Helping science succeed
Helping science succeed

Predicting the next 10 years: Climate change, AI, and the wealth gap

Arthur C. Clarke, the author of 2001: A Space Odyssey, was one the 20th century’s most brilliant science fiction writers and futurists. Along with fellow writers and futurists like Alvin Toffler (author of Future Shock), Isaac Asimov (I Robot), HG Welles (The Time Machine), George Orwell (1984), Gene Roddenberry (Star Trek), Marshall McLuhan (The Medium is the Message), Ray Bradbury (Fahrenheit 451), and Buckminster Fuller (Operating Manual for Spaceship Earth), Clarke’s work entertained and inspired generations of students, educators, inventors, and policymakers.

But Clarke was well aware of the limits of his predictive abilities. “The future,” he wrote, “is not to be forecast, but created.” The reality is probably somewhere in between. Futurists look at history, trends, politics, current events, technology, and social norms, and from this stew of factors predict where humanity will be in the coming years. Roddenberry’s Star Trek spoke forcefully against plagues like racism and war and offered a future where good can eventually triumph over evil, but only after the world unites in common cause; Orwell spoke of the horrors of a surveillance society, Welles of the disconnect between societies and the technology that rules them, and McLuhan about the gravitational effect communication has on thinking (see also, social media). To the extent predictions like these can help clearly expose our shortsightedness and prejudices, inspire us to action, encourage students to become scientists, help civic and government leaders prepare for the future, and help businesses find new opportunities or help great ideas align, future forecasts are good things.

To the extent, however, we should believe futurists actually know what’s coming next, we need to be mindful of how much stock to put in these forecasts. Future forecasting has for millenia before Clarke and decades after always been big business. In 1400 BC there was Pythia, the priestess of Apollo, answering questions about the future from the Oracle of Delphi in Greece. In ancient Rome, around 200 BC, the Sibylline Books provided prophecy the Romans consulted in times of crisis. In the Western world, religious prophecies dominated futurist thinking in the era between ancient Rome and modern times, from Isaiah, Daniel, and Ezekiel, to John the Baptist, Christ, and Muhammad. Magical prophecies were also very influential during this period, from figures like Merlin, to astrological forecasts, divination (fortune-telling), witchcraft and sorcery (none of these forecasting methods have since disappeared, of course). By the mid-1500s, Nostradamus was busy publishing his predictions about the fate of the world, and by the late 1800s, Jules Verne captivated readers with his futuristic tales of exploration by submarine (20,000 Leagues Under the Sea), hot air balloon (Around the World in Eighty Days), spelunking (A Journey to the Center of the Earth) and spacecraft (Around the Moon).

Verne’s work is generally regarded as the true beginning of science fiction, leading to the “golden era” of science fiction between the 1930s and 1960s. During this period, some of the most culturally significant and socially conscious novels and screenplays of the past century were written, including important science fiction work probing where our technology and social conventions would eventually lead us. Fast forward to today, physicist and futurist Michael Kaku predicts we will soon be able to communicate directly from brain to brain via the Internet, and computer scientist and futurist Ray Kurzweil predicts 3D printing will revolutionize everything from health care (through the widespread availability of printable replacement organs) to what we eat (printed food) to 3D-printed affordable houses. Other common themes—both today and throughout the years—involve combatting (or succumbing to) emerging infectious diseases, developing new sources of energy, inventing new modes of transportation, succumbing to environmental change, and reaching farther out into space.*

Another common theme of all these forecasts is inaccuracy, especially for predictions that extend decades or centuries into the future.  A famous example of just how wrong we can be involves the horse poop apocalypse.** In the late 1800s, the world’s major urban centers were struggling to dispose of increasingly large piles of horse manure. Horses were still the dominant form of transportation, and city streets were piling high with millions of pounds of horse manure every day. Architects in New York began building stoops on all new buildings, elevating front entrances a half-story from street level so they could stay above the mountains of waste and all the flies and rats it attracted. New York city planners predicted that at the current rate of accumulation, city dwellers would be buried several stories deep in horse manure by the 1930s. Something had to be done.

When the world’s first international urban-planning conference was held in New York in 1898, it was dominated by discussion of the manure situation. But the architects, public health officials and social workers who attended were unable to imagine cities without horses—industrialists and innovators were not invited because urban planning at the time was mostly about architecture—so the conference adjourned after just three days. Fortunately, a technological solution to this crisis emerged soon thereafter. Electricity had just started arriving to cities in the late 1800s, and the internal combustion engine was catching on. By the early twentieth century, cars outnumbered horses and electric trolleys replaced horse drawn ones. The manure crisis was averted (albeit, exchanged for the beginning of the climate crisis).

This parable has been told many times with varying aims. Climate change deniers have used it to claim we shouldn’t worry about global warming because technology will come to the rescue; anti-regulation types have used it to suggest that all government policymaking efforts are comically flawed. Here, the point is simply that experts make bad predictions all the time, from the architects, public health officials, and social workers who met in New York to discuss the future of horse waste, to the many business, military and engineering tycoons around the world who never saw a practical use for what the Wright brothers had invented, to politicians who never saw the need for social safety nets, to tech wizards who thought the computer would never amount to more than an electronic recipe box, to techno utopianists who thought the Internet and social media would only lead to global peace and understanding. For challenges as complex, interconnected, and unknowable as predicting the future, we can make educated guesses, but the track record of these guesses isn’t great.

All this said, businesses and governments still rely on forecasts for everything from preparing for population growth and demographic shifts, to managing social and economic trends, to keeping abreast of changing customer demands. There is a real and practical need for forecasting, so forecasters are everywhere, in every business, institution and government agency. At the international level, futurist Jerome Glenn runs the Millenium Project. In their global surveys of scientists and policymakers, Glenn’s group came up with this list of the top 15 challenges facing humanity:

  1. How can sustainable development be achieved for all while addressing global climate change?
  2. How can everyone have sufficient clean water without conflict?
  3. How can population growth and resources be brought into balance?
  4. How can genuine democracy emerge from authoritarian regimes?
  5. How can decisionmaking be enhanced by integrating improved global foresight during unprecedented accelerating change?
  6. How can the global convergence of information and communications technologies work for everyone?
  7. How can ethical market economies be encouraged to help reduce the gap between rich and poor?
  8. How can the threat of new and reemerging diseases and immune micro-organisms be reduced?
  9. How can education make humanity more intelligent, knowledgeable, and wise enough to address its global challenges?
  10. How can shared values and new security strategies reduce ethnic conflicts, terrorism, and the use of weapons of mass destruction?
  11. How can the changing status of women help improve the human condition?
  12. How can transnational organized crime networks be stopped from becoming more powerful and sophisticated global enterprises?
  13. How can growing energy demands be met safely and efficiently?
  14. How can scientific and technological breakthroughs be accelerated to improve the human condition?
  15. How can ethical considerations become more routinely incorporated into global decisions?

In their annual reports to the international community on these questions, the project reports where we are winning and where we are losing.

Where we are winning Where we are losing or there is no progress
GNP per capita Freedom
Poverty Wars and armed conflicts
Women in national parliaments Biocapacity per capita
Life expectancy CO2
School enrollment Renewable fresh water resources
Literacy rate Forest area
Electricity from renewables Physicians per capita
Energy efficiency Foreign direct investment
Access to drinking water Unemployment
Health expenditure per capita Income inequality
Undernourishment Terrorism
Infant mortality rate Population growth
Patent applications Public sector institutional stability
R&D expenditures  
Internet users  


These future forecasts aren’t as sexy as Merlin, Clarke, or Jules Verne, but they may ultimately be more helpful for policy planners than thinking about spaceships and brain implants, at least in the here and now. Still, it’s all pretty broad—a list like this covers a lot more ground than any single government agency administrator might be able to grasp. And what does a forecast this broad even mean unless you’re planning at the IGO level? In fact, even at this level, sweeping efforts like the United Nations’ seventeen Sustainable Development Goals (SDGs), cover everything from poverty to water to education, gender equality and peace. If a government or industry leader is looking for clear guidance about what will happen in the next 10 years, where do they start? IGOs, NGOs and governments try to align their various programs with SDGs, but this is more tactical alignment than strategic. Where is the strategic big picture?

Let’s say we wanted to identify just the super most important “Big 3 issues” today and figure out how to align our plans with these issues. To identify these Big 3, we might want to use these rules:

  1. Follow the needs. Need (or at least, opportunity) has always been the mother (or at least the midwife) of invention. We desperately need lots of things, but no one—at least not yet—has desperately needed a home on Mars. So I’m scratching space travel off my hot list (which is painful as a lifelong Star Trek fan). Ditto for the metaverse and brain-to-brain communication stuff. We may get there, but there’s no fire burning (yet) for this kind of widespread change in the next 10 years (there will be extremely valuable pockets of development of this kind, though).
  2. Follow the crises. With some issues, we can clearly and calmly see the need for solutions. With other issues, especially full-blown crises, we have become so accustomed to dismissing solutions as being inadequate that we forget we need to solve these issues, and quickly. The climate change debate is a perfect example of this.
  3. Don’t follow the money, opportunity, science, social trends, etc. There’s no harm in this strategy, of course, but the path is a lot more uncertain. Just because there’s money in an issue (like online gaming, or building elevated stoops to avoid piles of horse poop) doesn’t make it an accurate vector for prognostication. And just because science has discovered something new doesn’t mean this discovery will ever find a practical application in the marketplace.
  4. Don’t look out too far. If we do this, we’re really just throwing darts blindfolded. There are too many other changes and factors that will confound our predictions.

If we take this approach—if we look just at the low hanging fruit at the moment and guess how this might ripen over the next 10 years—you can make a pretty reasonable guess for what our future harvest will look like. Here, then, are the “Big 3 Ripest” issues to watch between now and 2033:

  1. Climate change: How populations and governments around the world respond to our changing climate is going to be the lead story of the next 50 years bar none. Four climate change related challenges jump out as being the most salient: Mass migration, climate adaptation, carbon dioxide removal, and desalination. Obviously, there are many other challenges ahead like green energy development, dealing with growing climate disasters, and protecting our food supplies, but migration, adaptation, CO2 removal and finding new water supplies are the four challenges that are most crisis-like in nature. We will plan for green energy, climate disasters and food supplies, but the extreme responses and extreme measures may be where our policies end up focusing because we’ve simply kicked the can for too long on taking meaningful action.
    • Mass migration: What happens when people can no longer buy home insurance to live in Florida, Texas, and other parts of the southeast US? Will we start seeing a mass exodus north? What happens when millions of people flee flooding coastal regions of India, or when the Middle East becomes completely unlivable due to heat? Will entire cities or countries move? If this crisis response unfolds, the middle of this century could witness the largest wave of human migration in history, involving massive costs and impacts of every socioeconomic kind imaginable, including famine, energy, and border crises (with regard to energy alone, who stays behind to operate Saudi oil refineries when average daily summer temperatures in the desert already top 130 degrees?).
    • Adaptation: Suppose it’s simply impractical for entire cities, states and countries to relocate and rebuild. What then? Expect to see huge investments in adaptation measures like seawalls to protect populations from rising oceans. Other adaption measures might include lifestyle changes—reducing outdoor activities, becoming nocturnal societies (which, curiously, might end up having business benefits for the eastern hemisphere since their business hours would become more closely aligned with the western hemisphere), more air conditioning (including—where these can afford to be built—giant air conditioned arenas so that daytime life can continue), increased desalination and flood control infrastructure (see desalination, below), and small-scale terraforming initiatives like spraying aerosols into the upper atmosphere to reflect sunlight.
    • Atmospheric CO2 removal: Forests are burning, glaciers and permafrost regions of the planet are melting, and deep-sea methane pockets are bubbling. All this added greenhouse gas is pushing global temperatures higher and faster that even our most dire climate change predictions, so our atmospheric CO2 removal efforts may need to ramp up in a big way in coming decade (we may even talk about removing excess CO2 from the oceans). Do we know what we’re doing, though? CO2 extraction models aren’t well developed, and there is neither international scientific nor political consensus about how or even whether to proceed, to say nothing of the finer policy points like the ethics of decarbonization or the management models. For example, what exactly is our “ideal” climate and who should get to decide? The people who pay for the equipment or all nations voting together? Who should get to decide whether Greenland melts or stays frozen? Who owns land masses like Antarctica? We’re going to have to debate these issues and many others at the same time we’re trying to transform our climate. And how does all this terraforming hardware get paid for and built? The effort to remove CO2 from our atmosphere may end up being the most important challenge in the history of mankind—working together across nations to save our planet in the nick of time. Are we up for it?
    • Desalination: We’re running out of water. Where are our desalination plants? There are currently all kinds of problems with cost, ROI, energy use, waste disposal, and land-locked access hurdles (see Nevada). But as our aquifers disappear and our rivers evaporate, we’re going to need more fresh water to sustain people, cattle, and agriculture, or all these entities will need to relocate. At present, about 20,000 desalination plants are operating worldwide—mostly in the Middle East, accounting for most of that region’s drinking water. An added logic to desalination might be that as oceans rise, wide-scale desalination can balance some or even all of the ocean rise from melting icecaps while at the same time replenishing our aquifers, lakes and rivers. Expect a lot more attention to this technology in the coming years, especially in areas like the southwest US that are facing severe drought and have the money to build these new systems. Also expect a lot more attention and investment on how to manage the brine left over from the desalination process (particularly how to extract more value from this potentially lucrative residue).
  2. The coming AI wave: We are on the cusp of being inundated by tools and services that will revolutionize many aspects of our lives. Unlike our transition from paper to digital, which took decades to play out (and is still playing out in many sectors like government and medicine), this one will be instant because its an add-on to our existing digital infrastructure and because we’ve grown accustomed to rapid tech evolution. Many of these coming changes will be subtle, like using AI to more efficiently track down financial fraud; other changes will be more visible, like widespread impacts on work and creativity, education, copyright, disinformation, and international relations, medicine and transportation.
    • Work and creativity changes: The million dollar question haunting government policymakers right now is what impact will AI have on jobs? What jobs are at risk, what new jobs will be created, what new goods and services will appear in the economy and what current goods and services will disappear? Computer coding will become much more automated (this change is already happening), for instance, but code checkers and prompters will still be needed. What will happen to the legal and medical professions as chatbot powered virtual doctors and lawyers become more common (this change is already happening in medicine—see below)? Will we need as many architects and engineers? How about artists, musicians and authors, who will soon be licensing their work and likenesses so that AI can automatically generate music using their voice, material, and style? How will all this change the creative process? Will the world end up needing fewer creative and technical professionals, or will the opposite happen—will more people be able to participate in and make money from a wide variety of work activities by simply being “prompters” who ask questions and let AI do the heavy lifting? Will we see a drop-off in all these professions, or will it simply be the case that workers will be freed to focus on higher-value aspects of their work, kind of like replacing the slide rule with the calculator or the typewriter with the computer?
    • Education: AI is poised to fundamentally change education, and teachers are already bracing for the impact. But what will be the long-term impacts? No one laments the demise of microfiche and card catalogs in libraries—change happens—and it would be hard to imagine teaching information literacy to students today without using Google. Similarly, no one laments how computer word processing systems completely replaced typewriters, white-out, and manual cut-and-paste editing between about 1985 and 1995. But card catalogs, Google, and word processing systems are all on a continuum of information seeking and translating behaviors that have humans at the center. With AI, no human work is required apart from asking a chat bot to write an essay. How will this capability—this ability to ask an oracle for the answer—affect our ability to think? Will it free us to focus on higher value thinking tasks so we don’t spend as much time looking for answers and writing this information down? Will it break down participation and achievement barriers for students who have been marginalized due to language, behavioral, developmental, or other reasons? Will we look down on Google searches and word processing 30 years from now as being a giant waste of time compared to actually learning more and reading more (not much different than how we now look down on teaching cursive writing in the 1970s)? Expect lots of questions, protests, handwringing, and innovation in education in the next 10 years, particularly with regard to how we teach research, writing and information literacy.
    • Research: In research, AI might end up being the goose that lays the golden eggs. At present, most of our attention has been focused on making published research more open and accessible so that both researchers and the computers that scour the internet for information can find needed answers. But AI has already demonstrated a keen ability to do this kind of work with non-scientific information. Why not train its antenna on science as well, enabling it to automatically generate short summaries for sharing, find connections between work, and even search for answers? Much more training will be needed first—these systems will need to be exposed to a lot more research work before their interpretations can be trusted. Most of this work is paywalled as well—meaning that a basic internet search will only turn up abstracts and metadata, not the full text of journal articles and datasets. But focusing on improving the ability of AI to help ingest and understand research (meaning, at minimum, focusing on making more research free to access at some level) could end up having a huge impact on how research is designed, conducted, and funded, and ultimately on what researchers discover. Expect pockets of development on this front over the next 10 years—field-by-field, publisher-by-publisher. Also expect some retrenchment to happen with open science since there’s profit to be made here. Publishers won’t be in any hurry to make their paywalled information free to access if they can charge for AI mining.
    • Copyright: Amidst all this disruption, legal challenges will abound. Will actors have any recourse when it comes to allowing their likenesses to be generated without permission? Will content creators like researchers, writers and designers have any recourse when it comes to having their work probed and mimicked by ChatGPT when users type in a prompt? And can any of these materials created by this automated process be copyrighted? Legal fights—or at minimum, heated debates—will erupt the intersection of what society needs versus what content creators deserve. While the courts will likely protect the rights of information creators, they may be less likely to agree that publishers have a right to keep research facts hidden from public view, so if research publishers try to profit from information hoarding, they are likely to end up on the losing end of this fight.
    • Disinformation explosion: Disinformation in society is already a major problem, as we’ve written about here, here, here and here. Truth is breaking into multiple realities like a shattered mirror. What’s next? The explosive growth of AI is going to complicate this picture—deepfake videos, fake news shows (think Fox, but with AI-generated content and personalities), and massive new streams of fake web content, all used by bad actors to make money, confuse public opinion, destabilize governments, and generate support for hate and fascist leaders. Too much? Russia has already been executing this playbook for years, and that’s without the benefit of AI. In this sector, look for the emergence of private-public partnerships to combat disinformation and create stronger foundations for truth in society. Even this effort, though, will be fighting a four-alarm fire with a garden hose. Therefore, also expect government oversight and regulation into what AI is allowed to do, how it is allowed to work, and even who is allowed to use it.
    • A renewed Cold War: Russia, China, North Korea, and Iran have been saber-rattling for years, but what happens when this talk turns to action—especially when high tech is involved, like North Korea giving munitions to Russia in exchange satellite technology, or China using new AI technology to enhance domestic surveillance and repression? Can technology be cordoned off, and to the extent we try to do this, how does this effort work against the open nature of science? Will science become increasingly walled off? Here, expect a Cold War pall to increasingly settle over science, with more grad students from China barred from American colleges, open science programs coming under government scrutiny (even regulation), tech embargoes escalating, chip manufacturing facilities relocating to the US, and more. All of this is already happening to some degree—expect more of it in the coming years. The question is what impact this change will have on both science and international relations since science has, in the moder era, been both a massively international undertaking and a major source of stability and back-channel diplomacy.
    • AI medical services: The pandemic spurred major advances in the growth of telemedicine, and surveys since then have shown good receptivity and good outcomes. What’s next? How much of medicine—consultation, diagnoses, surgery, treatment, monitoring—can be entirely taken over by bots? Can we improve access and outcomes by continuing to beef up our telemedicine infrastructure? Can we make more inroads into more neglected needs like mental health care, elder care, and rural care? Of additional note will be the collision between telemedicine and the surveillance state. To what degree can states that are erecting barriers between women and their doctors also monitor telemedicine visits if care is being provided from states where such care is legal? Look for lots of action here in the coming years—legal, technology, privacy rights, patient rights, and more.
    • AI transport services: Work is already underway for AI drivers for buses and long-haul freight vehicles. Expect a lot more development in this sector because the technology is here and the potential payoff is real, assuming all the safety kinks can be worked out soon. Indeed, as the cost of electric vehicles drops, it may even be possible to think in terms of AI-powered self-driving electric car fleets that can move people around at lower cost and greater convenience, eventually supplementing crowded bus and train fleets (even replacing them entirely if vehicle costs drop) or competing with car ownership (why own when you can quickly summon a robot car to your door?). Don’t expect to see any movement here right away though, at least until microcars can be produced and deployed at scale for a price that can’t be refused, and also after charging capacity becomes more ubiquitous. Once these breakthroughs happen, though, it’s going to be game on for transit revolution—maybe not 10 years from now, but certainly 20 (because it takes time for civic planners to debate, decide, plan, test, build, prove and replicate; a major transportation revolution that starts today might take decades to have widespread adoption and impact).
  3. The wealth gap: The global incidence of extreme poverty has been on the decline for the last 50 years due in large part to the dedicated coordination work of UN agencies, particularly the World Bank (and huge doses of foreign aid from G20 countries, particularly the US—which, as the world’s largest economy, gives the most to the budget of UN agencies and to foreign aid; extreme poverty rates did tick up during the pandemic but this is hopefully just temporary). At the same time, however, the consolidation of wealth in the world continues to climb. In the US, the top 10% of income earners now hold 72% of all wealth (up from 63% in 1989). In normal times, we might wish these top 10% well and hope we will also reach this bracket someday. But some vital resources in our economy, like housing and education, have twisted completely out of alignment with social needs, and are increasingly available only to these very wealthy. The impacts of this misalignment will be increasingly felt over the next 10 years as we reckon with fallout like:
    • Home prices: Who can afford to buy a home nowadays? The average price of a home is up 30% in the last three years, now about $408,000 nationwide (with huge variations by state and city, of course). As a result, when people move, they are moving farther away than ever before—about 50 miles on average (compared to 10-15 miles over the last 30 years), according to the National Association of Realtors. About a quarter of buyers are moving almost 500 miles away. Increasingly, only the well-off can choose where to live. Everyone else, including teachers, services personnel, and young families just starting out, needs to live where they can afford. The socioeconomic implications of this reality are only beginning to become apparent: longer commutes, more congested freeways, and more burning of fossil fuels; accelerating wealth gaps since only the wealthy can invest in homes; and more people living on the streets (and the many challenges that come with this situation, both for individuals and for communities). Something will need to give, and soon. Maybe deserted downtown skyscrapers (deserted due to remote work vacancies) will start getting converted en masse into affordable housing units (this is already happening in places); maybe cities will claim tracts of needed land through eminent domain (to allow more high density housing to be built); maybe loosening zoning laws will be part of the solution. Not all this change is negative. Apartments require less energy to heat and cool than homes, for example, and more urban density also means more efficient (and less polluting) transportation systems. While one might also hope that the prospect of young professionals moving from expensive liberal states to more affordable conservative states might help make the country more purple, this shift is probably being offset by people who are moving based on their political ideology. As a result, red states today are becoming redder and blue states bluer.
    • College prices: The cost of a college education continues to climb in the US, now averaging about $33,000 a year at private colleges (after financial aid) and $19,000 at public institutions. Americans now owe almost as much on student loans as they do on credit cards and car loans combined—over $1.7 trillion and counting—and unlike car loans, it’s likely that most of this balance will be defaulted on. This ticking time bomb aside (which is no small aside, but by comparison, the 2008 housing crash cost the US economy $6 trillion), the prospect of taking on forever loans to finance a college has forced students and families to begin rethinking the value of higher education (at least in the US; this is not a global problem since most countries almost completely subsidize the cost of college). College enrollment rates in the US peaked in 2010 but have been declining every year since. In response, colleges have been cutting back to focus on high demand programs like engineering and computer science. What does lower college enrollment mean for the future of the world’s leading economy and research power? What does a loss of liberal arts training mean for the future of American democracy? The answer may not be good. Unless America can find a way to lower the cost of college, we may quickly decline in several measures—research power, a high-skilled workforce, and a citizenry capable of reasoning its way out of a paper bag. College doesn’t need to be for everyone, but the socioeconomic model we’re familiar with in the modern era is one where education is a cornerstone that powers liberal societies and tech-fueled prosperity. It’s too soon to tell whether this change is just an adjustment or a permanent retrenchment, or whether other developments (like AI?) will be able to help close the gap.

There are, of course, many other predictions worth noting—just about as many as there are stars in the sky: expert, inspirational, spiritual, or apocryphal; grounded in data, trends, deep expertise, business hunches, or snake oil. And while these short term predications listed above might be sure bets, they may also end up being completely wrong if something happens tomorrow that changes everything—new energy discoveries, massive environmental or econmomic changes, or war. Here’s hoping the coming years will be more positive than negative. In the meantime, take all predications about the future with a giant grain of salt, and watch out for the horse poop.


* Apologies to sci-fi fans for the too-brief overview. Lots of details have obviously been glossed over in this one-paragraph history.

** The horse poop apocalypse story has been copied with permission from Considering evidence-based approaches to open policy (OSI Global).


Additional reading:

Glenn Hampson

Glenn is Executive Director of the Science Communication Institute and Program Director for SCI’s global Open Scholarship Initiative. You can reach him at [email protected].