This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How small businesses can leverage AI

From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. Large companies can hire experts to handle these tasks, but small businesses don’t always have that luxury.

That’s where AI comes in. Today’s models can already take on a range of basic administrative work, from organizing notes and summarizing meetings to invoicing, goal-setting, and social media planning. Find out how small-business owners can put AI to work.

—Peter Hall

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox, sign up here.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Anthropic has confidentially filed for IPO ahead of OpenAI
It aims to go public as early as this fall. (CNN)
+ The company did not disclose its target valuation. (Guardian)
+ It’s expected to list shortly after a trillion-dollar IPO by SpaceX. (BBC)
+ Beating OpenAI in the IPO race could have a big impact. (WSJ $)

2 The EU may exclude US cloud giants from critical contracts
The likes of Amazon, Microsoft, and Google could be shut out. (Reuters $)
+ The EU aims to reduce its dependence on US tech. (FT $)
+ Trump supercharged this sovereignty push. (Politico $)

3 Florida has become the first state to sue OpenAI
The lawsuit targets ChatGPT’s alleged child safety risks. (NPR)
 + Florida says OpenAI put profit ahead of safety. (Reuters $)
+ Chatbots are now starting to check user ages. (MIT Technology Review)

4 Hackers stole Instagram accounts just by asking Meta AI for them
They easily broke into a host of celebrity profiles. (404 Media)
+ The exploit shows the risk of offloading support to AI. (TechCrunch)
+ AI is making online crimes easier. (MIT Technology Review)

5 Chinese universities with military ties are seeking Nvidia chips
Two are blacklisted by the US Commerce Department. (Bloomberg $)
+ The Chinese military has sought restricted Nvidia chips for years. (NYT $)
+ US senators have slammed a loophole in chip export rules. (Reuters $)

6 Blue Origin and NASA disagree on a crucial rocket’s next flight
+ Blue Origin says the rocket will fly again this year. (Engadget)+ But NASA is less optimistic. (CNBC)+ The rocket’s failure cast doubt on NASA’s moon plans. (BBC)

7 Moderna has won funding to develop an Ebola mRNA vaccine
The CEPI has pledged over $60 million to the effort. (Ars Technica)
+ To fight an outbreak raging out of control. (MIT Technology Review)

8 China is using AI to predict future political dissent
A company called Geedge Networks is developing the tech. (NYT $)

9 Geoengineering can thicken Arctic ice, but melt results are mixed
Trials show the tech has had a limited impact. (New Scientist $)

10 Top AI labs are expanding research into machine ‘consciousness’
Meta, Anthropic, and DeepMind are increasing their investments. (FT $)
+ A new tool could show how consciousness works. (MIT Technology Review)

Quote of the day

“Sam Altman and ChatGPT have chosen the AI race over the safety and security of our kids. They have chosen profit over public safety, and we’re not going to stand for it in here in Florida.” 

—Florida Attorney General James Uthmeier tells reporters why his state is suing OpenAI, the LA Times reports.

One More Thing

Why the sci-fi dream of cryonics never died

Cryonics is best known for its appearance in sci-fi films like 2001: A Space Odyssey. But its adherents have held on to a dream that advances in medicine will one day allow for resuscitation and additional years on Earth.

Around 500 people are preserved in liquid nitrogen globally, while another 4,000 are on waiting lists. Despite scant evidence that cryonics can work, believers remain optimistic that future science could eventually revive them.

Discover why the hope of human reanimation refuses to die.

—Laurie Clarke

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Hear Dolly Parton reimagined through this spot-on Dire Straits-style cover of “Jolene”.
+ Find out which birds people search for most in this interactive visualization of bird popularity.
+ Explore thousands of Q&As between students and astronauts on the ISS at this interactive site.
+ Paris’s oldest bridge disappeared beneath a giant inflatable cave in this surreal public art installation.

The global health care sector is under increasing strain. 

Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse. The World Health Organization has warned that current shortfalls will increase to 11 million workers by 2030. 

In their urgent hunt for a solution, many health-care providers are now pinning their hopes on agentic AI, with more than two-thirds (68%) having already adopted AI agents into their workforce, according to KPMG. 

The technology is being deployed to automate complex back-office processes, collaborate with medical teams, and even triage patients, all in a bid to reduce the cognitive load on clinicians and improve quality of care for patients as the supply of human health-care workers dwindles.

A different type of digitalization 

Until now, the benefits of digitalization within health care have been limited. 

Many staff have blamed slow or outdated technology for adding to the administrative burden rather than alleviating it. For example, U.S. patient data was migrated to electronic health records (EHRs) in the early 2000s, but this data remains fragmented and reliant on manual inputs. 

New telehealth services and digital care tools, like remote monitors, have had similar shortcomings, says Ashis Barad, MD, chief digital and technology officer at Hospital for Special Surgery (HSS), an academic medical center in New York that focuses on musculoskeletal health. Both technologies have helped improve access to health care by removing geographical barriers, he says, but they’ve failed to replicate the quality of in-person care or win trust from patients. 

Agentic AI is different from these existing technologies, he insists. 

Rather than relying on manual inputs or defaulting to human workers for any case that sits slightly outside a rigid framework, AI agents can handle nuanced, complex scenarios. They can make autonomous decisions, retrieve information from expert clinical sources, and iterate over time, freeing clinicians to focus on higher-level patient care. As Dr. Barad puts it: “Agentic AI takes your workflow and collapses it, augments it, supercharges it, and makes it more performant.” 

At HSS, AI agents have already been deployed in multiple areas. They handle complex backend processes, such as insurance claims that previously took several weeks to complete and involved both HSS staff and a third-party contractor to handle the volume. Now, says Dr. Barad, AI agents complete 1,100 claims per month. They’ve reduced the appeals stage from 45 minutes to five and improved the success rate of those appeals from 65% to 100% in the nine months since implementation. HSS now handles all claims in-house. 

Building on that success, HSS is now deploying AI agents in non-clinical patient-facing settings with an AI scheduling and triage service, as part of a collaboration with enterprise agentic AI developer Ema Unlimited. The service is accessible 24/7 via web, text, or phone. It uses conversational AI to ask patients clarifying questions about their condition and then books appointments with the most appropriate clinician, factoring in location, insurance coverage, and physician availability. “It completes the whole loop,” says Dr. Barad. The AI agent is trained on “all of our context, all of our rules, and all of our knowledge base,” he adds, providing patients with streamlined access to highly specialist knowledge from world-leading surgeons.

Given the high-stakes decisions delegated to AI agents, the triage service has built-in safeguards—sensitive, complex, or uncertain scenarios are escalated to human specialists. Every decision made by the AI agent is auditable and human staff can step in at any point. Patient data is kept secure and the system is trained on all HSS protocols, policies, and care pathways. By keeping humans in the loop, Ema says its technology strikes the balance between efficient automation, patient-first safety, and human-informed decision making. 

As the technology becomes more prolific, it will be incumbent on providers to ensure they have these sorts of guardrails embedded into systems, says Dr. Barad. At HSS all decisions around the technology are filtered through an AI subcommittee that Dr. Barad co-chairs alongside a senior nursing executive. AI agents that may touch on patient care will be scrutinized with far more rigor than, say, backend processes, he explains.

AI agents prompt systems-level change

For example, Dr. Barad has plans to create a dedicated AI lab at the HSS main campus in New York City—a move that aims to democratize access to the technology across the organization. It will be open to all staff looking to understand or build AI agents, he explains, with informative classes and one-on-one training. “We’re getting agentic AI into everybody’s hands,” he says. This echoes research by Deloitte, which found that leading agentic AI adopters in health care were far more likely to have opted for multiagent solutions, redesigning end-to-end workflows rather than sticking to narrow solutions or individual use cases.

The key, it appears, is to integrate AI agents across the entire enterprise, treating them as a general-purpose technology. As Dr. Barad puts it: “It’s wrong to think of agentic AI in use cases… It’s a general-purpose technology, analogous to electricity.”

In practice, this means health-care providers need to set the right foundation to achieve value with agentic AI. This includes creating a unified data strategy, one that integrates fragmented data sources across an organization to create a single, comprehensive source of truth. In health care, data is often split across multiple departments and providers, each with their own legacy IT system.

In systems that rely on fragmented data sources, metrics often lack standardized definitions too. For example, Dr. Barad says that each hospital he’s worked in has had a slightly different definition for “time to start surgery,” a metric commonly used to gauge operating room efficiency. This level of fragmentation impedes AI agents from retrieving information from different sources or applications and assimilating the tacit knowledge that differentiates them from other technologies.

By creating greater interoperability of data at HSS, patient-facing AI agents can draw from a patient’s clinical care history and existing recommendations from their clinician, combine this information with current symptoms, and decide whether a situation requires escalation before notifying the correct specialist and informing the patient. 

Building better outcomes

For Dr. Barad, the potential for AI agents to overhaul health care and alleviate the current pressures on resources, access, and patient care is huge. 

He envisions a future in which 90% of non-clinical health-care tasks could be administered by AI agents, freeing clinicians up for what he calls white-glove work, meaning the most complex, specialized, and sensitive cases.

Most health-care providers seem equally optimistic. According to research by KPMG, 84% of providers are already comfortable handing decision making about specific processes over to AI agents.

“We’re spending so much time on keyboards and computers right now that we’re actually not doing what we should be doing,” says Dr. Barad. “This is going to rehumanize health care.”

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here.

From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to handle these tasks, but small businesses don’t always have this luxury.

That’s where AI comes in. Today’s AI models do a decent job at these tasks. The trick for small businesses is to understand where AI is good enough and where it’s not.

One place where a “good enough” AI can already be quite valuable to small business owners is in providing secretarial skills and handling basic administrative matters. Let’s take a look at how one private tutor is using it to improve his recordkeeping and free up his time.

Case study

Sam Finnegan-Dehn works in fundraising for a charity, but he moonlights as a math and philosophy tutor for university students from his home in London. Through this part-time business, he can leverage his degrees in philosophy and share his love of the subject with clients.

But meeting with students is only a fraction of the work it takes to be a good tutor. He also plans lessons and finds fresh reading materials, creates assignments, sends invoices, and keeps up with new research—all on top of his regular job. Given these demands, Finnegan-Dehn doesn’t have as much time as he’d like to grow his tutoring roster.

So he’s turned to AI for some help in managing the day-to-day aspects of his business. He says AI has taken on a secretarial role across all of his digital notebooks, where he jots down reminders about his clients’ progress and new readings to keep himself up-to-date. He describes using AI as kind of like having a second memory that helps him connect ideas he’s written down in various places.

While he has experimented with different tools like Claude and ChatGPT, he’s now landed on Notion AI because it integrates better with his tutoring notes, which live across his notebook tabs in the Notion app. Finnegan-Dehn doesn’t use AI to create teaching materials, but he does let Notion AI record meetings with his clients (after getting their consent), and then uses its automated summaries to refine his teaching strategy. For example, if he notices from the AI’s summary that it seems like a certain technique was not helping a student, he may change how he approaches the subject next time.

Beyond this, Notion AI also helps him with goal-setting, drafting lesson notes, invoicing, and generating and syncing social media posts. For goal-setting, for example, Finnegan-Dehn says he understands his long-term goals for his business but not always the concrete steps to build to them. He uses AI to help fill in these gaps. He starts by writing down a “North Star” goal—say, to have a certain number of clients by the end of the year. Next, he asks his AI to generate the steps that he needs to take to get there, given the profile he has built up in the app. Then, he can reflect on the results and choose which tasks to tackle first.

The tool

Notion has been a big player in note-taking software for many years. Its AI add-on, released in late 2023, now has tools that enable it to interact with many other online productivity platforms. There’s an email client, calendar integrations, and a newly released agent. And while this level of access has raised privacy concerns, it can also make for a pretty powerful virtual assistant.

Many of the tasks targeted by Notion AI are less creative and more rote: syncing information across documents or searching through old scribbles, for example. This makes the tool especially appealing to small business owners, who have limited bandwidth, particularly for menial work.

Other companies are developing tools targeted at specific industries. For example, Grandma’s Quilt Shop in Yuma, Arizona, uses Rain, which has a software suite tailored to craft companies, to generate inventory descriptions and pricing for its stock of fabric designs. The owners claim this AI tool cuts the time it takes to list items by 60 to 80%.

There are drawbacks, though, as Finnegan-Dehn described some of Notion AI’s idiosyncrasies as “clunky” at times. And the AI add-on for Notion costs $20 per month. As with all new tools, small business owners should carefully assess how the potential gains and headaches measure up against the cost of just doing the job themselves.

User tips

Consider these points when thinking about whether AI might be able to help you run a business, or make any part of your work life just a little bit easier. 

• Look before you leap. Since LLMs feed on the data you input to answer your queries or complete tasks, you want to give them information in a way that’s convenient for you and for the model. For many of these notebook AI services, this means, for example, using their platform for notetaking so you don’t have to input or upload notes later. Because of this, it’s a good idea to weigh your options carefully before committing to an AI-powered ecosystem.
• Work to your strengths. Think about what skills you lack in-house, and see if AI can either help with training or take these tasks on for you. Just be aware: AI hallucinates and makes mistakes, so think about where accuracy is needed and keep humans in charge there.
  
• AI isn’t always the best tool. It’s okay to use something off the shelf when that’s the better choice. It’s going to be safer, for example, to use existing payment processing platforms like Shopify or Square than to vibe-code one using AI.
  
• Consider using local models for any sensitive information. Our reporting has covered the risks that online AI models have in leaking sensitive data, and there have been many reports about how AI companies collect your data when you ask their chatbots questions. Even if your business doesn’t handle personal information, there can still be some things you’d prefer not to share publicly. In these cases, using an open-source model that makes inferences on your prompts locally can be a great option, instead of ChatGPT or Claude or other proprietary models. Thankfully, some LLMs can now be run off of laptops and small desktops. Here’s how to set one up and start using it.

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