The amount of money appropriated for U.S. intelligence increased in 2016 by about 5 percent to a total of $70.7 billion, up from $66.8 billion the year before.
The total includes FY 2016 appropriations for both the National Intelligence Program (NIP) and the Military Intelligence Program (MIP), which were officially disclosed on October 28, as they have been each year since 2007.
Opponents of intelligence budget disclosure had argued for decades that release of the total budget figures would lead inexorably to further uncontrolled disclosures.
In 1976, former Director of Central Intelligence James Schlesinger told the Church Committee that “One of the problems here is the camel’s nose under the edge of the tent, and I think that that is the fundamental problem in the area. There are very few people who can articulately argue that the publication of those [budget] figures in and of themselves, if it stopped there, would be harmful. The argument is that then the pressure would build up to do something else, that once you have published for example the… budget, that the pressures would build up to reveal the kinds of systems that are being bought for that money, and it is regarded as the first step down a slippery slope for those who worry about those kinds of things.”
But that concern about a “slippery slope” appears to have been refuted in practice, and — aside from unauthorized disclosures — additional budget secrets have been effectively preserved.
After months of delay, the council tasked by President Trump to review the FEMA released its final report. Our disaster policy nerds have thoughts.
FAS and FLI partnered to build a series of convenings and reports across the intersections of artificial intelligence (AI) with biosecurity, cybersecurity, nuclear command and control, military integration, and frontier AI governance. This project brought together leaders across these areas and created a space that was rigorous, transpartisan, and solutions-oriented to approach how we should think about how AI is rapidly changing global risks.
Investment should instead be directed at sectors where American technology and innovation exist but the infrastructure to commercialize them domestically does not—and where the national security case is clear.
AI is already consequential, but its future trajectory remains contested. Policymakers should make their assumptions explicit, focus on what can be shaped rather than what can be perfectly predicted, and build institutions that can learn and respond as evidence changes.