A MEO desenvolve e disponibiliza APIs standard de rede móvel 5G para autenticação aplicacional, prevenção de fraude e proteção de transações críticas. Esta aposta conta com a parceria da Nokia, Infobip e Aduna.
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Because generative AI (genAI) tools and services have become so ubiquitous (and popular), the costs of using them are going through the roof — leading to an insatiable appetite for tokens.
Tokens represent a common way to measure and price AI use. Much like letters and words in English, large language models (LLMs) grasp a sentence or query by breaking words into tokens.
With the AI explosion well under way, tokens are now “the fundamental units of data our models process, many representing a problem being solved,” according to Google CEO Sundar Pichai. (Google, by the way, processes about 3.2 quadrillion tokens a month.)
But as the price of all those tokens adds up, business and IT execs are looking for ways to cut costs while keeping corporate productivity up. Uncontrolled token use has already landed one company with an unexpected $500 million AI bill.
There are a number of ways companies can rein in the price of AI at the model, infrastructure, silicon, and business levels. Here’s a look at how some of those savings might actually be achieved.
One way of potentially saving money is by re-routing AI work to a cheaper model, Pichai said. At Google that would Gemini 3.5 Flash. It delivers “frontier-level capabilities at less than half the price of comparable frontier models.
“If companies use a mix of [Gemini 3.5] Flash and other frontier models, they could save a lot of money,” Pichai said.
Those kinds of models provide cheaper tokens, with reasoning that’s good enough for many users — if not as strong as mainstream Gemini 3.5 — to deliver useful results.
“There is sometimes overkill with the [LLMs],” said Deepak Seth, senior director analyst at Gartner. “I don’t always need a large language model which has been trained on the works of Charles Dickens and Shakespeare and Harry Potter.”
Hyperframe Research principal analyst Steven Dickens can’t stop using Amazon’s Quick, which costs $20 a month, for personal tasks. “It is great personal ROI as it has not only made tasks faster, but unlocked tasks I would never have even attempted previously,” Dickens said.
The token crisis isn’t new, said Dheeraj Pandey, CEO of DevRev, who likens what’s going on now in the AI market to the disruptions that emerged with the arrival of cloud computing and virtualization years ago.
“We let chaos reign and then we had to rein in the chaos,” Pandey said. “The word that people started using was server consolidation and virtualization.”
The answer to the token problem, he said, is the same: “Anything in systems can be solved with caching and indirection.”
DevRev, for example, is building a memory layer between AI agents and primary data sources, such as Salesforce or ERP records; that can cut token load and make data movement more efficient. The layer holds a knowledge graph with answers to common agent questions and runs on cheaper CPUs, avoiding more costly GPU cycles.
Sending agents straight at systems like ServiceNow and Salesforce “will burn a lot more tokens. It’s also not precise. And finally, it’s not safe enough where I can roll it back in case an agent has committed a mistake,” Pandey said.
Network automation firm NetBrain uses a different method: It uses conventional computing to map a network’s layout then feeds only key information to models for planning and reasoning, where AI excels. “So you don’t have to spend all the tokens,” said Netbrain CTO Song Pang.
Staffing firm ManpowerGroup has found that prompt efficiency can be an effective tool for improving token use, both internally and externally for clients.
For example, users accessing its internal labor-market tool initially needed 10 follow-up questions to drill into a query. A year later, more efficient use of prompts has brought that number down to an average of four, said Max Leaming, head of data science and AI solutions at ManpowerGroup.
“They’re using fewer tokens and they’re simply more efficient,” he said. “And that in large part has to do with your ability to prompt efficiently.”
New AI hardware that generates free tokens at home could ease some of the cost crisis.
At GTC Taipei earlier this month, Nvidia and Microsoft unveiled RTX Spark, an agentic AI desktop PC that runs agents and 120-billion-parameter models locally on Windows. The goal is “to deliver unmetered intelligence to every home and every desk with Windows,” Microsoft CEO Satya Nadella said in a statement.
Some companies are looking to reduce cloud AI costs by putting their own hardware in data centers, with vendors such as HPE and Dell providing servers installed in independent facilities. (On-premise AI is gaining ground amid sovereign AI and geopolitical concerns, including the recent conflict in the Middle East, where large data centers were struck with missiles.)
“There are local, region-specific and multiple vendor AI solutions. All of those things can help mitigate the risk. But they’re not going to eliminate it,” said Max Goss, senior director analyst at Gartner.
Reducing token costs is something that may fall to forward-deployed engineers (FDEs) in customer environments, said Taimur Rashid, managing director of AWS’s Generative AI Innovation Center.
“I expect these teams to be able to architect systems that have those cost requirements in mind, whether it’s use a different model or a different use case that doesn’t increase the per-token cost,” Rashid said.
Companies may spend heavily on token consumption, “but if you’re generating revenue, as long as the economics work out, then you’re at peace,” Rashid said.
The use of FDEs is gaining ground as IT decision-makers look to both rollout successful AI deployments while also keeping an eye on costs.
Even with the current emphasis on reducing token use to save money, the metrics used to measure AI success are likely to shift, Gartner’s Seth said. At some point, token-based pricing will move more toward an outcome-based model, where the unit of value is outcomes, not fragments of words.
“Some companies are moving towards outcome-based pricing,” Seth said. “When people start realizing the real cost of tokens, then companies will start looking at token efficiency.”
Across the expansive military installations of the United States, where substantial tracts of restricted-access natural lands persist, an evolving challenge has taken shape: how to effectively conserve endangered species amid overlapping and often conflicting recovery plans. These lands, which serve dual purposes of defense readiness and environmental stewardship, present a unique ecological puzzle. Amid this complexity, researchers at Virginia Tech’s College of Natural Resources and Environment have embarked on pioneering projects funded by two $2 million grants, aiming to redefine conservation strategies on military properties by emphasizing ecosystem-wide management rather than isolated species-specific interventions.
Historically, wildlife management on military bases has heavily relied on individual species’ recovery plans, a methodology that becomes problematic when multiple endangered species inhabit the same territory. Each species’ plan often prescribes distinct conservation actions, potentially leading to redundant efforts, increased management complexity, and sometimes conflicting directives. Recognizing these inefficiencies, the Virginia Tech research initiative seeks to develop integrated strategies that optimize land management actions to benefit diverse species assemblages simultaneously. This integrative approach has the potential not only to streamline operational practices on military lands but also to serve as a scalable model for ecosystems worldwide grappling with multifaceted conservation demands.
One cornerstone of this research is led by Assistant Professor Elizabeth Hunter, who operates within the U.S. Geological Survey’s Virginia Cooperative Fish and Wildlife Research Unit. Hunter’s project area in southeastern Georgia—including large military bases characterized by fire-managed ecosystems—examines the application of prescribed burning as a habitat management tool. Prescribed fire has traditionally been tailored around a handful of focal species, such as the red-cockaded woodpecker, leaving many other species with differing ecological needs inadequately addressed. Hunter’s work challenges this paradigm by employing cutting-edge monitoring technologies to survey a wide spectrum of taxa, from avian and mammalian species to amphibians, reptiles, insects, and crucial pollinators, aiming to capture a comprehensive biological response to various fire regimes.
By integrating extensive biodiversity data into a sophisticated multi-objective optimization framework, Hunter’s team can evaluate how different prescribed burning strategies affect numerous species simultaneously. This analytical tool allows for the assessment of trade-offs and synergies in conservation outcomes, addressing complex ecological interactions. The methodology asks crucial questions such as whether it is possible to adjust burn frequency, intensity, and spatial patterns to enhance habitats for multiple species concurrently. Such questions are vital for military land managers, who must balance ecosystem health with operational imperatives. Collaborations with organizations like the Orianne Society and the National Council for Air and Stream Improvement anchor this work in both academic and applied conservation contexts.
Parallel to Hunter’s fire ecology focus, Associate Professor Haldre Rogers directs research tackling the inefficiencies inherent in the current species-by-species recovery plan framework under the U.S. Endangered Species Act. Rogers’ project, focused on the Marianas Islands, explores whether ecosystem-wide habitat management can serve as an effective surrogate for the management of multiple individually listed species. Given that military lands often harbor dozens of endangered species with overlapping but separate recovery plans, the question arises: can habitat proxies be identified that allow for effective ecosystem-level conservation without the cumbersome requirement of separate interventions and monitoring for each species?
Rogers utilizes innovative ecosystem hypergraph modeling to encapsulate the intricate web of species interactions, habitat connectivity, and life-history traits, thereby identifying clusters of species that might respond similarly to shared management actions. This approach strives to uncover ecological congruencies and potential conflicts, aiming to inform management choices that maximize conservation benefits while minimizing inadvertent harm to co-occurring species. The project acknowledges inevitable ecological trade-offs but aspires to equip land managers with a landscape-scale understanding that promotes more efficient and informed decision-making.
The ecological importance of military installations in the United States cannot be overstated. Many contain well-preserved habitats that have been safeguarded from urban development, offering refugia for imperiled species. Activities such as prescribed burning and restricted access have inadvertently preserved biodiversity hotspots. At the same time, these installations must fulfill critical defense and training missions, underscoring the need for a delicate balance between operational readiness and environmental stewardship. The innovations proposed by Hunter, Rogers, and their teams promise a new frontier in reconciling these dual mandates through science-driven, scalable conservation models.
Their research partnership traverses multiple disciplines and institutions, spanning Virginia Tech’s Department of Fish and Wildlife Conservation and Statistics, collaborations with Rice University, Brown University, MIT, and contributions from postdoctoral scholars. This transdisciplinary approach enriches the projects with a fusion of ecological expertise, advanced computational modeling, and applied management perspectives. By leveraging emerging technologies and sophisticated analytical tools, these projects are poised to generate transformative insights relevant to conservation practitioners, policymakers, and military land managers alike.
In essence, these Virginia Tech-led studies embody a paradigm shift: transitioning from fragmented, species-specific management plans to holistic ecosystem-based strategies is both a necessity and an opportunity. This shift acknowledges the complex interdependencies among species and their habitats, calling for innovative methodologies that embrace ecological complexity rather than oversimplify it. For military lands, where the intersection of conservation and operational use is most pronounced, such research can catalyze both more effective biodiversity protection and enhanced land-use efficiency.
By integrating large-scale biological monitoring with computational optimization and ecosystem modeling, this research presents a robust framework for addressing the mounting challenges of endangered species management amid competing land uses. The implications extend far beyond military properties, offering a strategic blueprint adaptable to diverse ecosystems facing overlapping conservation mandates. The collective efforts spearheaded by Hunter and Rogers represent a significant leap forward in safeguarding the nation’s biological heritage through science-guided stewardship, setting the stage for broader applications in the global conservation community.
As endangered species faces continue to escalate globally, the insights arising from military installation ecosystems can inform best practices worldwide. The integration of multi-species data into management decisions, the recognition of ecosystem processes over isolated species needs, and the embracing of technological and analytical innovation underscore the future of conservation science. In a world of finite conservation resources and competing priorities, finding operationally feasible yet ecologically sound approaches is paramount.
These projects, supported by the Strategic Environmental Research and Development Program, exemplify such an approach, bridging theoretical science and practical land management. Their success will not only enhance the resilience of endangered species populations on military lands but also serve as a clarion call to redefine conservation paradigms universally. Ultimately, their work invites a transformative dialogue around how humanity navigates the complex stewardship of nature amid modern societal demands.
Subject of Research:
Endangered species management on military lands through ecosystem-based strategies and multi-species conservation optimization.
Article Title:
Rethinking Conservation: Integrated Ecosystem Management for Endangered Species on Military Installations
News Publication Date:
Not provided
Web References:
Image Credits:
Photo courtesy of Elizabeth Hunter.
Keywords:
Endangered species, Extinction, Wildlife management, Conservation biology, Ecosystem management, Prescribed fire, Habitat connectivity, Multi-species optimization, Military land conservation, Ecosystem hypergraphs.
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