While Google nailed the switch from R&D to deployment, it arguably still bet big on scaling up the wrong technology. In the early 2010s, the solar race looked like a tight competition between solar photovoltaic (PV) and utility-scale concentrated solar power (CSP), which uses sun-heated fluids to drive power turbines. Google quickly invested more than $1 billion in a slew of renewables companies and utilities, including big investments in CSP outfits BrightSource Energy and eSolar. A decade later, such choices aren’t looking promising, as CSP, too, has been losing out to PV’s continuing rapid cost declines.
Google is not alone in repeatedly misjudging the dropping price of solar cells over the last few decades and its impact on how we think about clean energy. Solar PV costs fell roughly by a factor of 10 in the past decade, on top of already impressive cost declines up to that point, for a total decline of around a factor of a hundred since US President Jimmy Carter unveiled solar panels on the White House in 1979. (Ronald Reagan took them down in 1986, during his second term as president.)
To put it in perspective, if gasoline had similarly dropped in price from 1979 levels, it would cost pennies a gallon today. Gasoline, of course, is a commodity, with prices fluctuating for a number of technological, economic, and political reasons. Solar PV prices are also driven by all these factors, but over the years, technology has clearly dominated. (This year, prices for solar PV modules have increased by around 18% because of a temporary crunch in the silicon supply chain.)
In its latest annual World Energy Outlook, the International Energy Agency declared solar PV to be “the cheapest source of electricity in history” for sunny locales with a low cost of financing. These two qualifications are important. Sun is obvious—solar is always going to be cheaper in Phoenix, Arizona, than in New York City—but the report concluded that solar is now cheaper than coal and natural gas in many places.
Financing is key to why this is true. Solar PV and other renewables such as wind have low or close-to-zero operating expenses—upfront costs have always been the big hurdle, and financing has been a big reason why. Thanks in part to various government policies, solar investment has become much less risky over the last decade or so, freeing up cheap money.
As a result, solar PV deployment has increased rapidly; it’s now the fastest-growing source of electricity globally, and figures to be for some time to come. It’s starting from a low base of installed capacity, however, far behind coal, gas, hydro, nuclear—even wind, which has been cheap for longer. And therein lies one of the biggest problems for solar PV. It might be the cheapest form of electricity for many, but that on its own doesn’t make the clean-energy transition nearly quick enough.
We need ever further technological advances. Why stop at grid parity, the point where it’s as cheap to build and operate solar PV as to supply electricity via fossil energy sources? Why not 10% cheaper? Why not strive to slash costs by another factor of 10 within a decade? Such drops are needed because the hallowed grid-parity goal is misleading—the real question is at what point utilities will actually abandon existing coal plants and switch to solar, rather than merely avoid adding new coal capacity. Solar needs to be so cheap it makes financial sense to build new solar capacity and shutter working coal and gas plants still making money for their owners.
All that calls for policy to both push existing solar technology and support R&D in new technologies. The entire package includes technology research, development, demonstration, deployment, and diffusion. Every step along this chain deserves direct government support, keeping in mind that it also gets increasingly more expensive the further down the chain one moves.
How to get cheaper
To better optimize investments to get to even cheaper solar, it’s worthwhile to understand what factors have driven down the cost of renewable power over the last few decades.
MIT energy systems scientist Jessika Trancik and her group find that the dramatic cost declines in solar cells over the course of three decades can largely be attributed to three factors: R&D leading directly to improvements in module efficiency (how much of the sunlight is converted into electricity) and other fundamental technological advances; economies of scale attributed to the size of solar-cell manufacturing plants and the increasing volume of inputs such as silicon; and improvements achieved through learning by doing.
None of that is too surprising, but what is less obvious is that the relative contribution of each varies greatly over time. From 1980 to 2000, R&D accounted for around 60% of cost declines, with economies of scale coming in at 20%, and learning by doing a distant third at around 5%; other largely unattributable factors account for the balance. That makes sense; it was a period of impressive advances in the efficiencies of solar cells but not a time of significant manufacturing and deployment. Since then, the pendulum has swung from R&D and fundamental technological improvements toward economies of scale in manufacturing, now accounting for over 40% of cost declines. It’s worth noting, however, that research advances still account for some 40% of declines.
The lesson for future investments that aim to make solar even cheaper: there should be direct support for all three, skewed toward economies-of-scale factors. Trancik’s findings only consider the solar PV module itself. That still leaves installation, connection to the grid, and other factors that make up total system costs. These are areas that will likely be improved as technicians and companies become more experienced. While the results of subsidies for increasing solar PV installations appear to be mixed at best, policies such as feed-in tariffs, which offer favorable long-term contracts to solar PV producers, and renewable portfolio or clean energy standards, which set quantity targets for renewables, show clear results in driving overall deployment.
No free lunch
Despite the dropping price of solar, the transition to renewables will still be costly. The big question, of course, is how expensive compared with what—climate change, too, comes with costs. Cheap solar gets even more financially attractive to developers if the social and environmental costs of carbon emissions from fossil fuels are considered.
A lot here hinges on the social cost of carbon (SCC), a tally of the financial damage each metric ton of carbon dioxide emitted today causes to the economy, society, and the environment—and, by extension, how much each ton of CO2 emitted should cost. It’s a number that says a lot about the true cost of coal and other fossil fuels—and about the appropriate support for solar PV and other renewables.
Everything you need to know about artificial wombs
The technology would likely be used first on infants born at 22 or 23 weeks who don’t have many other options. “You don’t want to put an infant on this device who would otherwise do well with conventional therapy,” Mychaliska says. At 22 weeks gestation, babies are tiny, often weighing less than a pound. And their lungs are still developing. When researchers looked at babies born between 2013 and 2018, survival among those who were resuscitated at 22 weeks was 30%. That number rose to nearly 56% at 23 weeks. And babies born at that stage who do survive have an increased risk of neurodevelopmental problems, cerebral palsy, mobility problems, hearing impairments, and other disabilities.
Selecting the right participants will be tricky. Some experts argue that gestational age shouldn’t be the only criteria. One complicating factor is that prognosis varies widely from center to center, and it’s improving as hospitals learn how best to treat these preemies. At the University of Iowa Stead Family Children’s Hospital, for example, survival rates are much higher than average: 64% for babies born at 22 weeks. They’ve even managed to keep a handful of infants born at 21 weeks alive. “These babies are not a hopeless case. They very much can survive. They very much can thrive if you are managing them appropriately,” says Brady Thomas, a neonatologist at Stead. “Are you really going to make that much of a bigger impact by adding in this technology, and what risks might exist to those patients as you’re starting to trial it?”
Prognosis also varies widely from baby to baby depending on a variety of factors. “The girls do better than the boys. The bigger ones do better than the smaller ones,” says Mark Mercurio, a neonatologist and pediatric bioethicist at the Yale School of Medicine. So “how bad does the prognosis with current therapy need to be to justify use of an artificial womb?” That’s a question Mercurio would like to see answered.
What are the risks?
One ever-present concern in the tiniest babies is brain bleeds. “That’s due to a number of factors—a combination of their brain immaturity, and in part associated with the treatment that we provide,” Mychaliska says. Babies in an artificial womb would need to be on a blood thinner to prevent clots from forming where the tubes enter the body. “I believe that places a premature infant at very high risk for brain bleeding,” he says.
And it’s not just about the baby. To be eligible for EXTEND, infants must be delivered via cesarean section, which puts the pregnant person at higher risk for infection and bleeding. Delivery via a C-section can also have an impact on future pregnancies.
So if it works, could babies be grown entirely outside the womb?
Not anytime soon. Maybe not ever. In a paper published in 2022, Flake and his colleagues called this scenario “a technically and developmentally naive, yet sensationally speculative, pipe dream.” The problem is twofold. First, fetal development is a carefully choreographed process that relies on chemical communication between the pregnant parent’s body and the fetus. Even if researchers understood all the factors that contribute to fetal development—and they don’t—there’s no guarantee they could recreate those conditions.
The second issue is size. The artificial womb systems being developed require doctors to insert a small tube into the infant’s umbilical cord to deliver oxygenated blood. The smaller the umbilical cord, the more difficult this becomes.
What are the ethical concerns?
In the near term, there are concerns about how to ensure that researchers are obtaining proper informed consent from parents who may be desperate to save their babies. “This is an issue that comes up with lots of last-chance therapies,” says Vardit Ravitsky, a bioethicist and president of the Hastings Center, a bioethics research institute.
The Download: brain bandwidth, and artificial wombs
Last week, Elon Musk made the bold assertion that sticking electrodes in people’s heads is going to lead to a huge increase in the rate of data transfer out of, and into, human brains.
The occasion of Musk’s post was the announcement by Neuralink, his brain-computer interface company, that it was officially seeking the first volunteer to receive an implant that contains more than twice the number of electrodes than previous versions to collect more data from more nerve cells.
The entrepreneur mentioned a long-term goal of vastly increasing “bandwidth” between people, or people and machines, by a factor of 1,000 or more. But what does he mean, and is it even possible? Read the full story.
This story is from The Checkup, MIT Technology Review’s weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
Everything you need to know about artificial wombs
Earlier this month, US Food and Drug Administration advisors met to discuss how to move research on artificial wombs from animals into humans.
These medical devices are designed to give extremely premature infants a bit more time to develop in a womb-like environment before entering the outside world. They have been tested with hundreds of lambs (and some piglets), but animal models can’t fully predict how the technology will work for humans.
Why embracing complexity is the real challenge in software today
The reason we can’t just wish away or “fix” complexity is that every solution—whether it’s a technology or methodology—redistributes complexity in some way. Solutions reorganize problems. When microservices emerged (a software architecture approach where an application or system is composed of many smaller parts), they seemingly solved many of the maintenance and development challenges posed by monolithic architectures (where the application is one single interlocking system). However, in doing so microservices placed new demands on engineering teams; they require greater maturity in terms of practices and processes. This is one of the reasons why we cautioned people against what we call “microservice envy” in a 2018 edition of the Technology Radar, with CTO Rebecca Parsons writing that microservices would never be recommended for adoption on Technology Radar because “not all organizations are microservices-ready.” We noticed there was a tendency to look to adopt microservices simply because it was fashionable.
This doesn’t mean the solution is poor or defective. It’s more that we need to recognize the solution is a tradeoff. At Thoughtworks, we’re fond of saying “it depends” when people ask questions about the value of a certain technology or approach. It’s about how it fits with your organization’s needs and, of course, your ability to manage its particular demands. This is an example of essential complexity in tech—it’s something that can’t be removed and which will persist however much you want to get to a level of simplicity you find comfortable.
In terms of microservices, we’ve noticed increasing caution about rushing to embrace this particular architectural approach. Some of our colleagues even suggested the term “monolith revivalists” to describe those turning away from microservices back to monolithic software architecture. While it’s unlikely that the software world is going to make a full return to monoliths, frameworks like Spring Modulith—a framework that helps developers structure code in such a way that it becomes easier to break apart a monolith into smaller microservices when needed—suggest that practitioners are becoming more keenly aware of managing the tradeoffs of different approaches to building and maintaining software.
Because technical solutions have a habit of reorganizing complexity, we need to carefully attend to how this complexity is managed. Failing to do so can have serious implications for the productivity and effectiveness of engineering teams. At Thoughtworks we have a number of concepts and approaches that we use to manage complexity. Sensible defaults, for instance, are starting points for a project or piece of work. They’re not things that we need to simply embrace as a rule, but instead practices and tools that we collectively recognize are effective for most projects. They give individuals and teams a baseline to make judgements about what might be done differently.
One of the benefits of sensible defaults is that they can guard you against the allure of novelty and hype. As interesting or exciting as a new technology might be, sensible defaults can anchor you in what matters to you. This isn’t to say that new technologies like generative AI shouldn’t be treated with enthusiasm and excitement—some of our teams have been experimenting with these tools and seen impressive results—but instead that adopting new tools needs to be done in a way that properly integrates with the way you work and what you want to achieve. Indeed, there are a wealth of approaches to GenAI, from high profile tools like ChatGPT to self-hosted LLMs. Using GenAI effectively is as much a question of knowing the right way to implement for you and your team as it is about technical expertise.
Interestingly, the tools that can help us manage complexity aren’t necessarily new. One thing that came up in the latest edition of Technology Radar was something called risk-based failure modeling, a process used to understand the impact, likelihood and ability of detecting the various ways that a system can fail. This has origins in failure modes and effects analysis (FMEA), a practice that dates back to the period following World War II, used in complex engineering projects in fields such as aerospace. This signals that there are some challenges that endure; while new solutions will always emerge to combat them, we should also be comfortable looking to the past for tools and techniques.
McKinsey’s argument that the productivity of development teams can be successfully measured caused a stir across the software engineering landscape. While having the right metrics in place is certainly important, prioritizing productivity in our thinking can cause more problems than it solves when it comes to complex systems and an ever-changing landscape of solutions. Technology Radar called this out with an edition with the theme, “How productive is measuring productivity?”This highlighted the importance of focusing on developer experience with the help of tools like DX DevEx 360.
Focusing on productivity in the way McKinsey suggests can cause us to mistakenly see coding as the “real” work of software engineering, overlooking things like architectural decisions, tests, security analysis, and performance monitoring. This is risky—organizations that adopt such a view will struggle to see tangible benefits from their digital projects. This is why the key challenge in software today is embracing complexity; not treating it as something to be minimized at all costs but a challenge that requires thoughtfulness in processes, practices, and governance. The key question is whether the industry realizes this.
This content was produced by Thoughtworks. It was not written by MIT Technology Review’s editorial staff.