SpaceX is a space launch juggernaut. In just two decades, the company has managed to edge out former aerospace heavyweights Boeing, Lockheed, and Northrop Grumman to gain near-monopoly status over rocket launches in the US; it accounted for 87% of the country’s orbital launches in 2024, according to an analysis by SpaceNews. Since the mid-2010s, the company has dominated NASA’s launch contracts and become a major Pentagon contractor. It is now also the go-to launch provider for commercial customers, having lofted numerous satellites and five private crewed spaceflights, with more to come.  Other space companies have been scrambling to compete for years, but developing a reliable rocket takes slow, steady work and big budgets. Now at least some of them are catching up.  A host of companies have readied rockets that are comparable to SpaceX’s main launch vehicles. The list includes Rocket Lab, which aims to take on SpaceX’s workhorse Falcon 9 with its Neutron rocket and could have its first launch in late 2025, and Blue Origin, owned by Jeff Bezos, which recently completed the first mission of a rocket it hopes will compete against SpaceX’s Starship.  Some of these competitors are just starting to get rockets off the ground. And the companies could also face unusual headwinds, given that SpaceX’s Elon Musk has an especially close relationship with the Trump administration and has allies at federal regulatory agencies, including those that provide oversight of the industry.
But if all goes well, the SpaceX challengers can help improve access to space and prevent bottlenecks if one company experiences a setback. “More players in the market is good for competition,” says Chris Combs, an aerospace engineer at the University of Texas at San Antonio. “I think for the foreseeable future it will still be hard to compete with SpaceX on price.” But, he says, the competitors could push SpaceX itself to become better and provide those seeking access to space with a wider array of options.. A big lift There are a few reasons why SpaceX was able to cement its position in the space industry. When it began in the 2000s, it had three consecutive rocket failures and seemed poised to fold. But it barreled through with Musk’s financial support, and later with a series of NASA and defense contracts. It has been a primary beneficiary of NASA’s commercial space program, developed in the 2010s with the intention of propping up the industry. 
“They got government contracts from the very beginning,” says Victoria Samson, a space policy expert at the Secure World Foundation in Broomfield, Colorado. “I wouldn’t say it’s a handout, but SpaceX would not exist without a huge influx of repeated government contracts. To this day, they’re still dependent on government customers, though they have commercial customers too.” SpaceX has also effectively achieved a high degree of vertical integration, Samson points out: It owns almost all parts of its supply chain, designing, building, and testing all its major hardware components in-house, with a minimal use of suppliers. That gives it not just control over its hardware but considerably lower costs, and the price tag is the top consideration for launch contracts.  The company was also open to taking risks other industry stalwarts were not. “I think for a very long time the industry looked at spaceflight as something that had to be very precise and perfect, and not a lot of room for tinkering,” says Combs. “SpaceX really was willing to take some risks and accept failure in ways that others haven’t been. That’s easier to do when you’re backed by a billionaire.”  What’s finally enabled international and US-based competitors to emerge has been a growing customer base looking for launch services, along with some investors’ deep pockets.  Some of these companies are taking aim at SpaceX’s Falcon 9, which can lift as much as 20,000 kilograms into orbit and is used for sending multiple satellites or the crewed Dragon into space. “There is a practical monopoly in the medium-lift launch market right now, with really only one operational vehicle,” says Murielle Baker, a spokesperson for Rocket Lab, a US-New Zealand company. Rocket Lab plans to take on the Falcon 9 with its Neutron rocket, which is expected to have its inaugural flight later this year from NASA’s Wallops Flight Facility in Virginia. The effort is building on the success of the company’s smaller Electron rocket, and Neutron’s first stage is intended to be reusable after it parachutes down to the ocean.  Another challenger is Texas-based Firefly, whose Alpha rocket can be launched from multiple spaceports so that it can reach different orbits. Firefly has already secured NASA and Space Force contracts, with more launches coming this year (and on March 2 it also became the second private company to successfully land a spacecraft on the moon). Next year, Relativity Space aims to loft its first Terran R rocket, which is partially built from 3D-printed components. And the Bill Gates–backed Stoke Space aims to launch its reusable Nova rocket in late 2025 or, more likely, next year. Competitors are also rising for SpaceX’s Falcon Heavy, holding out the prospect of more options for sending massive payloads to higher orbits and deep space. Furthest along is the Vulcan Centaur rocket, a creation of United Launch Alliance, a joint venture between Boeing and Lockheed Martin. It’s expected to have its third and fourth launches in the coming months, delivering Space Force satellites to orbit. Powered by engines from Blue Origin, the Vulcan Centaur is slightly wider and shorter than the Falcon rockets. It currently isn’t reusable, but it’s less expensive than its predecessors, ULA’s Atlas V and Delta IV, which are being phased out. 

Mark Peller, the company’s senior vice president on Vulcan development and advanced programs, says the new rocket comes with multiple advantages. “One is overall value, in terms of dollars per pound to orbit and what we can provide to our customers,” he says, “and the second is versatility: Vulcan was designed to go to a range of orbits.” He says more than 80 missions are already lined up.  Vulcan’s fifth flight, slated for no earlier than May, will launch the long-awaited Sierra Space Dream Chaser, a spaceplane that can carry cargo (and possibly crew) to the International Space Station. ULA also has upcoming Vulcan launches planned for Amazon’s Kuiper satellite constellation, a potential Starlink rival. Meanwhile, though it took a few years, Blue Origin now has a truly orbital heavy-lift spacecraft: In January, it celebrated the inaugural launch of its towering New Glenn, a rocket that’s only a bit shorter than NASA’s Space Launch System and SpaceX’s Starship. Future flights could launch national security payloads.  Competition is emerging abroad as well. After repeated delays, Europe’s heavy-lift Ariane 6, from Airbus subsidiary Arianespace, had its inaugural flight last year, ending the European Space Agency’s temporary dependence on SpaceX. A range of other companies are trying to expand European launch capacity, with assistance from ESA. China is moving quickly on its own launch organizations too. “They had no less than seven ‘commercial’ space launch companies that were all racing to develop an effective system that could deliver a payload into orbit,” Kari Bingen, director of the Aerospace Security Project at the Center for Strategic and International Studies, says of China’s efforts. “They are moving fast and they have capital behind them, and they will absolutely be a competitor on the global market once they’re successful and probably undercut what US and European launch companies are doing.” The up-and-coming Chinese launchers include Space Pioneer’s reusable Tianlong-3 rocket and Cosmoleap’s Yueqian rocket. The latter is to feature a “chopstick clamp” recovery of the first stage, where it’s grabbed by the launch tower’s mechanical arms, similar to the concept SpaceX is testing for its Starship. Glitches and government Before SpaceX’s rivals can really compete, they need to work out the kinks, demonstrate the reliability of their new spacecraft, and show that they can deliver low-cost launch services to customers.  The process is not without its challenges. Boeing’s Starliner delivered astronauts to the ISS on its first crewed flight in June 2024, but after thruster malfunctions, they were left stranded at the orbital outpost for nine months. While New Glenn reached orbit as planned, its first stage didn’t land successfully and its upper stage was left in orbit.  SpaceX itself has had some recent struggles. The Federal Aviation Administration grounded the Falcon 9 more than once following malfunctions in the second half of 2024. The company still shattered records last year, though, with more than 130 Falcon 9 launches. It has continued with that record pace this year, despite additional Falcon 9 delays and more glitches with its booster and upper stage. SpaceX also conducted its eighth Starship test flight in March, just two months after the previous one, but both failed minutes after liftoff, raining debris down from the sky.
Any company must deal with financial challenges as well as engineering ones. Boeing is reportedly considering selling parts of its space business, following Starliner’s malfunctions and problems with its 737 Max aircraft. And Virgin Orbit, the launch company that spun off from Virgin Galactic, shuttered in 2023. Another issue facing would-be commercial competitors to SpaceX in the US is the complex and uncertain political environment. Musk does not manage day-to-day operations of the company. But he has close involvement with DOGE, a Trump administration initiative that has been exerting influence on the workforces and budgets of NASA, the Defense Department, and regulators relevant to the space industry. 
Jared Isaacman, a billionaire who bankrolled the groundbreaking 2021 commercial mission Inspiration4, returned to orbit, again via a SpaceX craft, on Polaris Dawn last September. Now he may become Trump’s NASA chief, a position that could give him the power to nudge NASA toward awarding new lucrative contracts to SpaceX. In February it was reported that SpaceX’s Starlink might land a multibillion-dollar FAA contract previously awarded to Verizon.  It is also possible that SpaceX could strengthen its position with respect to the regulatory scrutiny it has faced for environmental and safety issues at its production and launch sites on the coasts of Texas and Florida, as well as scrutiny of its rocket crashes and the resulting space debris. Oversight from the FAA, the Federal Communications Commission, and the Environmental Protection Agency may be weak. Conflicts of interest have already emerged at the FAA, and the Trump administration has also attempted to incapacitate the National Labor Relations Board. SpaceX had previously tried to block the board from acting after nine workers accused the company of unfair labor practices. SpaceX did not respond to MIT Technology Review’s requests for comment for this story. “I think there’s going to be a lot of emphasis to relieve a lot of the regulations, in terms of environmental impact studies, and things like that,” Samson says. “I thought there’d be a separation between [Musk’s] interests, but now, it’s hard to say where he stops and the US government begins.” Regardless of the politics, the commercial competition will surely heat up throughout 2025. But SpaceX has a considerable head start, Bingen argues: “It’s going to take a lot for these companies to effectively compete and potentially dislodge SpaceX, given the dominant position that [it has] had.” Ramin Skibba is an astrophysicist turned science writer and freelance journalist, based in the Bay Are

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Every AI model release inevitably includes charts touting how it outperformed its competitors in this benchmark test or that evaluation matrix.  However, these benchmarks often test for general capabilities. For organizations that want to use models and large language model-based agents, it’s harder to evaluate how well the agent or the model actually understands their specific needs.  Model repository Hugging Face launched Yourbench, an open-source tool where developers and enterprises can create their own benchmarks to test model performance against their internal data.  Sumuk Shashidhar, part of the evaluations research team at Hugging Face, announced Yourbench on X. The feature offers “custom benchmarking and synthetic data generation from ANY of your documents. It’s a big step towards improving how model evaluations work.” He added that Hugging Face knows “that for many use cases what really matters is how well a model performs your specific task. Yourbench lets you evaluate models on what matters to you.” Creating custom evaluations Hugging Face said in a paper that Yourbench works by replicating subsets of the Massive Multitask Language Understanding (MMLU) benchmark “using minimal source text, achieving this for under $15 in total inference cost while perfectly preserving the relative model performance rankings.”  Organizations need to pre-process their documents before Yourbench can work. This involves three stages: Document Ingestion to “normalize” file formats. Semantic Chunking to break down the documents to meet context window limits and focus the model’s attention. Document Summarization Next comes the question-and-answer generation process, which creates questions from information on the documents. This is where the user brings in their chosen LLM to see which one best answers the questions.  Hugging Face tested Yourbench with DeepSeek V3 and R1 models, Alibaba’s

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The sleeping giant has awoken! For a while, it seemed like Amazon was playing catchup in the race to offer its users — particularly the millions of developers building atop Amazon Web Services (AWS)’s cloud infrastructure — compelling first-party AI models and tools. But in late 2024, it debuted its own internal foundation model family, Amazon Nova, with text, image and even video generation capabilities, and last month saw a new Amazon Alexa voice assistant powered in part by Anthropic’s Claude family of models. Then, on Monday, the e-commerce and cloud giant’s artificial general intelligence division Amazon AGI has announced the release of Amazon Nova Act, an experimental developer kit for building AI agents that can navigate the web and complete tasks autonomously, powered by a custom, proprietary version of Amazon’s Nova large language model (LLM). Oh, and the standard developer kit (SDK) is open source under a permissive Apache 2.0 license, though the SDK is designed to work only with Amazon’s in-house custom Nova model, not any third-party ones. The goal is to enable third-party developers to build AI agents capable of reliably performing tasks within web browsers. But how does Amazon’s Nova Act stack up to other agent building platforms out there on the market, such as Microsoft’s AutoGen, Salesforce’s Agentforce, and of course, OpenAI’s recently released open source Agents SDK? A different, more thoughtful approach to AI agents Since the public rise of large language models (LLMs), most “agent” systems have been limited to responding in natural language or providing information by querying knowledge bases. Nova Act is part of the larger industry shift toward action-based agents—systems that can complete actual tasks across digital environments on behalf of

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Researchers from the Soochow University of China have introduced Chain-of-Tools (CoTools), a novel framework designed to enhance how large language models (LLMs) use external tools. CoTools aims to provide a more efficient and flexible approach compared to existing methods. This will allow LLMs to leverage vast toolsets directly within their reasoning process, including ones they haven’t explicitly been trained on.  For enterprises looking to build sophisticated AI agents, this capability could unlock more powerful and adaptable applications without the typical drawbacks of current tool integration techniques. While modern LLMs excel at text generation, understanding and even complex reasoning, they need to interact with external resources and tools such as databases or applications for many tasks. Equipping LLMs with external tools—essentially APIs or functions they can call—is crucial for extending their capabilities into practical, real-world applications. However, current methods for enabling tool use face significant trade-offs. One common approach involves fine-tuning the LLM on examples of tool usage. While this can make the model proficient at calling the specific tools seen during training, it often restricts the model to only those tools. Furthermore, the fine-tuning process itself can sometimes negatively impact the LLM’s general reasoning abilities, such as Chain-of-Thought (CoT), potentially diminishing the core strengths of the foundation model. The alternative approach relies on in-context learning (ICL), where the LLM is provided with descriptions of available tools and examples of how to use them directly within the prompt. This method offers flexibility, allowing the model to potentially use tools it hasn’t seen before. However, constructing these complex prompts can be cumbersome, and the model’s efficiency decreases significantly as the number of available tools grows, making it less practical for scenarios with large,

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More American Express is a giant multinational company with roughly 80,000 employees, so as you can imagine, something’s always coming up with IT — whether it be a worker struggling with WiFi access or dealing with a laptop on the fritz.  But as anyone knows firsthand, interacting with IT—particularly chatbots—can be a frustrating experience. Automated tools can offer vague, non-specific responses or walls of links that employees have to click through until they get to the one that actually solves their problem—that is, if they don’t give up out of frustration and click “get me to a human” first.  To upend this worn-out scenario, Amex has infused generative AI into its internal IT support chatbot. The chatbot now interacts more intuitively, adapts to feedback and walks users through problems step-by-step.  As a result, Amex has significantly decreased the number of employee IT tickets that need to be escalated to a live engineer. AI is increasingly able to resolve problems on its own.  “It’s giving people the answers, as opposed to a list of links,” Hilary Packer, Amex EVP and CTO, told VentureBeat. “Productivity is improving because we’re getting back to work quickly.” Validation and accuracy the ‘holy grail’  The IT chatbot is just one of Amex’s many AI successes. The company has no shortage of opportunities: In fact, a dedicated council initially identified 500 potential use cases across the business, whittling that down to 70 now in various stages of implementation.  “From the beginning, we’ve wanted to make it easy for our teams to build gen AI solutions and to be compliant,” Packer explained.  That is delivered through a core enablement layer, which provides “common recipes” or starter code that engineers can

Provided byThoughtworks Rumors of the ongoing death of software development — that it’s being slain by AI — are greatly exaggerated. In reality, software development is at a fork in the road: embracing the (currently) far-off notion of fully automated software development or acknowledging the work of a software developer is much more than just writing lines of code. The decision the industry makes could have significant long-term consequences. Increasing complacency around AI-generated code and a shift to what has been termed “vibe coding” — where code is generated through natural language prompts until the results seem to work — will lead to code that’s more error-strewn, more expensive to run and harder to change in the future. And, if the devaluation of software development skills continues, we may even lack a workforce with the skills and knowledge to fix things down the line.  This means software developers are going to become more important to how the world builds and maintains software. Yes, there are many ways their practices will evolve thanks to AI coding assistance, but in a world of proliferating machine-generated code, developer judgment and experience will be vital. The dangers of AI-generated code are already here The risks of AI-generated code aren’t science fiction: they’re with us today. Research done by GitClear earlier this year indicates that with AI coding assistants (like GitHub Copilot) going mainstream, code churn — which GitClear defines as “changes that were either incomplete or erroneous when the author initially wrote, committed, and pushed them to the company’s git repo” — has significantly increased. GitClear also found there was a marked decrease in the number of lines of code that have been moved, a signal for refactored code (essentially the care and feeding to make it more effective).
In other words, from the time coding assistants were introduced there’s been a pronounced increase in lines of code without a commensurate increase in lines deleted, updated, or replaced. Simultaneously, there’s been a decrease in lines moved — indicating a lot of code has been written but not refactored. More code isn’t necessarily a good thing (sometimes quite the opposite); GitClear’s findings ultimately point to complacency and a lack of rigor about code quality. Can AI be removed from software development? However, AI doesn’t have to be removed from software development and delivery. On the contrary, there’s plenty to be excited about. As noted in the latest volume of the Technology Radar — Thoughtworks’ report on technologies and practices from work with hundreds of clients all over the world — the coding assistance space is full of opportunities. 
Specifically, the report noted tools like Cursor, Cline and Windsurf can enable software engineering agents. What this looks like in practice is an agent-like feature inside developer environments that developers can ask specific sets of coding tasks to be performed in the form of a natural language prompt. This enables the human/machine partnership. That being said, to only focus on code generation is to miss the variety of ways AI can help software developers. For example, Thoughtworks has been interested in how generative AI can be used to understand legacy codebases, and we see a lot of promise in tools like Unblocked, which is an AI team assistant that helps teams do just that. In fact, Anthropic’s Claude Code helped us add support for new languages in an internal tool, CodeConcise. We use CodeConcise to understand legacy systems; and while our success was mixed, we do think there’s real promise here. Tightening practices to better leverage AI It’s important to remember much of the work developers do isn’t developing something new from scratch. A large proportion of their work is evolving and adapting existing (and sometimes legacy) software. Sprawling and janky code bases that have taken on technical debt are, unfortunately, the norm. Simply applying AI will likely make things worse, not better, especially with approaches like vibe.   This is why developer judgment will become more critical than ever. In the latest edition of the Technology Radar report, AI-friendly code design is highlighted, based on our experience that AI coding assistants perform best with well-structured codebases.  In practice, this requires many different things, including clear and expressive naming to ensure context is clearly communicated (essential for code maintenance), reducing duplicate code, and ensuring modularity and effective abstractions. Done together, these will all help make code more legible to AI systems. Good coding practices are all too easy to overlook when productivity and effectiveness are measured purely in terms of output, and even though this was true before there was AI tooling, software development needs to focus on good coding first. AI assistance demands greater human responsibility Instagram co-founder Mike Krieger recently claimed that in three years software engineers won’t write any code: they will only review AI-created code. This might sound like a huge claim, but it’s important to remember that reviewing code has always been a major part of software development work. With this in mind, perhaps the evolution of software development won’t be as dramatic as some fear. But there’s another argument: as AI becomes embedded in how we build software, software developers will take on more responsibility, not less. This is something we’ve discussed a lot at Thoughtworks: the job of verifying that an AI-built system is correct will fall to humans. Yes, verification itself might be AI-assisted, but it will be the role of the software developer to ensure confidence.  In a world where trust is becoming highly valuable — as evidenced by the emergence of the chief trust officer — the work of software developers is even more critical to the infrastructure of global industry. It’s vital software development is valued: the impact of thoughtless automation and pure vibes could prove incredibly problematic (and costly) in the years to come. This content was produced by Thoughtworks. It was not written by MIT Technology Review’s editorial staff.