The Governance Crisis Nobody's Talking About

Welcome to Tomorrow Unveiled.

>> You know, we're sitting at one of those moments in history. It feels like the future isn't just arriving. It's not

even knocking politely.

>> No, it's crashing through the door.

>> It really is. It's demanding our attention and and forcing society to make some huge structural choices right now.

>> Absolutely. And our focus today is on a stack of really fresh research. I mean,

data compiled just this week in early December 2025. And it paints such a

December 2025. And it paints such a complex picture.

>> It really does. It's a world that's simultaneously delivering this this unbelievable technological productivity, but at the same time, it's creating this destabilizing displacement for millions

of people.

>> It's a profound paradox, and that really gets to the heart of what our research team found. They're calling it the age

team found. They're calling it the age of conditional abundance.

>> Conditional abundance. I think that phrase is perfect. It's the core thesis from all the information our dedicated research team pulled together.

>> Exactly. the technology it's not theoretical anymore. AI and especially

theoretical anymore. AI and especially nextg energy storage they are mathematically capable of generating just a vast deflationary wealth.

>> So we've basically solved or are very close to solving some fundamental problems. >> We are the problems of digital creation and sustainable power supply. I mean

this should be leading to true abundance for everyone.

>> But and this is the huge but here is the structural contradiction the deep rub that all this data is showing us. The

windfall is here, but the systems we have to, you know, to catch that windfall, they were built for the industrial age.

>> They're obsolete >> completely. So without intentional

>> completely. So without intentional massive intervention, without fundamentally redesigning our institutional plumbing, all these huge productivity gains are just automatically concentrating wealth.

>> And they're widening that chasm of inequality faster than any single effort like like simple reskilling could ever hope to close on its own. The abundance

is conditional.

>> It's conditional upon our choices, specifically our institutional design choices. We have the technical capacity

choices. We have the technical capacity for prosperity for all. But our current economic framework, well, it ensures that only the already rich or the highly skilled capture the benefit.

>> And that's the monumental challenge for all of us right now. How do we design these institutions? How do we reshape

these institutions? How do we reshape the social contract to make sure this technological abundance leads to widely shared prosperity? and not just

shared prosperity? and not just abundance for a select few.

>> Exactly.

>> So to answer that, our research team really zeroed in on three critical interlin areas that define this moment.

>> Okay. So first we have to look at the tectonic shift in the labor market. This

is where people feel that change most acutely and we're looking at what the research is calling the entrylevel extinction event.

>> It's a powerful term. Second, we're

going to dive into the physical and digital realities that are actually driving this abundance, >> right? the collapse in the cost of

>> right? the collapse in the cost of intelligence turning advanced revenue into what a commodity >> a utility almost and at the same time this dramatic deflation in energy storage which is paving the way for

truly cheap limitless power >> and finally we'll get into the great governance fracture this chaotic global regulatory battle that's pitting sort of Americanstyle innovation acceleration

against a more European style social protection >> and that's a battle for the very soul of AI governance worldwide Let's unpack all this. Let's start with that seismic

this. Let's start with that seismic shift in the workforce. For years, we've heard the predictions about AI's impact, but the data, it's finally crystallizing

into a clear, if uh paradoxical story about this workforce metamorphosis, >> the initial fear was always the singularity.

>> The moment the robot takes every single job, >> the reality is so much more nuanced. But

>> I have to say, no less dramatic. On one

hand, you have these massive widespread productivity gains, all confirmed by our research.

>> This is the 96% figure, right?

>> That's the one. A landmark survey found a staggering 96% of companies reporting significant AI productivity gains.

That's that's virtually every large company in the world seeing a benefit.

>> 96% that's a near universal technological benefit. But the question

technological benefit. But the question is always where does that cash go? Does

it immediately translate into pink slips?

>> That was the fear. But the data on layoffs is I think the first big surprise here.

>> The majority of those companies, 57% are actually reinvesting those productivity gains. They're building internal

gains. They're building internal capability, bolstering cyber security, and crucially upskilling their existing employees.

>> So they're not just cutting costs.

>> No, only 17% of the companies surveyed explicitly use the gains for immediate mass headcount reduction.

>> So that knee-jerk apocalyptic response isn't really happening. Instead of a job elimination event, it's more of a job redefinition event.

>> At least for the majority of the corporate world, yes, companies are prioritizing growth agendas and building new capabilities because they see AI as a way to do more complex things, >> not just the same things with fewer

people.

>> Exactly. And the research team even broke down the investment. It was

something like 42% developed new AI tools, 41% on cyber security, and 38% dedicated resources to upskilling their workforce. That's a huge number on

workforce. That's a huge number on upskilling.

>> It is. And we see a clear correlation.

Companies spending say upwards of $10 million on AI reported even higher productivity gains. It's a compound

productivity gains. It's a compound return.

>> So the successful companies aren't treating AI as a cost cutting tool.

>> No, they're treating it as a new engine for growth that requires strategic human capital. However, we do have to connect

capital. However, we do have to connect that internal corporate optimism with the uh external displacement reality because while these companies are upskilling, the numbers show a

substantial structural shift is already happening in the broader economy.

>> Yeah. The tech layoffs in 2025, they totaled between 126,000 and 183,000 positions.

>> And these layoffs weren't random, were they?

>> Not at all. Our research confirms they were really concentrated in specific routine white collar roles.

>> Things like HR, administration, >> HR, admin, customer support roles that saw major shifts to intelligent AI agents. The jobs being displaced are the

agents. The jobs being displaced are the highly structured, predictable, clerical ones.

>> The ones where the process is the same every single time.

>> Exactly. Which makes them ripe for immediate automation.

>> This brings us to a really terrifying quantitative measure, something our research team is calling the iceberg index. It's a finding from MIT and it

index. It's a finding from MIT and it really quantifies the sheer scale of disruption that's what hiding beneath the surface.

>> The iceberg index is well it's perhaps the most sobering data point we came across because it doesn't look at current adoption. It looks at technical

current adoption. It looks at technical capability.

>> So what's possible right now not what's being done >> precisely. And it suggests that current

>> precisely. And it suggests that current commercially available AI systems are technically capable of performing work equivalent to 11.7% of the entire US labor market.

>> 11.7%. That's that's a huge slice.

That's nearly 1/8 of the total labor force.

>> It represents about $1.2 trillion in wages.

>> A trillion dollars. That's a massive landscape of instability. I mean to put 1.2 trillion into perspective, that's roughly the GDP of a country like Spain >> or Mexico. Yeah, we are talking about

the technical exposure of a major global economy just being upended >> and this is way bigger than previous estimates that just focused on you know manufacturing or truck driving.

>> Much bigger. This confirms the disruption is already far deeper and affects more knowledge workers than just the early stage adoption numbers would suggest. If you picture the labor

suggest. If you picture the labor landscape, 11.7% is now sitting on a very unstable foundation. And it's only a matter of time and corporate implementation before that structure shifts.

>> Yeah.

>> But let's dive into a key paradox here.

One that really challenges that usual AI takes all jobs narrative. Other research

looked at specific jobs, lawyers, software engineers, customer service agents, and found something really counterintuitive.

>> It's fascinating. They found that the lower skilled or least experienced workers often get the greatest initial productivity boost from using AI.

>> The AI acts as a leveling force.

>> It does. is it fills in the knowledge gaps for the novice. I mean, a less experienced worker can use a tool like an LLM to draft a perfect email or summarize a complex legal doc or write

routine code that used to require years of experience.

>> So, the productivity jump for the junior employee is massive.

>> Absolutely.

>> Wait. Okay. If AI makes the least experience the most productive, shouldn't that be a massive hiring boon?

Why are twothirds of organizations slowing down their entry-level hiring?

What are we missing? What we're missing is the institutional response. The goal

of the institution isn't always to maximize human performance.

>> It's to minimize cost and maximize predictability.

>> Exactly. If AI makes an entry-level analyst twice as good, but it can make an AI agent 100 times cheaper.

>> The company chooses the agent.

>> The company chooses the agent. The AI

has automated the tasks that made up the job even if it can boost the performance of the human doing those tasks. So that

potential for AI to reduce wage polarization is strictly conditional.

Then the potential is there, but institutional forces are steering us toward displacement, not augmentation.

At least for the entry level.

>> This is the heart of it. This is the entry-level extinction event or what the research team is calling the broken run crisis.

>> The apprentice layer, the very first step on the career ladder is just dissolving.

>> It is. We've seen these corroborated global trends showing that twothirds, so 66% of global organizations expect to slow entry-level hiring over the next three years.

>> And it's not because they don't need people.

>> No, it's because AI agents are now just excelling at the basic tasks. Data

entry, summarization, routine emails.

That used to be the unavoidable, messy, time-consuming training ground for junior employees. So, if you can't get

junior employees. So, if you can't get your foot in the door doing basic data scrubbing, how do you learn the complex stuff? How do you become a future

stuff? How do you become a future leader?

>> That's the question. We're essentially

optimizing for immediate efficiency by removing the training mechanism for the next generation of leadership.

>> It's a huge strategic risk >> and that risk is already being quantified. 71% of firms are reporting

quantified. 71% of firms are reporting difficulty recruiting future leaders because of this erosion of entry-level pathways.

>> We're building a skyscraper without a ground floor.

>> That's a great way to put it. We're

gutting the ability of the organization to accumulate institutional memory and groom future talent. It's a classic case of sacrificing long-term health for short-term savings.

>> Which means the new labor standard isn't the human or the pure robot, but this centaur, the human augmented by AI, >> right? And this leads to a whole new

>> right? And this leads to a whole new type of employment strategy. Precision

hiring.

>> Precision hiring.

>> Correct. Companies are no longer hiring for mass capacity to execute routine tasks. They are precision hiring for

tasks. They are precision hiring for high leveraged skills people who can orchestrate the AI, not compete with it.

>> So you're hired to frame the problem and validate the AI's output, not to generate the output yourself.

>> Yes. And the market is immediately and aggressively rewarding this shift.

Workers with AI literacy, with prompting skills, with the ability to define the problem for this Centaur team, they command a remarkable 56% wage premium.

>> 56% >> 56%. That's a monumental, almost

>> 56%. That's a monumental, almost instantaneous premium. It just

instantaneous premium. It just underscores how scarce and valuable the ability to wield these tools really is.

>> It proves the future of work isn't about eliminating the human, but augmenting the human.

>> If you have the right skill set, it's the new digital literacy and it's creating a rapid new class divide that our institutions have to address immediately or we risk cementing this

two-tiered economy. And that focus on

two-tiered economy. And that focus on augmentation and precision hiring. It's

accelerating an economic bifurcation that has analysts really worried. Our

employment data shows the structural divide right between the large enterprises and the small businesses that are the true backbone of local economies.

>> The data is stark. Large enterprises are generally adding jobs. They're

integrating AI effectively because they have the capital to invest and absorb the short-term training costs. But small

businesses are shedding jobs, >> a lot of them. They reported a substantial decline of 120,000 positions in November 2025 alone.

>> Why are small businesses the canary in the coal mount here?

>> Well, it's the challenge of capital, talent, and direction. Large firms have the capital for the infrastructure. They

can hire the centaurs with that 56% wage premium, and they can invest in massive reskilling. Small businesses lack all

reskilling. Small businesses lack all three.

>> What are the adoption numbers like for them?

>> It's low. Only 58% of US small businesses utilize any AI tools at all.

They're struggling with capity constraints. They can't afford the high

constraints. They can't afford the high lever talent and they just lack a clear strategic direction for how to adopt AI.

>> So we're creating this two-tiered economy. one where the AI global

economy. one where the AI global enterprises get richer and more productive >> and one where the small foundational businesses the primary way we distribute economic growth and opportunity are

being hollowed out because they just can't afford the tools or the talent to compete.

>> This structural disadvantage for small businesses, it ties right back to our core thesis of conditional abundance.

>> It does. The gains are massive, but the key distribution mechanisms, the small and midsize businesses are collapsing under the weight of this technological shift. The displacement is hitting the

shift. The displacement is hitting the weakest first.

>> That economic stratification is a perfect bridge to our next section. The

small businesses are collapsing because they can't afford the new capital. So,

let's look at the incredible technical reality that's about to make that capital, both power and intelligence, dirt cheap.

>> Precisely. If you're going to run an augmented economy that's built on massive AI models and data centers, you need two things to be effectively free.

Energy and intelligence.

>> And the research confirms that both are collapsing in price at a stunning velocity.

>> Which validates the idea that abundance isn't some utopian concept anymore. It's

a deflationary reality right now.

>> Let's start with the physical world.

>> Yeah, >> the energy singularity.

We have some key data on the crash in lithium ion battery prices. This is

truly transformative news. We've hit a long sought-after inflection point. The

volume weighted average price for lithium ion battery packs fell to $108 per kilowatt hour.

>> $18. And that's significant because the $100 mark was always the magic number, right?

>> It was the critical threshold for electric vehicles to achieve true price par with internal combustion engines, even without subsidies. We're

essentially there.

>> So, the transition to electric transport is now fundamentally an economically superior choice. It decouples the

superior choice. It decouples the consumer decision from just being about environmental motivation, >> which is huge for global consumer markets and supply chains. But the real

game changer for our theme on Tomorrow Unveiled is the crash in stationary storage packs.

>> These are the giant batteries for the grid.

>> Exactly. The ones used to back up grids, stabilize large industrial operations, and store renewable power. And those

prices crashed even further down to a staggering $70 per kilowatt hour.

>> $70. That's a 45% drop from 2024 prices >> is massive.

>> $70 per kilowatt hour for stationary storage. If storage is that cheap, it

storage. If storage is that cheap, it fundamentally solves the central economic and engineering challenge of renewables.

>> Intermittency, >> the intermittency problem. Solar and

wind only work when the sun shines or the wind blows. But if you can store that power at this low cost, you've effectively decoupled energy production from energy consumption time.

>> This is the key to energy abundance. It

means a community micro grid, which used to depend on expensive polluting diesel generators, can now afford massive battery banks. It fundamentally changes

battery banks. It fundamentally changes how cities and utilities plan their power supply.

>> Moving from centralized, rigid power plants to something more decentralized and flexible.

>> And that cheap power is precisely what is needed to fuel the other deflationary engine intelligence.

>> Let's talk about that race to zero in intelligence. The price shock from the

intelligence. The price shock from the major large language model providers this week. It confirms that intelligence

this week. It confirms that intelligence is rapidly becoming a utility approaching commodity status faster than anyone predicted.

>> It was an aggressive API price war and it drove input costs for advanced reasoning to approach 20 cents per million tokens.

>> Okay, let's break that down. A million

tokens is roughly equivalent to the length of Toltoy's war and peace. Mhm.

>> So you're saying the cost of reading war and peace and then asking an AI to generate advanced to complex analysis about it is now 20.

>> It's the price of a single Eminem just 5 years ago that same task would have cost thousands of dollars in human analyst time. Now we're watching the marginal

time. Now we're watching the marginal cost of intellectual output. The ability

to process, analyze, and synthesize data fall to near zero.

>> So intelligence is effectively free.

>> Effectively, yes. And this collapse in marginal cost, it enables massive scale applications that were just economically impossible before.

>> It allows AI to be applied ubiquitously.

For example, our research shows this is already driving huge administrative cost reductions in high friction sectors like healthcare and legal review.

>> I saw numbers like 70 to 80% cost reductions in legal contracting alone.

>> That's right. This is what drives the abundance economy across the entire white collar sector. So, we have cheap, effectively limitless energy on the horizon, and we have intelligence

approaching zero cost. It sounds like pure utopia. The moment conditional

pure utopia. The moment conditional abundance turns into guaranteed prosperity.

>> But there's a massive bottleneck preventing its immediate realization.

>> We've hit a physical constraint, >> a huge one. The speed of the software is clashing violently with the inertia of the hardware and the grid. We're talking

about the physical constraints captured in this concept of the capex valley of death, >> right? The software world, the AI models

>> right? The software world, the AI models and algorithms, they move at the speed of light, following Moore's law.

>> But the physical world, data centers, power grids, cooling infrastructure, it moves at the speed of concrete and copper. We're in a race between Moore's

copper. We're in a race between Moore's law and Ohm's law.

>> And we saw major financial volatility this week because of this mismatch.

Yeah.

>> Reports from companies like Oracle showed delays in completing these critical AI data centers.

>> Timeline slipping from 2027 to 2028.

>> Yeah. And that delay is a direct consequence of a massive unanticipated demand surge hitting an inflexible system. It's caused by supply chain

system. It's caused by supply chain bottlenecks for physical components like high voltage transformers and critically just immediate power availability issues.

>> The AI revolution needs more electricity than the grid was ever designed to handle.

>> Sometimes as much power as a small city for a single data cluster, the bottleneck isn't algorithmic, it's electrical. It's the physical

electrical. It's the physical foundation.

>> This highlights where that institutional design is most needed. The physical

power grid is the primary immovable bottleneck for massive AI deployment.

>> We have to prioritize infrastructure modernization.

Our research points to global strategies like the UK's commitment of 8.9 billion pounds to high voltage upgrades because they recognize that the main obstacle to

the digital future is the physical capacity of their electrical network.

>> The speed of software is a Lamborghini hitting a traffic jam made of rebar and copper. That's it. We have to build a

copper. That's it. We have to build a new highway, the modernized power grid, just to keep the digital speed limit up.

And this requires massive public private investment just to build the foundation for this potential abundance.

>> Before we move on to the great governance fracture and the fight over who sets the rules for this new reality, I just want to take a quick moment.

>> If you're listening on a podcast service like Apple Podcast or Spotify, we'd be incredibly grateful if you could leave us a rating and review for Tomorrow Unveiled. It truly helps us reach new

Unveiled. It truly helps us reach new listeners and keeps this analysis moving forward. Okay, so we've established that

forward. Okay, so we've established that the technology is ready for abundance, but the labor market is fracturing and the physical infrastructure is playing catch-up. The critical factor now

catch-up. The critical factor now becomes governance.

>> Who controls the rules for this technology?

>> And the past week has seen an explosion of regulatory tension, particularly within the United States.

>> The US has seen a major really aggressive shift toward federal preeemption.

>> It has. Our research details the Trump administration's executive order which is designed to consolidate AI regulatory power firmly at the federal level and it explicitly prioritizes innovation speed

over the safety measures enacted by individual states.

>> And this order didn't just suggest new regulations. It used a really potent

regulations. It used a really potent coercive mechanism against the states to force them to comply.

>> That's the most aggressive and potentially unconstitutional part of it.

It explicitly threatens to withhold federal broadband equity, access, and deployment or bead funding from any state whose AI laws are deemed ownorous or conflict with national policy.

>> Let's just pause on that because bead funding is a massive pot of money. We're

talking multibillions of dollars meant for universal internet infrastructure and closing the digital divide.

>> And the federal government is essentially holding a critical infrastructure project hostage. It's

weaponizing infrastructure funding. This

move is a direct shot at states like Colorado and California.

>> Oh, absolutely. They've already tried to fill the regulatory vacuum with their own state laws focused heavily on algorithmic transparency and bias mitigation.

>> The administration's argument is that these state laws stifle innovation, >> right? They argued that and that they

>> right? They argued that and that they could potentially force developers to embed ideological bias into models to achieve statistical parody and that it slows the overall technological

acceleration the US needs to maintain its lead.

>> But the states argue they're just protecting fundamental consumer rights and social stability against these blackbox systems, >> which sets the stage for major constitutional litigation. Legal experts

constitutional litigation. Legal experts are predicting this will land directly in the courts over the limits of federal spending power and the balance between state level police powers and national

economic priorities.

>> So until the courts rule, the regulatory landscape in the US is basically in chaos, >> complete chaos with states and the federal government pulling in opposite

and extremely powerful directions. And

this US chaos stands in stark contrast to the rest of the world leading to what we're calling the three kingdoms of AI governance.

>> Okay, let's start with the European Union which is operating under the precautionary model.

>> The EU is moving very methodically with the implementation of its landmark AI act. Their focus is not speed. It's

act. Their focus is not speed. It's

about establishing a framework of trust.

>> So they prioritize fundamental rights, safety auditability >> process and compliance over rapid deployment. Yes, for a company

deployment. Yes, for a company compliance is difficult but at least the rules are clear.

>> Then you have China which operates the state control model >> and China's approach is fundamentally different rooted in national security and social stability. They recently

tightened controls through amendments to their cyber security law explicitly linking AI governance to state interests and public discourse control. So they're

mandating strict labeling of all AI generated content >> to maintain internet cleanliness and prevent online rumors as they put it.

For China, AI is a tool to be centrally controlled and subservient to the state, meaning innovation is only permitted within state parameters. So we have the US prioritizing deregulation and speed,

the EU prioritizing precaution and process, and China prioritizing state control and stability.

>> Which means a multinational company trying to launch one product globally has to build three entirely different compliance stacks. It drives up costs

compliance stacks. It drives up costs and slows the very innovation the US is trying to accelerate.

>> And this divergence isn't just a commercial headache. The research team

commercial headache. The research team highlights a critical risk here of global inequality.

>> Right? If the cost of reasoning is 20 cents, but you don't have reliable electricity to plug in the computer, that price reduction is totally irrelevant. The UN is warning that this

irrelevant. The UN is warning that this regulatory and infrastructure divergence risks systematically excluding developing nations. They lack power

developing nations. They lack power stability, digital connectivity, and the deep skill bases needed to participate.

So while advanced economies capture these vast deflationary gains from cheap intelligence and power, poorer nations risk the displacement without getting the benefit of the productivity boost.

>> The gap widens based on physical infrastructure, not digital talent. It's

a powerful illustration of conditional abundance. The tech is there, but the

abundance. The tech is there, but the lack of coordinated policy and infrastructure investment acts as a hard barrier to shared prosperity, which deepens global instability. So to

mitigate these deep structural risks, the broken rung, the small business collapse, the geopolitical fracture societies have to adapt their fundamental institutions. And that

fundamental institutions. And that adaptation has to be driven by rapid masscale reskilling.

>> The sheer scale of the need here is just intimidating. Our dedicated research

intimidating. Our dedicated research team identifies that a staggering 60% of employees, that's three out of every five workers globally, will need significant reskilling by 2027. That's

an institutional challenge we probably haven't faced since the rapid industrialization of the 20th century.

>> And the pacing problem is critical. We

need institutions that can move at startup speed. We can't just rely on

startup speed. We can't just rely on employees figuring it out on their own or on traditional four-year universities operating on these slow academic timelines.

>> Are we seeing any promising models emerge?

>> We are early ones, but they show a path forward. California, for instance, has

forward. California, for instance, has partnered directly with major tech giants to build state AI workforces focused on government efficiency.

>> But the pivotal institutions in this fight seem to be community colleges.

>> They are emerging as the rapid response education unit. They're agile, they're

education unit. They're agile, they're localized, and they're focused on immediate labor market needs. They're

providing rapid microcredentiing and targeted work-based learning.

>> Things like six week or 12week intensive courses.

>> Exactly. allowing workers to pivot skills much faster than getting a traditional degree.

>> And that focus on learnability, the capacity to learn how to learn, that's what creates an adaptive workforce, right? One that can evolve every 5 years

right? One that can evolve every 5 years as the technology demands.

>> And what are these institutions teaching? It's not just Python or basic

teaching? It's not just Python or basic coding. We've established technical

coding. We've established technical literacy commands a massive premium, but the true scarcity is in uniquely human abilities. The market is telling us that

abilities. The market is telling us that if a task can be quantified, optimized, and processed, the AI will do it. The

human job is to handle the ambiguity.

>> Precisely. The research is consistent.

The high value skills are the ones machines can't replicate. Critical

thinking, complex negotiation, adaptability, ethical reasoning, and creativity.

>> We need humans who can ask the right questions >> and orchestrate these powerful AI agents, validating their outputs and applying moral judgment to their decisions. The future value of the human

decisions. The future value of the human worker lies in exception handling.

>> This necessity is driving this aggressive AI native education pivot all over the world. Education systems are shifting their focus entirely from just preventing students from cheating with

AI to fostering student agency and symbiosis with AI.

>> We're seeing comprehensive national scale plans. South Korea, for example,

scale plans. South Korea, for example, is investing a massive $1.25 billion to fully infuse AI education from K12 onward. Their goal is to make AI

onward. Their goal is to make AI literacy a core universal subject by 2027.

>> Yes. Ensuring every single citizen can use AI effectively in daily life, not just those in the tech sector. It's a

whole of society approach, treating AI competence as basic infrastructure for the 21st century economy.

>> And El Salvador is demonstrating another model of leaprogging traditional constraints.

>> Right. using a partnership to deploy AI tutors to millions of students nationwide. It suggests AI can be used

nationwide. It suggests AI can be used to dramatically personalize learning even in regions with limited resources.

>> And even in the US, federal commitments are being implemented, providing free access to AI tools for K12 students.

>> The institutional conversation has completely shifted. It's now about how

completely shifted. It's now about how do we use AI to personalize learning, handle administrative loads, and develop deep student agency. Okay, let's talk about the economic foundation that allows people to survive the volatility

of this transition, the social safety net. We've established the economy is

net. We've established the economy is delivering sudden structural shocks, displacing workers without warning. We

need a safety net built for that kind of volatility.

>> And we have concrete corroborated data on reinventing the social safety net through guaranteed income models. This

is perhaps the strongest data point we have on institutional adaptation to conditional abundance.

>> These are the pilot programs in St.

Lewis and Cook County, Illinois.

>> That's right. They provided $500 monthly unconditional income and the results, well, they challenged a lot of political assumptions.

>> So, what were the empirical benefits of providing that liquidity buffer?

>> The data showed two critical things.

First, a high rate of participants, 75% felt more financially secure, which dramatically reduced stress. And second,

>> second, and this is crucial for policy.

94% of the funds were used for consumption smoothing, >> meaning the money wasn't used for luxuries.

>> Not at all. It was used for those low probability, high impact emergency expenses, a car repair, a medical bill, a sudden rent increase. The very things that would otherwise send a volatile

household into a debt spiral and displacement. This validates the idea

displacement. This validates the idea that in an economy defined by volatility liquidity unconditional cash is the most effective safety net.

It provides the financial elasticity that the traditional labor market has stripped away.

>> It allows people to absorb shocks and stay in the workforce rather than falling into the permanent safety net.

>> And the policy implication is clear and is already being enacted. Cook County

voted to make its guaranteed income program permanent, funded by local tax revenue, >> which signals a transition. It's moving

from being a temporary pandemic era experiment to becoming a structural policy fixture, acknowledging that for many, the normal economy is a state of permanent volatility that requires a

permanent liquidity buffer. These

solutions, the rapid reskilling from community colleges, the AI native education pivot in South Korea, the liquid safety nets like in Cook County, these are the blueprints for navigating conditional abundance successfully.

>> They are the human institutions we need to match the speed of the technology.

And before we move to our final synthesis, I think it's a good time to invite our listeners to join the conversation.

>> We love hearing your thoughts. Whether

you're on YouTube or your favorite podcast app, leave a comment and let us know what you think about how tech is changing society and specifically what impact you think the entrylevel extinction will have on the next

generation of leadership.

>> And if you're finding this insightful, please take a moment to like this video, subscribe to Tomorrow Unveiled, and consider hitting that new hype button to help us reach more viewers.

>> And of course, be sure to turn on notifications so you never miss an update on the future.

>> So, let's bring this all together. The

central conclusion from the extensive research compiled this week is that the technological abundance we've been predicting, cheap energy, free intelligence, it's no longer a theoretical projection.

>> It's a measurable physical and digital reality.

>> The economy is learning to produce vastly more with vastly less. The cost

curve of progress has just fallen off a cliff.

>> But the future remains entirely conditional upon institutional design.

The chasm between AI's capacity to create wealth and society's readiness to distribute that wealth is widening. We

see it in the decline of entry-level jobs and the exclusion of small businesses.

>> The battle for the future isn't about stopping technology or slowing the pace of innovation. It's about choosing how

of innovation. It's about choosing how to deploy the gains. We have to choose, as the data shows, to invest massively in resilience,

>> in universal reskilling infrastructures, in fluid liquidity based safety nets, >> and in balancing innovation acceleration with genuine accountability through clear governance structures.

>> The next 18 to 24 months, as the research suggests, this is our critical window to implement these structural changes before the displacement just overwhelms our current rigid safety net.

We have the blueprints from Cook Countyy's guaranteed income model, from South Korea's national education plan, and from our own community college systems. The question is no longer technical.

>> It is purely political and organizational will.

>> And if we successfully deploy cheap intelligence and cheap energy, and if we manage to solve the distribution problem and bridge this growing inequality gap, well, our dedicated research team

suggests that the ultimate challenge facing humanity won't be economic.

>> It'll be existential.

>> Exactly. So here is the final provocative thought. If AI delivers the

provocative thought. If AI delivers the productivity gains predicted, if it makes reduced work hours economically feasible, makes reduced resource consumption possible, and potentially

makes paid work optional for a significant percentage of the population. How will society define

population. How will society define human purpose, status, and fulfillment when paid employment is no longer the central organizing principle of life?

When the machines handle the computation, the creation, and the coordination, and the scarcity of goods vanishes, what is left for the human spirit to strive for? The crisis of purpose will become the next great

frontier.

>> That profound question of purpose is what Tomorrow Unveiled will continue to explore. We'll see you next time on

explore. We'll see you next time on Tomorrow Unveiled.