A new propulsion system feeds the same "monopropellant" fuel into electrical and chemical thrusters, potentially saving on mass and complications for future missions to Mars.
A new Chinese quantum computing system pairs two independent neutral-atom arrays in one processor, aiming to boost stability, efficiency and scalability.
Last year, Microsoft announced its Majorana quantum computing chip, which supposedly used topological qubits to make calculations. The problem was that the paper published alongside the announcement didn’t show any evidence for those claims. Now they’ve announced a new quantum chip. Is it any better this time?. Let’s take a look.
Across the stories today, a common thread emerges: the growing tension between our rapidly expanding digital world and the natural systems struggling to sustain it. From AI data centers planned for drought-stricken regions to coal plants getting emergency extensions, we’re witnessing the collision between technological acceleration and environmental limits.
The numbers tell a stark story. Two-thirds of new AI data centers are slated for areas already grappling with water scarcity, while these facilities demand massive amounts of both electricity and cooling water. Meanwhile, Florida’s coal plant—scheduled for retirement as part of a clean energy transition—gets an emergency lifeline to keep the lights on. It’s a reminder that progress and pressure often arrive together, forcing difficult choices between immediate needs and long-term sustainability.
The day’s coverage points to growing momentum around accountability, particularly in how we fund and regulate resource-intensive industries. Australian lawmakers are pushing for fair taxation of AI infrastructure, determined not to repeat what they see as mistakes with gas exports. North Carolina legislators face their own paradox, crafting bills that simultaneously curb data center expansion while boosting fossil fuel development. These policy tensions reflect a broader reckoning: how do we harness technological progress without overwhelming the ecosystems that support us?
Yet signs of genuine environmental recovery punctuate today’s more challenging headlines. Water voles have returned to an English nature reserve after four decades, while marine protected areas now cover 10% of the world’s oceans—a significant milestone toward the 30% goal by 2030. In Sumatra’s hills, coffee farmers like Sri Atmiatun are proving that conservation and community prosperity can grow together, restoring forests while securing livelihoods through innovative social forestry programs.
Behind the numbers are real communities adapting in real time. Alaska’s fishing industry faces uncertainty as critical ocean monitoring systems face budget cuts, while Arizona’s San Carlos Lake sits closed indefinitely after drought and dam releases killed all fish life. These aren’t abstract environmental statistics—they’re disruptions to ways of life, forcing entire communities to reimagine their relationships with changing landscapes.
The financial flows reveal where power and priorities truly lie. The world’s largest banks poured a record $906 billion into fossil fuels despite climate commitments, an 8% increase that environmental researchers call “unfathomable.” Meanwhile, China launched the world’s first wind-powered underwater data center, signaling that sustainable technology infrastructure is possible when political will aligns with innovation.
Perhaps most troubling is what we’re losing while focused on these larger battles. Wildfire smoke has erased four years of air quality progress across the United States, while sea levels are now rising at twice the rate recorded just a decade ago. Scientists are racing to record forest sounds before wild spaces fall silent—a poignant reminder that some losses can’t be reversed.
The day’s stories also illuminate the gap between good intentions and effective action. Louisiana legislators unanimously approved expanding their wood pellet industry despite the bill’s sponsor admitting he knows little about the sector. FIFA appears unprepared for wildfire smoke threats at upcoming World Cup venues. These moments reveal how quickly environmental challenges can outpace institutional readiness.
As the week unfolds, all eyes will be on whether emerging solutions—from underwater data centers to marine protection expansions—can scale fast enough to match the magnitude of the challenges. The central question remains: can we innovate our way toward sustainability, or will the pace of environmental change force more fundamental reckonings with how we live and build in a resource-constrained world?
A new proposed Senate bill aims to establish clear guardrails for the U.S. military’s use of artificial intelligence (AI), drawing a definitive line on one of the most consequential questions in modern warfare: who has the final say when lethal force is used?
On June 8, Senator Adam Schiff (D-Calif.) introduced the proposed Human Authority in Lethal Operations Act of 2026, or “HALO Act,” which would impose sweeping requirements on the Department of War (DoW) to ensure that autonomous and semi-autonomous weapon systems remain under meaningful human control.
The legislation would establish new design standards, testing requirements, oversight procedures, and legal safeguards for military systems that use artificial intelligence to identify, recommend, prioritize, or engage targets.
The bill aims to ensure that responsibility for battlefield decisions remains with human commanders, rather than shifting to algorithms. The legislation would require the Pentagon to designate accountable commanders for any engagement involving AI-enabled autonomous or semi-autonomous weapons and mandates that those commanders retain “ultimate discretion, judgment, and control over the use of force.”
“The past few months have shown us that there is an urgent need for commonsense guardrails to ensure the Defense Department’s use of AI is in line with Americans’ national security and privacy priorities,” Sen. Schiff said in a press release. “There are good reasons to use AI technology to advance our national security,” although he emphasized that, like with any tool, AI should not be allowed solely to guide human enterprises, “particularly when the risks of harm can be fatal.”
The proposal arrives amid growing concern among policymakers, military planners, and technology experts about the increasing role of artificial intelligence in combat operations.
Significantly, these concerns are no longer theoretical. As The Debrief previously reported, Israel’s reported use of the AI targeting system “Lavender” in Gaza triggered renewed debate over how much trust militaries should place in algorithmic target selection, particularly when civilian lives may be at risk.
Other recent examples have added to the concern, including studies showing that AI models can escalate dangerously in simulated war games, as well as the Pentagon’s continued push toward autonomous drones, AI-controlled aircraft, and robotic battlefield systems.
Even Pope Leo XIV recently weighed in on the issue in Magnifica Humanitas, his first encyclical, or formal papal teaching letter, warning that autonomous weapons could make war more feasible and less subject to human control, and arguing that lethal decisions must not be delegated to opaque or automated processes.
While the Pentagon has repeatedly stressed its commitment to responsible AI development, the HALO Act would codify many of those principles into law and create new statutory requirements governing how military AI systems are designed, tested, approved, and monitored.
Among the bill’s most significant provisions are requirements that autonomous weapon systems operate within predefined geographic areas, target sets, and timeframes. If a system cannot complete an engagement within those authorized parameters, it would be required to terminate the engagement until additional human operator and commander review is completed.
The legislation also calls for extensive record-keeping, cybersecurity protections, and continuous oversight to guard against unintended engagements and hostile manipulation.
The bill would also require senior Pentagon officials to conduct formal reviews before AI-enabled weapons enter development and again before they are fielded. Those reviews would assess legal compliance, operational safety, reliability, cybersecurity, and risks to civilians and protected infrastructure under the Law of Armed Conflict.
Beyond battlefield autonomy, the legislation targets several controversial uses of artificial intelligence in national security. It would prohibit covered Defense Department AI capabilities from inferring a person’s religion, race, political identity, sexual orientation, or emotional state. It would also restrict the purchase or receipt of Americans’ personal data from data brokers or other third-party sources when that data was obtained in a manner that would violate the bill’s rules.
Perhaps most notably, the proposed HALO Act explicitly forbids removing humans from decision-making processes involving presidential actions related to nuclear weapons.
“No covered artificial intelligence capability may be used with the intent, purpose, or outcome of… removing a human from the chain of decisionmaking for actions critical to informing and executing decisions by the President, including initiating or terminating nuclear weapons employment,” the proposed bill reads.
The measure also includes whistleblower protections for personnel working on military AI systems, mandates regular reporting to Congress, and requires the Pentagon to identify and remediate any fielded systems that fail to meet the law’s requirements.
Existing autonomous systems would undergo a comprehensive review, and any system found to be noncompliant could face retirement, modification, or suspension pending corrective action.
While the bill includes exemptions for certain defensive systems, cyber capabilities, mines, and other categories of military technology, its wider message is that as artificial intelligence becomes more deeply integrated into national defense, Congress wants humans, not machines, to remain responsible for high-stakes decisions.
The HALO Act is not the only effort underway on Capitol Hill to establish guardrails around military AI.
In March, Senator Elissa Slotkin (D-MI) introduced the AI Guardrails Act. Last week, Senator Kirsten Gillibrand (D-N.Y.) introduced separate legislation that would similarly prohibit the Pentagon from using artificial intelligence to launch nuclear weapons, conduct warrantless surveillance of Americans, or deploy fully autonomous weapons.
“Right now, the Pentagon is moving toward deploying incredibly powerful AI technology without commonsense guardrails in place, which could have catastrophic consequences that make all of us less safe,” Sen. Gillibrand said in a statement. “We must act now – not to stifle technological progress, but to establish clear rules of the road that keep humans in charge and keep AI’s use in warfare smart and safe.”
The competing proposals could set the stage for a broader congressional debate over the role of artificial intelligence in future military operations, particularly if lawmakers seek to incorporate them into the Fiscal Year 2027 National Defense Authorization Act (NDAA).
Before that can happen, however, the HALO Act and similar proposals must navigate the legislative process. The bills will first be considered by the relevant Senate committee, where lawmakers may hold hearings, debate their provisions, amend the text, or decline to advance them. If approved, it would then need to pass both the Senate and the House before being sent to the president for signature, either as a standalone measure or as part of a larger NDAA.
“My legislation would protect Americans from unlawful domestic surveillance, ensure that humans in the chain of command exercise responsibility for the use of any lethal technology, and maintain strong ethical protections in the deployment of autonomous and semi-autonomous weapons,” Sen. Schiff said in a statement.
Tim McMillan is a retired law enforcement executive, investigative reporter and co-founder of The Debrief. His writing typically focuses on defense, national security, the Intelligence Community and topics related to psychology. You can follow Tim on Twitter:@LtTimMcMillan. Tim can be reached by email: tim@thedebrief.org or through encrypted email:LtTimMcMillan@protonmail.com
As the United States grapples with record-breaking drought conditions across much of the country, the booming artificial intelligence industry appears to be on a collision course with water scarcity. New analysis reveals that approximately two-thirds of planned AI data centers are slated for construction in areas that have experienced some of the most severe drought conditions over the past year.
This concerning trend highlights a fundamental conflict between technological advancement and environmental sustainability. AI data centers are notorious for their enormous water consumption, requiring vast quantities of water for cooling systems to prevent the massive server farms from overheating. These facilities can consume millions of gallons of water daily, putting additional strain on already stressed water resources in drought-affected regions.
The timing couldn’t be worse for many communities already struggling with water shortages. The current drought has shattered previous records, leaving reservoirs at critically low levels and forcing water restrictions in numerous states. Yet the AI industry’s expansion continues largely unabated, with companies apparently prioritizing factors such as energy costs, tax incentives, and connectivity over water availability when selecting locations for new facilities.
This development raises serious questions about the sustainability of AI expansion and whether current planning approaches adequately consider long-term environmental impacts. As public concern grows over the water demands of AI infrastructure, communities and policymakers are increasingly calling for more responsible site selection and water-efficient technologies in data center development.
A growing grassroots movement in Australia is demanding that technology companies pay their fair share of taxes if they plan to establish energy-intensive AI data centers across the country. The campaign draws parallels to Australia’s experience with gas exports, where critics argue the nation failed to capture adequate revenue from companies extracting and exporting natural resources.
Over recent months, tens of thousands of Australians have contacted their representatives advocating for a 25% tax on gas exports, with more than 2,200 citizens funding billboard campaigns to promote the initiative. This momentum reflects broader concerns about ensuring foreign corporations contribute appropriately to public coffers when utilizing Australia’s natural resources and infrastructure.
The debate now extends to artificial intelligence infrastructure, as tech giants eye Australia for data center expansion. These facilities require enormous amounts of electricity, water for cooling systems, and land – all drawing heavily on public resources and utilities. Environmental advocates worry that without proper taxation frameworks, Australia could repeat past mistakes where resource extraction generated substantial corporate profits while providing minimal returns to taxpayers.
Proponents argue that fair taxation of AI data centers would not only generate revenue for public services and infrastructure but also ensure that the environmental costs of these energy-intensive operations are properly accounted for. As the AI industry continues rapid expansion globally, Australia faces a critical decision about how to balance economic opportunities with fair compensation for the use of national resources.
Datacentre off Shanghai coast uses less power and water than land-based equivalent
The world’s first wind-powered underwater datacentre has started operations off the coast of Shanghai, as China presses forwards with solutions for energy challenges created by the country’s artificial intelligence boom.
The Shanghai Lingang undersea datacentre demonstration project, which launched in May, has a capacity of 24 megawatts. It is a joint effort between HiCloud Technology and China Communications Construction, a state-owned company.
Scientists have finally packed a laboratory-class ultrafast laser onto a tiny photonic chip. Ultrafast lasers generate bursts of light that last only a few hundred femtoseconds, each one just a quadrillionth of a second long. These extremely short pulses are used in a wide range of technologies, including precision manufacturing, eye surgery, and optical frequency [...]
Researchers may have unlocked the future of computing by turning flat silicon chips into densely stacked 3D architectures. For decades, the semiconductor industry has boosted computing power by making transistors smaller and fitting more of them onto a single chip. That strategy has fueled remarkable advances in electronics, but it is now approaching fundamental physical [...]
We cannot afford to make the same mistake as we did with gas. If tech companies are going to use our land, energy and water for AI, they must pay their fair share of tax
Over the past few months, tens of thousands of Australians have emailed their local MP calling for a 25% tax on gas exports. More than 2,200 people have even chipped in their own money to fund billboards promoting the idea.
Across the stories today, a common thread emerges: the gap between environmental promises and practice is forcing communities to take matters into their own hands, even as powerful interests dig in their heels.
The day’s coverage points to growing momentum around local action filling spaces where larger systems have failed. In Iowa, farmer James Hepp is breaking ranks with agricultural tradition, calling for mandatory water quality standards after voluntary programs fell short on his 1,600 acres. Meanwhile, in Ghana, marine biologist Issah Seidu has convinced fishing communities to trade endangered guitarfish for giant snail farming—creating economic opportunity that doesn’t depend on depleting the ocean.
These grassroots shifts are happening against a backdrop of institutional retreat. The aviation industry quietly admitted its 2050 net-zero pledges will likely miss the mark, just five years after making them. Democratic politicians are avoiding climate messaging despite electoral benefits, a phenomenon researchers call “climate hushing.” And the Trump administration invoked wartime powers to pour $700 million into coal revival, framing fossil fuels as national security.
Yet nature itself is sending mixed signals that complicate neat narratives about environmental decline. UK waters are experiencing an unprecedented octopus population boom that’s reshaping marine ecosystems from England to Scotland. Tuna populations are showing remarkable recovery through strict international fishing regulations. But for every success story, there’s a reminder of fragility: critically endangered Chinese pangolins discovered thriving in Nepal’s community forests, or 119 sailors’ graves from 1801 now threatened by Norfolk’s rising seas.
It’s a reminder that progress and pressure often arrive together. The same regulatory changes that created UK electric vehicle loopholes will pump an extra 17 million tonnes of carbon into the atmosphere by 2030. North Carolina legislators are simultaneously curbing data centers’ massive electricity consumption while boosting fossil fuel incentives—an environmental paradox wrapped in a single bill.
Behind the numbers are real communities adapting in real time. Over 600 Thai residents just completed a 42-mile “peace walk” demanding government action on heavy metal river pollution. In the Dominican Republic, 65-year-old Leoncia Ramos wants to leave the home she’s known her entire life, fleeing gold mine pollution while Canada’s corporate watchdog office sits empty. Long Island residents in Brookhaven are discovering that decades of landfill contamination now threatens their groundwater.
These stories reveal something crucial about this environmental moment: when institutions fail or retreat, communities don’t wait. They organize marches, develop alternative livelihoods, break with traditional alliances, and demand accountability. Whether it’s Michigan’s farm training program addressing agricultural crisis through hands-on education, or Malawi’s Elephant Marsh feeding thousands while facing conservation pressures, local knowledge and local action are filling critical gaps.
The tension between extraction and protection remains fierce—Shell operated leaking Nigeria pipelines for years despite knowing the risks to mangrove forests, while Colorado opens 156,000 acres of critical wildlife habitat to oil drilling. But today’s coverage also suggests that the most durable environmental progress might be happening at smaller scales, in communities that can’t afford to wait for global agreements or corporate promises.
As the week unfolds, all eyes will be on whether these grassroots innovations can scale up fast enough to match the challenges revealed in today’s headlines.
Electric vehicles and other clean energy devices rely on batteries that use a metal called lithium. The expanded use of lithium has made lithium mining a rapidly growing industry. Lithium is most often found in volcanic rocks called pegmatites. Researchers have studied how much carbon and water pegmatite mining uses, but they don’t know how it affects local water quality.
Pegmatite mining releases not only lithium but also other metals commonly found in pegmatite, such as rubidium and cesium. These trace metals are under-researched in terms of their environmental effects and possible human toxicity, though the EPA has linked high levels of lithium to adverse health effects in kidney function, neurological development, and thyroid function. As such, there’s currently no established safe level for lithium in drinking water, but the U.S. Geological Survey suggests a preliminary safety level of 10 micrograms per liter (μg/L).
Researchers at Duke University recently tested how much pegmatite mining increased lithium levels in local waters, and how long those effects lingered after mining processes ended. To do so, they measured metal concentrations in waters downstream from 2 lithium mines and adjacent processing plants. They hypothesized that mining activities would increase interactions between rocks and the surrounding surface water and groundwater, impacting their lithium concentrations.
The researchers collected 99 water samples from surface streams and 93 samples from groundwater wells surrounding the Kings Mountain and Hallman Beam lithium mines, in a zone of about 40 kilometers (25 miles) along the border of South Carolina and North Carolina. The researchers took 51 surface water samples upstream of the mines to evaluate natural metal levels, and compared these with 48 downstream samples showing the mining-impacted metal levels.
The researchers measured lithium and other trace elements, like rubidium, cesium, arsenic, and strontium, in the waters using an instrument called an inductively coupled plasma mass spectrometer. They found that lithium concentrations in the surface waters increased from a background of 0.2 to 79 μg/L to concentrations of 785 to 1,249 μg/L within 10 kilometers (6.2 miles) of the mines and processing facilities. The groundwater lithium levels were as high as 4,500 to 47,000 μg/L in the wells at the mines, but were not affected at downstream wells where lithium levels ranged from 0.5 to 890 μg/L.
They suggested that lithium in the groundwater downstream from the mines came from natural interactions of the pegmatite rocks with the water, rather than from the mines themselves. This natural process was confirmed when the researchers noticed that lithium levels in the groundwater rose after rainfall, which caused more water-rock interaction.
The researchers also measured ions like calcium, sulfate, and chloride in the waters using a tool that can analyze compound molecules, called an ion chromatograph. They found that calcium and sulfate concentrations increased in surface waters within 10 kilometers (6.2 miles) downstream of the processing facilities. These downstream waters had 50 to 120 milligrams per liter (mg/L) of calcium and 100 to 300 mg/L of sulfate, compared to 5 to 20 mg/L of calcium and 3 to 10 mg/L of sulfate in the background surface waters. These ions are also byproducts of pegmatite processing, produced from a waste product made up of calcium sulfate, called gypsum.
The team explained that active mining has been discontinued at this site for approximately 3 decades, meaning that the lithium concentrations they measured reflect the long-term release of lithium from inactive mines and mining waste. Drawing from historical data, they estimated that while the mines were active, they likely released 10 to 30 times the amount of trace metals they measured at the same location today.
The researchers concluded that hard-rock processing impacts downstream metal and ion concentrations more than the mining itself. As lithium mining operations expand, the researchers suggested that future researchers examine lithium toxicity as well as the potential effects of its co-occurring metals, like rubidium and cesium. They also emphasized the need to find solutions for removing the trace metals and dissolved gypsum from water systems, or preventing them from leaking into the water systems to begin with.
The platypus, Australia’s most famous evolutionary oddball, is teaching conservationists a crucial lesson: you can’t protect what you can’t properly count. This semi-aquatic mammal’s secretive lifestyle makes it notoriously difficult to study and monitor, creating challenges for scientists trying to assess its conservation status.
With their dawn and dusk feeding habits and preference for spending most of their time underwater in rivers, platypuses leave few obvious traces of their presence. The IUCN currently classifies the species as near threatened, estimating around 50,000 individuals remain in the wild, though researchers acknowledge this number is highly uncertain. This knowledge gap has become increasingly problematic as environmental pressures mount on Australia’s river systems.
Climate change and human activities are creating a perfect storm of threats for platypus populations. Extended droughts shrink the pools where they hunt for food, while intense bushfires damage critical riverbank habitat and surrounding vegetation. Devastating floods can overwhelm their burrows faster than animals can escape, and pollution from mining, industry, and urban runoff depletes the aquatic invertebrates that form the backbone of their diet.
Despite these challenges, there’s reason for hope. Scientists have developed new frameworks to guide conservation decisions, helping determine when to protect platypuses in their current habitats versus when relocation might be necessary. Zoos are preparing enhanced emergency response capabilities, and innovative citizen science projects combined with environmental DNA sampling from water sources are helping fill critical data gaps about where platypuses still survive.
Anyone can build an app now. But nobody seems to care.
Well, not nobody. VCs keep funding startups that add AI to, well, everything. But users aren’t buying the massive influx of new apps. In a chart shared by Jen Zhu Scott based on the new National Bureau of Economic Research’s working paper “Writing Code vs. Shipping Code,” iOS app releases have exploded since the advent of agentic AI. That would perhaps be cause for celebration had app reviews not declined during this same period, and apps with significant usage have stayed essentially flat.
In other words, more apps but almost nobody new showing up to use them.
For those of us that grew up in open source, it’s a familiar problem. The greater the abundance of code, the greater the need to help would-be customers navigate it through marketing (including branding), sales, etc. AI is creating so much noise, in terms of new code, new products, etc., that the real work has shifted to taste-making.
Getting more but not using more
I’ve been saying for a while that developer productivity isn’t about producing more code faster. Or at least it shouldn’t be. Productivity is about producing well-architected, secure, maintainable code that solves a problem someone actually has.
That’s a very different thing. It’s not something AI can fix; at least, not yet.
Charity Majors put it more bluntly in a post I wrote in 2024: “Writing code is the easiest part of software engineering,” she said. The harder parts are figuring out what to build, integrating it into a larger system, validating that it works, maintaining it over time, and getting humans to trust it enough to use it.
Turns out the harder parts are really hard. Mert Demirer, Leon Musolff, and Liyuan Yang tracked more than 100,000 GitHub developers alongside their AI usage telemetry. Autocomplete, interactive agents, and autonomous agents each ramped raw coding activity, with cumulative effects on commits of 40%, 140%, and 180%. That sounds great until you look how the gains attenuate the closer you get to actual users. For example, that 180% jump in commits became roughly 50% more projects and just 30% more actual releases. The report’s authors call this the weak-link problem: The strong link (writing code) got much stronger, while the weak links (everything else humans have to do) didn’t. The estimated elasticity of substitution between AI and human effort is 0.25, which is economist for “these complement each other, but they don’t replace each other.”
When the authors checked four major app marketplaces, they found a bump in new apps but no increase in total usage. In other words, we’re getting better at creating things with AI’s help; apparently we’re not good at turning that into user interest. User attention (and budget) is finite. The hard part is to figure out how to get users to care enough to pay (with their time or their money).
Learning to love marketing
So what’s actually scarce in a world of near-infinite software? Not code, for sure. No, what’s scarce is attention, trust, and a reason to switch.
That means the durable advantages in software increasingly live in the parts of the business developers often undervalue, like a recognizable brand (something that Red Hat figured out early on in open source) or a channel they already use. Or it could be in areas that developers appreciate but don’t pay for, like good documentation or a welcoming community.
This isn’t a new idea; it’s just newly unavoidable. We’re watching something similar inside enterprises, where AI adoption iswildly uneven, not because the technology has no value, but because the organizational plumbing around it often hasn’t been built. This is why a new kind of speed is important. I once argued that speed was the killer app, and that was mostly true. Today the more interesting speed is how quickly you can earn trust, fit into a workflow, answer objections, and get adopted.
Years ago I wrote that RethinkDB was dead and MongoDB wasn’t what killed it. RethinkDB was at the time, by many technical measures, a better database, built around “correctness, simplicity, and consistency.” It still lost—and badly. Its founder’s own postmortem was unsparing: They had picked a brutal market and tuned the product to the wrong definition of good. Being technically right turned out to have almost nothing to do with getting adopted.
With AI we’re generating a thousand smaller “RethinkDBs,” only faster and with nicer landing pages generated by the same model that wrote the code. These aren’t going to win, any more than RethinkDB was able to unseat MongoDB. It’s not just about the tech, but rather about making that tech fit within a user’s or enterprise’s world and making it easy to adopt. AI makes this harder, not easier.
It’s a cliché that developers don’t like marketing. It’s also false in my inexperience. What developers don’t like is traditional marketing. During my time at MongoDB and now at Oracle, my developer relations teams have focused on offering developers deep, hands-on enablement, and the response has been fantastic. Is a technical tutorial marketing? Of course it is. So is a forward-deployed engineer working side by side with an enterprise’s engineers to help them effectively use your tech. Just because it’s not a 30-second Super Bowl ad doesn’t mean it’s not marketing.
Years ago Matt Klein, creator of the open source Envoy project, once talked me through all the non-development work that goes into making an open source project thrive. As he reflected, “If you look at what I did in 2016 and early 2017 to [introduce] and grow the project, it was not technical.” So what was it, if not core engineering? “It was all leadership, public relations, marketing, documentation, etc., and I did it all myself and I nearly killed myself [doing it].” As he summed it up for me, it was a “f—ing lot of work,” and much (most?) of it wasn’t about code. It was about helping users appreciate the value of that code.
TL;DR? The boring but essential go-to-market grind has always been the real job. It’s what will separate winners from losers in AI.
The boring work wins
None of this is an argument against AI coding tools. Developers should absolutely use them. I use them constantly.
But AI can’t compensate for the hard work that goes into making a product successful in the market. In a world drowning in AI-generated sameness, taste becomes a competitive advantage. For me, “taste” translates into knowing what not to build, or what not to publish. It’s also all about knowing how to market one’s product, which might include ads but definitely needs to incorporate technical training that helps developers make sense of your code.
In short, there’s no Field of Dreams “build it and they will come.” There’s just a lot of hard, human work to help real people find and use your code.
Luxembourg’s OQ Technology plans to test direct-to-smartphone satellite connectivity next year in Germany using a local telco’s cellular spectrum, setting up a challenge to U.S.-led services in the emerging market.
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