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This Company Uses Robots and AI to Create ‘Handwritten’ Notes

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Writing thank you notes might be one job plenty of people would be willing to let AI and robots take over.

Turns out, they already are.

The company Handwrytten deploys artificial intelligence to help customers whip up notes and then uses an army of robot scribes, gripping ballpoint pens, to write them.

“The vast, vast, vast majority of the time, you’d never have an idea that it’s written by a machine,” David Wachs, founder and CEO of the Tempe, Arizona, company, told Business Insider.

After all, we’re in a moment in which tech boosters say our digital counterparts will soon free us from work, scrub clean our to-do lists, and wade deeper into our personal lives.

Using technology to recreate the intimacy of a handwritten note also raises questions about authenticity, etiquette, and breaking through the everyday onslaught of emails, DMs, and text messages.

“Everybody’s getting so much electronic communication. What really stands out as old-fashioned communication,” Wachs said.

He founded Handwrytten in 2014 after leaving a text-messaging startup he’d launched a decade earlier. As he was departing that company, he wanted an easier way to send the handwritten goodbye notes he was drafting for employees and key clients because they would carry more weight than a digital message.

Avoiding the ‘uncanny valley’

In order to make sure the letters don’t look too perfect, Wachs said the robots vary letter shapes, line spacing, the left margin, and the strokes that join letters together.


A letter written by one of Handwrytten's robots

Handwrytten tries to make its letters look good, but not too perfect.

Courtesy Handwrytten



“We do all this stuff to try to create the most accurate human writing, without falling into that uncanny valley,” Wachs said.

Using robots that can write in nearly three dozen styles of penmanship — some of which carry alliterative names like Enthusiastic Erin and Slanty Steve — the company sends about 20,000 cards a day to customers or, more often, directly to the recipient.

Most of Handwrytten’s customers are businesses, though about 20% to 30% are individual consumers, Wachs said. Clients include companies hoping to engage with customers, recruiters looking to soften up executive prospects, and nonprofits that want to stay close to donors. Sales grew about 30% in 2024, Wachs said.

In recent years, the company gave users the option of having AI write all or part of the messages.

“Our slogan has always been ‘Your words in pen and ink,’ but half the time now it’s not your words, it’s ChatGPT,” he said.


David Wachs, founder and CEO of Handwrytten stands in the company's factory

David Wachs is the founder and CEO of Handwrytten.

Courtesy Handwrytten



What matters, Wachs said, is that the resulting note looks real to the recipient. He said that many people assume custom digital messages like emails and texts have been written with AI, which, Wachs said, then discounts their effectiveness.

Does it count?

As a tactile throwback, a letter written by a robot is real enough for many Handwrytten customers, Wachs said.

While the intent of a letter meant to look handwritten might be genuine, Lizzie Post, great-great-granddaughter of protocol maven Emily Post and coauthor of the book “Emily Post’s Business Etiquette,” told BI she believes something is lost by using a robot.

Post said a note that someone actually writes by hand is special, not because it shows effort on the part of the sender, but because a person’s penmanship — even if it’s imperfect — is unique to them and to a moment.

“It makes that handwritten version that much more precious and amazing and special,” Post said.

Wachs said that critics have a point when they say part of writing a letter is to demonstrate that someone took the time to do it. Yet, he said, many people are simply too busy.

“Often, the choice is not Handwrytten note or actual handwritten note. The choice is Handwrytten note or nothing,” he said.

Wachs, whose business relies on 55 workers and 185 robots, said that the results are convincing enough to help job seekers, business owners, marketers, and others distinguish themselves.

“My wife will receive notes from her friends that use our service,” Wachs said. “And she’ll be like, ‘Wow, they have beautiful handwriting.”





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I asked ChatGPT to help me pack for my vacation – try this awesome AI prompt that makes planning your travel checklist stress-free

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It’s that time of year again, when those of us in the northern hemisphere pack our sunscreen and get ready to venture to hotter climates in search of some much-needed Vitamin D.

Every year, I book a vacation, and every year I get stressed as the big day gets closer, usually forgetting to pack something essential, like a charger for my Nintendo Switch 2, or dare I say it, my passport.



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Sakana AI: Think LLM dream teams, not single models

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Enterprises may want to start thinking of large language models (LLMs) as ensemble casts that can combine knowledge and reasoning to complete tasks, according to Japanese AI lab Sakana AI.

Sakana AI in a research paper outlined a method called Multi-LLM AB-MCTS (Adaptive Branching Monte Carlo Tree Search) that uses a collection of LLMs to cooperate, perform trial-and-error and leverage strengths to solve complex problems.

In a post, Sakana AI said:

“Frontier AI models like ChatGPT, Gemini, Grok, and DeepSeek are evolving at a breathtaking pace amidst fierce competition. However, no matter how advanced they become, each model retains its own individuality stemming from its unique training data and methods. We see these biases and varied aptitudes not as limitations, but as precious resources for creating collective intelligence. Just as a dream team of diverse human experts tackles complex problems, AIs should also collaborate by bringing their unique strengths to the table.”

Sakana AI said AB-MCTS is a method for inference-time scaling to enable frontier AIs to cooperate and revisit problems and solutions. Sakana AI released the algorithm as an open source framework called TreeQuest, which has a flexible API that allows users to use AB-MCTS for tasks with multiple LLMs and custom scoring.

What’s interesting is that Sakana AI gets out of that zero-sum LLM argument. The companies behind LLM training would like you to think there’s one model to rule them all. And you’d do the same if you were spending so much on training models and wanted to lock in customers for scale and returns.

Sakana AI’s deceptively simple solution can only come from a company that’s not trying to play LLM leapfrog every few minutes. The power of AI is in the ability to maximize the potential of each LLM. Sakana AI said:

“We saw examples where problems that were unsolvable by any single LLM were solved by combining multiple LLMs. This went beyond simply assigning the best LLM to each problem. In (an) example, even though the solution initially generated by o4-mini was incorrect, DeepSeek-R1-0528 and Gemini-2.5-Pro were able to use it as a hint to arrive at the correct solution in the next step. This demonstrates that Multi-LLM AB-MCTS can flexibly combine frontier models to solve previously unsolvable problems, pushing the limits of what is achievable by using LLMs as a collective intelligence.”

A few thoughts:

  • Sakana AI’s research and move to emphasize collective intelligence over on LLM and stack is critical to enterprises that need to create architectures that don’t lock them into one provider.
  • AB-MCTS could play into what agentic AI needs to become to be effective and complement emerging standards such as Model Context Protocol (MCP) and Agent2Agent.
  • If combining multiple models to solve problems becomes frictionless, the costs will plunge. Will you need to pay up for OpenAI when you can leverage LLMs like DeepSeek combined with Gemini and a few others? 
  • Enterprises may want to start thinking about how to build decision engines instead of an overall AI stack. 
  • We could see a scenario where a collective of LLMs achieves superintelligence before any one model or provider. If that scenario plays out, can LLM giants maintain valuations?
  • The value in AI may not be in the infrastructure or foundational models in the long run, but the architecture and approaches.

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‘Superintelligence’ Takes Meta Platforms to Record Highs. Should You Buy META Stock Here?

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Image of Mark Zuckerberg by Rokas Tenys via Shutterstock

Mark Zuckerberg-led Meta Platforms (META) has proved its critics wrong as its shares have recently climbed to new heights, largely thanks to its artificial intelligence-driven strategy. Central to this AI strategy is “Superintelligence,” a long-term vision Zuckerberg has for creating AI systems that exceed human-level intelligence across many domains.

And although Zuckerberg burned shareholders before with the metaverse, his last passion project, Superintelligence feels different. Unlike the metaverse, AI is a megatrend that is already revolutionizing daily life. And Meta, with its arsenal of popular social media platforms like Instagram, WhatsApp, and Facebook, is betting big on AI to drive growth in the coming years. Meta is hiring big to staff this revolution, with Scale AI founder Alexandr Wang tasked with heading the new Superintelligence unit at Meta.

The market seems to be convinced this time, with Meta stock already up about 23% on a YTD basis.

Can Meta sustain this rally? I believe so, and here is why.

www.barchart.com
www.barchart.com

Meta has been doubling down on its AI ambitions, both by making significant financial commitments and by attracting top talent from rival firms. To that end, the company has reportedly extended compensation offers ranging from $50 million to $100 million to lure engineers away from OpenAI and Anthropic. It also made a $14.3 billion investment for a 49% stake in Scale AI, a startup recognized for its industry-leading data labeling capabilities. This investment positions Meta advantageously when it comes to securing high-quality training datasets.

With such resources in place, Mark Zuckerberg is equipping Meta’s AI models to be not just competitive, but potentially market-leading.

Meta’s powerful cash generation is giving it the flexibility to aggressively invest in AI infrastructure. The company has earmarked $60 billion to $72 billion for capital spending in 2025, much of which will be spent on building and upgrading data centers. This rapid pace of investment demonstrates Meta’s conviction that long-term value can be realized by investing in innovation-driven scale.



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