parts of Humane have been sold to HP, and the Ai Pin will cease to function in a week. Cause of death? An outdated and undercooked bet against the iPhone made by former Apple engineers that Humane tried to disguise as artificial intelligence hardware.

Bloomberg reports that HP has agreed to acquire assets from Humane for $116 million. Humane’s sole product, Ai Pin, won’t become an HP product. Instead, HP will put the parts of Humane that it is acquiring into its AI portfolio.

Despite the product’s name, there wasn’t a whole lot of AI in the Ai Pin.

The hardware was basically an Apple Watch-sized version of the iPhone. Only it had no display aside from a low fidelity projector that relied on an unintuitive hand gesture system to control.

It could summarize your text messages, but not your text messages. Just the ones that people sent to your special Ai Pin phone number.

The actual AI aspects mostly relied on early versions of ChatGPT. Apple isn’t doing enough with Siri to make its voice assistant competitive with ChatGPT, but at least the iPhone is where the best features of ChatGPT exist — and they’re evolving daily.

Before it was called Ai Pin, the product was being developed during a global conversation around smartphone addiction and too much screen time in our everyday lives. Apple even released a feature to ease concern called Screen Time that allegedly tracks device usage.

Then the pandemic made remote work the norm, and screens became the only way we stayed connected.

Humane still positioned its product as the solution for helping you reconnect with the real world and use your phone less. The hardware was managed through a website because Humane knew that requiring a smartphone app was antithetical to the device’s purpose.

Personally, I never got to try the Ai Pin. The concept is cool, but it’s a smartphone accessory that extends the experience — like AirPods or the Apple Watch — not a standalone device worth hundreds of dollars for the hardware and another monthly fee for the product to do anything.

9to5Mac’s Take

Humane co-founders and former high level Apple executives Imran Chaudhri and Bethany Bongiorno join HP as part of the deal, helping the PC maker integrate AI into its products.

Much less glamorous than what the duo were previously selling, but marketing the miniature smartphone without a display or much battery as an artificial intelligence product seems to have been a bet that ultimately paid off.

$116 million seems generous for a company that sold customers $500 and up hardware with a $24/month subscription that ceases to function 10 months and two weeks after release.

Rest in HP, Humane.

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On Jan. 20, Chinese artificial intelligence start-up DeepSeek released its first-generation reasoning models. In the release, the company made some astonishing claims.
First, DeepSeek said its DeepSeek-R1 model achieves performance comparable with OpenAI-o1, widely considered the best-performing model available across most domains. Considering the Chinese company is working with significantly worse hardware than OpenAI and other American companies, that’s certainly remarkable.
Even more impressive is that the company claims to have achieved these results at an incredibly low cost. R1 was built on DeepSeek’s V3 large language model, released in December. The company estimates the compute cost for training V3 came to just $5.6 million. To put that in perspective, OpenAI’s GPT-4 cost $100 million to train.
DeepSeek achieved similar performance at a fraction of the cost. And since it’s completely open source, allowing anyone to copy its techniques, it will have lasting implications on the entire industry.
Two companies, in particular, are in a great position to benefit from DeepSeek’s innovations.

Image source: Getty Images.

The long-term impact of DeepSeek-V3 and R1
DeepSeek focused on maximizing the efficiency of its limited hardware capabilities. Because of AI chip export restrictions, Nvidia isn’t able to sell its most powerful H100 GPUs in China. Instead, it sells H800 GPUs, which are specifically designed to comply with U.S. regulations. The H800 reduces the chip-to-chip transfer rate, reducing the speed at which it’s possible to train large AI models.
Due to such limitations, DeepSeek developed processes that enable it to reduce the amount of data it needs to transfer throughout the system at any given time. For example, its “mixture of experts,” or DeepSeekMoE, introduced last year, made it so it only had to activate part of the model to respond to queries.
DeepSeek isn’t the only company using this method, but its novel approach also made its training more efficient. Most methods involve more training overhead in order to reduce the cost of inference later on.
The start-up also developed methods to reduce the amount of memory required for AI inference by compressing important data before storing and transmitting it. It brought new approaches to load balancing, which is how processes are distributed across its network of GPUs.
The result of these and other breakthroughs isn’t just an AI model that’s faster to train and costs less. The longer-term impact of DeepSeek’s innovations are that it’s cheaper to run, and it can run on less-capable hardware. In other words, AI inference just got a lot more accessible.
In a world with the potential to run AI systems on hardware that fits in your pocket and for a tiny fraction of a penny, there are two very big winners: Apple (AAPL -0.67%) and Meta Platforms (META 0.32%). Here’s why.

Making on-device AI a reality
When Apple started developing its artificial intelligence features for the iPhone and other devices, it put data privacy at the forefront of its efforts. Apple Intelligence is designed to run as much as possible on the iPhone. When it has to make a call to the cloud, it takes every step it can to encrypt user data in the process.
There’s a reason the new AI features Apple introduced last year are only available on iPhones released in the last 15 months. Since Apple is trying to keep everything on the device, it needs enough processing power and memory to run its AI. The newest iPhone chip, the A18 Pro, boosted the memory bandwidth to support faster AI processing.
Apple could adopt many of DeepSeek’s methods to make the iPhone more capable of handling AI inference. That opens the door for features like a more conversational and context-aware Siri, faster translation with no internet connection needed, smart camera features, and better productivity tools. More advanced AI features could boost Apple’s iPhone sales and services revenue.
Apple stock currently trades for a relatively high multiple of 32.5 times forward earnings. But with its massive cash flow, which it uses to buy back shares, and improving profitability from services revenue, it can justify that high multiple, especially considering the consistency Apple has exhibited in recent years. The potential boost from major improvements to on-device AI could be a catalyst for growth over the next few years.

Scaling AI to 3 billion people
Meta’s AI spending is growing fast as it works to scale its capabilities and expand AI features to more parts of its business. Capital expenditures grew about 40% in 2024, and management said it expects a 60% increase in 2025. Those AI investments have paid off well for Meta, resulting in stronger engagement, better advertising tools, and new features like Meta AI, which have the potential to become meaningful sources of revenue down the road.
One important decision Meta made when it came to AI was to open-source its AI model Llama. One of the impetuses behind that decision was to help make the model more efficient. In fact, DeepSeek used Llama as the foundation for developing R1, so this is exactly what Meta hoped for.
Reducing the cost of AI inference could unlock huge profits for Meta. It’s a problem Meta’s been working on for a long time. “A lot of the stuff is expensive, right, to kind of generate an image or a video or a chat interaction,” Zuckerberg said during an earnings call in Feb. 2023. “So one of the big interesting challenges here also is going to be how do we scale this and make this work more efficient so that way, we can bring it to a much larger user base.”
DeepSeek’s answering that challenge and giving Meta the tools it needs to scale AI to its 3 billion users. While Meta might not slow down its spending on AI anytime soon, it’s now capable of making a lot more money off the spending it’s committed to.
Meta stock has zoomed higher on the DeepSeek news, reaching a new all-time high. Still, shares trade for 26.8 times forward earnings estimates as of this writing. Meta’s also a cash cow, using excess free cash flow to buy back shares and support strong earnings-per-share growth. If it can make AI more profitable, it stands to see earnings climb substantially over the next few years, making it well worth the price.

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An expanding ecosystemStartups are quickly identifying how to tailor voice technology to a host of enterprise and consumer needs. And with agentic AI a hot topic for CEOs, its overlap with voice technology could hold high potential.PlayAI, a startup that is developing an AI platform for text-to-speech models and AI voice agents, raised $21 million in seed funding in November.”We’ve seen a massive increase in interest in building voice agents, which a human can speak to just like it’s another human,” said PlayAI cofounder Hammad Syed. “Voice AI is going mainstream and will be a key interface in how people interact with technology. Investors definitely realize this opportunity,” he added.VCs scouring the ecosystem to make their next big bet are now looking at voice AI as a technology stack, said Steve Jang, founder and managing partner at Kindred Ventures, which also backed PlayAI. The firm’s investment thesis is to back startups “with multiple layers with many use cases in consumer, enterprise, and creativity.””First, there are specialized and foundational models. Second, there are infrastructure services and tools, which offer access and integration with these models. And then, perhaps most importantly, there is the vast vertical application space,” he told BI.The sector is also attractive to investors because voice is an easy category to cash in on. “You can price it by the outcome, so it’s pretty easy to monetize,” said Jonathan Userovici, general partner at VC firm Headline. “That’s why you have so much revenue traction — it’s pretty easy to get a return on investment, especially if you’re replacing a human doing that labor.”Consumer appetite for voice AI has also skyrocketed. With more users preferring to take in information through audio formats like podcasts, Sukumar highlighted the growing consumer demand for voice control and audio interfaces. He built PersuAIsion, a voice AI platform that allows users to practice real-world conversations — from job interviews to first dates — because he saw the scope for voice to cater to such consumer needs.”If OpenAI can capture the consumer voice agent, they’ll be what Siri was supposed to be,” he said. “I think there’s going to be a lot more interfacing with personal devices, and there’s just going to be better e-commerce consumer experience on that front.”Frontier labs are catching upDespite its growing popularity, voice AI doesn’t seem to have one established juggernaut just yet. Part of the reason could be that frontier labs have largely stayed away from the space, possibly due to a concern that a misuse of voice generation capabilities could result in a potential backlash, according to Air Street Capital’s 2024 State of AI report.”Despite scraping huge quantities of audio and video data, frontier labs have been slow to release text-to-speech products,” said Nathan Benaich, founder and general partner of Air Street Capital. He pointed to OpenAI’s Advanced Voice Mode, which was repeatedly postponed, and Google’s NotebookLM, which “is relatively locked down.”AI experts had sounded the alarm about the possible rise of deepfakes in a year marked by global elections — but that didn’t end up being the case.”In all likelihood, labs were keen to avoid being dragged into the panics about deepfakes that often accompany major elections. I think it’s inevitable they will play more in this space, just because the potential commercial opportunity is so large,” Benaich said.Big Tech may be slowly pivoting toward the trend. Amazon’s plans to ramp up its voice assistant offerings through Alexa were delayed until 2025, and Apple recently bulked up its Siri feature by adding ChatGPT capabilities.Still, Benaich noted that it won’t be an easy task for any one company to take the crown. “Displacing companies like ElevenLabs, who already enjoy widespread adoption and have been optimizing their tools for enterprise users for years now, could prove challenging,” he said.