Donald Trump shakes Peter Thiel's hand during a meeting with technology executives, December 14, 2016.<\/p>\n
Drew Angerer \/ Getty Images<\/p>\n
The newest White House tech strategy pushes faster development of emerging technologies for the military, gives defense innovators more room to experiment, and will broaden the federal market for young defense tech startups. But it also doubles down on a particular approach to developing AI\u2014one that favors a handful of well-connected U.S. tech companies at the expense of other innovative approaches. That could benefit China and slow the U.S. military from getting the tools commanders actually want.\u00a0<\/p>\n
Here\u2019s a breakdown of the winners and losers.<\/p>\n
Winners: swarms, deployed AI, faster military technology<\/p>\n
The strategy puts a big emphasis on new, tech-driven approaches to warfare, particularly the use of drones and even drone swarms alongside manned and legacy assets. Specifically, it calls for \u201coptimal combinations of lower-cost (and sometimes lower-tech or attritable) platforms that can be deployed in larger numbers and complement more limited numbers of sophisticated platforms.\u201d<\/p>\n
Over the last decade, efforts to field autonomous aircraft alongside fighter jets or ships, for instance, have been uneven at best\u2014even when the Pentagon changed its talking points on the necessity for large numbers of low-cost drones\u2014constrained by programs' internal buying policies and previous commitments to other programs' investments. The Replicator program provides a vivid example, hailed as an essential pathfinder but receiving only $1 billion in funding.<\/p>\n
The new strategy sets specific \u201cpriority areas\u201d for new military research and spending: undersea, space, and AI and autonomy. These priorities are the most relevant to \u201cdeterrence in the Indo-Pacific\u201d\u2014as in, preventing China from launching a major conflict. Within those priority areas, it lists \u201cmulti-agent systems and swarm intelligence\u201d and various uses of autonomy, from robotics to command and control, as \u201ccritical.\u201d<\/p>\n
The strategy also takes square aim at policies and practices that slow down military buying of new technologies. Building off other executive orders, it pushes Pentagon buyers to bypass traditional acquisitions in favor of a \u201cmix\u201d of contract types, such as performance-based contracts and other transaction authorities, or OTAs. It also resets the tone for military purchasing toward \u201cfaster non-traditional approaches that may involve higher risk but offer high potential reward,\u201d something that commanders, younger defense tech leaders, and even lawmakers have long pushed for.<\/p>\n
Perhaps most importantly, it also takes steps to actually grow the market for younger defense tech players beyond just the military. It takes aim at foreign military sales rules and export controls that hobble the sale of much defense tech to allies. That will be especially important for newer defense companies trying to secure early-stage investment to survive. And it calls for other federal agencies to build \u201cstreamlined pathways\u201d for smaller companies to at least get their foot in the door, even via partnerships that don\u2019t offer young companies much more than access to federal infrastructure, such as \u201cCooperative Research and Development Agreements (CRADAs) and Agreements for Commercializing Technology (ACTs).\u201d<\/p>\n
While the strategy champions smaller, more innovative players in defense, it also weighs in on the debate about what the future of AI should look like and comes down on the side of Big Tech companies that are pushing a specific, energy-intensive, and deeply unpopular future of AI over emerging strategies favored increasingly by researchers. China will benefit from the oversight, Michael Schiffer, a partner at Scalare Advisors and former Deputy Assistant Secretary of Defense for East Asia, wrote on Tuesday for Just Security.<\/p>\n
In its section on critical technologies for support, the strategy lists \u201cFoundation models, including large language, multimodal, and world systems.\u201d The section also includes niche tech areas like brain-computer interfaces but omits entirely open-weight models and open-source software development.<\/p>\n
Foundation models, such as today\u2019s large language models from companies like OpenAI and Anthropic, represent just one potential method for building AI tools\u2014one that relies heavily on pushing as much data through as much computational infrastructure as possible to achieve something that looks like \u201creasoning,\u201d but that\u2019s really just plain old probability calculation.<\/p>\n
Anthropic co-founder Dario Amodei helped to transform that concept from a research effort into a massive endeavor to acquire computational resources and data as quickly as possible. Amodei explained it simply in a 2023 discussion with Dwarkesh Patel. \u201cIt turns out that raw scale\u2014more compute and more data\u2014drives capabilities far more powerfully than complex algorithmic inventions or hand-crafted architectural changes.\u201d<\/p>\n
A small handful of frontier AI labs, and their big cloud computer backers, are in a literal race for company survival because there\u2019s only so much power, so many chips to continue to pursue \u201craw scale.\u201d If, as Amodei says, just having more \u201cmore compute\u201d and \u201cmore data\u201d means having the best performing models, then whoever has the most of those resources can push out any competitor.\u00a0<\/p>\n
It\u2019s no wonder the AI race has become a concentration of wealth and power, in just a handful of labs with their large cloud compute backers. No wonder, also, that public perception of AI has plummeted: just 18 percent of Americans believe it will be a positive force for the United States, one new poll found. But the foundation model approach does have its fans: large-scale U.S. cloud providers who have invested billions in foundation model labs.\u00a0<\/p>\n