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.

Coming soon: our 2026 list of Climate Tech Companies to Watch

With the planet nearing 1.5 °C of warming, climate policies being unraveled, and Big Tech backpedaling on its climate ambitions, it can be tempting to give in to climate doom and defeatism. But despite the headwinds, the world has still made incredible progress. That’s why MIT Technology Review publishes its annual list of Climate Tech Companies to Watch.

Once a year, we compile a list of 10 companies that we believe have done the most, or have the best chance, to make a real dent in emissions or improve public safety and health. We’ve now finalized our 2026 list and will publish it on October 6.

It features companies making strides in energy storage, nuclear power, transportation and other areas despite today’s regressive climate politics. We hope you’ll enjoy the package and perhaps come away feeling a bit more hopeful about our climate’s future. Here’s what to expect.

—James Temple

Subscribe to receive full access to this year’s list, and keep reading The Download to be among the first to see it.

When can we say AI made a scientific discovery?

Last week, Anthropic announced that its new molecular biology lab had made its first discovery: its AI agents had flagged a previously uncatalogued pattern surrounding an enzyme, a pattern “reminiscent” of what led to the gene-editing technology CRISPR. But the claims angered biologists.

Some questioned whether merely finding the pattern amounted to a discovery at all. Another said his team had already discovered the same pattern, raising questions about whether Anthropic’s system had learned from his conversations with Claude.

It’s a reminder that what’s novel for AI may be routine, unsurprising or simply not that consequential to a biologist. And the grand claims could make real progress harder to recognize.

Find out why AI companies may be setting the wrong bar for scientific discovery.

—James O’Donnell

This story is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.

The must-reads

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

1 OpenAI has scrapped a new AI model over safety concerns
The company said GPT-6.1 Astra “didn’t quite meet the bar.” (BBC)
+ It’s also apologised for a “new kind of cyber incident” in Australia. (Guardian)
+ Anthropic’s IPO filing warns that AI could threaten humanity. (FT $)
+ As the safety fears spiral, Trump is selling an AI “golden age.” (WP $)
+ He’s meeting more AI leaders at the White House today. (Reuters $)
+ Could AI really kill us all? (MIT Technology Review)

2 SpaceX’s Starship rocket has reached orbit for the first time
It also deployed new Starlink satellites in the test flight. (AP)
+ But the flight was cut short after an engine failure. (Axios)
+ We’re putting more stuff into space than ever. (MIT Technology Review)

3 The FBI told staff it assumes hackers have stolen data from every employee
An internal memo tells staff to prepare for potential attacks. (Gizmodo)
+ But the hackers say they won’t publish the stolen data. (404 Media)

4 Florida has asked a court to block OpenAI from developing new models
The state wants independent safety guardrails for new AI. (Politico)
+ It also aims to ban ChatGPT from acting like a person. (Verge)
+ While Rep. Khanna has proposed banning self-improving AI. (CNBC)

5 Meta’s AI agent invited a stranger to a user’s home without permission
Muse shared the address while negotiating a Marketplace sale. (Guardian)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

6 Old hearts appear to get younger after transplantation
The finding could expand the pool of available donor organs. (Nature)
+ But young organs may not be a fountain of youth. (MIT Technology Review)

7 Trump has finalized a rule to make cars less fuel-efficient
The new rules sharply weaken Biden-era fuel economy standards. (Verge)

8 Mathematicians have cracked a 55-year-old problem using randomness
The solution completes a proof proposed in 1971. (Quanta)

9 Space lasers are about to beam power between two spacecraft
Star Catcher will test the technology in orbit this week. (Wired $)

10 A tiny moon of Saturn could host alien life and be detected from space
Scientists found evidence that its ocean could support microbial life. (404 Media)

Quote of the day

“We may sound like children whose feelings are hurt, sure, but in our game, reputation is all that matters.”

—The ShinyHunters hacking group tells The New York Times that they targeted the FBI to retaliate against an advisory warning about their activities.

One more thing

A plan to make drugs in orbit is going commercial

A startup called Varda Space Industries is betting that the future of pharmaceuticals lies in orbit. The company has signed a deal with United Therapeutics to test whether drugs crystallize differently in microgravity, potentially creating improved versions with new properties.

The idea sounds futuristic, but falling launch costs and reusable rockets are making space-based manufacturing seem increasingly plausible. Varda says the partnership could mark an important step toward building products in orbit for use back on Earth.

Discover how space could become the next frontier for drug development.

—Antonio Regalado

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.)

+ A professor has turned her childhood Double Dutch idea into a real machine.
+ These 11 beautiful underwater wildlife photos capture the wonder of life beneath the oceans.
+ See Earth from a rocket’s-eye view as this camera rides all the way from launch to a precision landing.
+ A re-released 2000 BBC exhibition offers a fascinating glimpse at how people imagined the internet’s future.

Read more

Earlier this month, the UN announced the planet will tip past 1.5 ˚C of warming, “likely within the next few years,” squashing any lingering hope that nations would cut emissions fast enough to achieve the loftiest goal of the Paris climate agreement.

In the US, the world’s second-largest-emitting country, its leader continues to deny climate realities and unravel climate policies. Big Tech is backpedaling on its climate ambitions, as companies race to build massive AI data centers.

And all this comes amid yet another season of shattered heat records, raging wildfires, and deadly floods.

It can be tempting to give in to climate doom and defeatism. But despite the headwinds, the world has still made incredible progress. Companies are developing and deploying renewables, batteries, and EVs; inventing cleaner means of producing industrial goods; and devising ways to keep cities and people safe even in the face of more extreme weather conditions.

Those advances will only accelerate as costs decline and people demand greater climate action, as well as all the economic and health benefits that come with it.

Recognizing our ability to make such progress is why MIT Technology Review began publishing our annual list of Climate Tech Companies to Watch three years ago.

Once a year the editors and writers use their deep industry knowledge and academic sources to compile a long list of companies around the world that are transforming the way we generate energy, produce goods, move people and things, or mitigate rising climate dangers.

We then conduct several rounds of spirited newsroom debate to winnow that list down to 10 that we believe have done the most, or have the best chance, to make a real dent in emissions or improve public safety and health.

We’re happy to say we’ve finalized that list for 2026, and will publish it on October 6. You can sign up for The Download newsletter to be among the first to see it, or subscribe to receive full access to this year’s list. It features a mix of companies making strides in energy storage, nuclear power, transportation, and other areas, including ones that have figured out how to continue to make progress amid the regressive climate politics of the moment.

We hope you’ll enjoy the package and perhaps come away feeling a bit more hopeful that we can move fast enough to avoid the gravest dangers of climate change, and build a safer, healthier, and more sustainable world.

Read more

When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? Not necessarily. But that is often where the conversation goes.

As AI moves from experimentation to production, model choice is only part of the equation. When demand becomes steady and business-critical, a consumption-only approach can turn AI spending into a variable monthly line item that is difficult to forecast as usage, workloads, and model requirements change.

At that point, the question is no longer simply which model to consume, or which provider offers the lowest token price: It is how to run AI economically, predictably, and at sustained scale.

AI is moving from isolated pilots into production portfolios: assistants, retrieval-and-knowledge systems, and agentic applications. Customer-service, IT, research, and business-process agents can execute multi-step workflows across enterprise systems, creating recurring demand across models, data, and tools.

This is already starting to happen. Deloitte’s 2026 State of AI in the Enterprise reflects what many leaders are seeing: worker access to AI rose 5% in 2025, and the share of companies with at least 40% of their AI projects in production is expected to double within six months.

When AI becomes a portfolio of always-on workloads, not a collection of experiments, the economics change. Consumption pricing gives teams flexibility and limits commitment. But when usage becomes steady, predictable, and large enough to keep capacity productive, leaders need to ask a different question: Does it still make economic sense to buy AI one request at a time, or is it time to invest in capacity they can optimize and control?

This is not an abstract cloud-versus-on-premises debate. It is a workload-by-workload business decision. Over the next 12 to 18 months, how much AI demand can the company reasonably expect? How consistently will that capacity be used? When multiple workloads share infrastructure, the enterprise can spread fixed costs across more productive use—improving the economics of ownership.

The question is how much you run

Ownership is not automatically the lower-cost answer. It only makes sense when an enterprise can keep capacity productive.

Every organization has a crossover point, the level of sustained use at which owning capacity can become more economical than buying it one request at a time. There is no universal number. It depends on the models being used, the balance of input and output tokens, performance requirements, system design, energy costs, and the operating model required to support it.

A retrieval-heavy knowledge system can have a very different cost profile from a simple assistant because it may process far more context for every interaction. Agentic workflows can be different again: a single business task may involve repeated reasoning, retrieval, model calls, and tool use. That is why generic cost benchmarks are not enough. Enterprises need to model their actual workloads, understand expected demand, and size capacity accordingly.

At the right utilization level, the benefit is not only lower effective cost, but also greater predictability: the ability to manage AI capacity as a strategic infrastructure investment rather than watch a monthly spend line fluctuate with model use and workload demand.

Ownership only works when it is put to work

The capital decision is only half the equation. Even when the economics support ownership, capacity creates value only when the business gets workloads into production quickly and keeps them running.

That takes more than installing infrastructure. It takes an operating model that connects the technology to adoption and business outcomes: bringing users and workloads on board, governing how AI is used, reviewing utilization, and continually identifying the next high-value use case.

The goal is to create value early, then build on it. That means measuring use, identifying underutilized capacity, and bringing additional high-value workloads onto the platform over time. Without that discipline, the business may never realize the economic value that justified the investment. With it, AI capacity becomes a productive asset the business can optimize, expand, and use to create measurable value.

Three questions to ask

Before committing capital, leaders should ask three questions:

  1. Is demand becoming steady, predictable, and large enough to justify dedicated capacity?
  2. At what level of usage does ownership make economic sense?
  3. Can we keep that capacity productive through adoption, governance, and continued use-case expansion?

Make the shift deliberately

As AI moves into production, the organizations that create the most value will look beyond token prices and the latest model. They will know when recurring demand calls for a different economic model—and they will have the operating discipline to make that capacity productive.

That is when AI stops being an expense and becomes an asset.

This content was produced by HPE. It was not written by MIT Technology Review’s editorial staff.

Read more

How to Build Your Own AI Apps (No Coding Required) by Social Media Examiner

Are you spending too much every month on software subscriptions that don’t actually fit the way your business works? Wondering how to build custom AI-powered apps and tools, even without any coding or technical experience? In this article, you’ll discover how to brainstorm, build, and launch custom AI apps that replace expensive software, even with […]

The post How to Build Your Own AI Apps (No Coding Required) appeared first on Social Media Examiner.

Read more

Here’s a recap of the most important insights, trends, and updates you need to know about today! Catch up in minutes and go into the rest of the week prepared. In today’s edition: YouTube packaging formula Instagram reels content creation 🗞️ Industry news from LinkedIn, Meta, YouTube, and more YouTube Packaging Formula to Improve CTR Publishing […]

The post YouTube CTR, Instagram Reels Structure, and Industry News appeared first on Social Media Examiner.

Read more
1 … 31 32 33 34 35 … 3,393