June 23, 2025 • 23 mins
OpenAI to Launch GPT-5 This Summer, Explores New Monetization Strategies
Midjourney launches its first AI video generation model, V1
AWS Graviton4 and Trainium3 Chips Aim to Lead AI Infrastructure Market
Amazon Workforce Reduction Due to AI Adoption and Job Role Transformation
Google Launches Gemini 2.5 Pro and Flash-Lite Models for AI Development
The Gentle Singularity by Sam Altman
#AI, #OpenAI, #Midjourney, #AWS, #Amazon, #Google, #SamAltman
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Welcome to Innovation Pulse, your quick no-nonsense update on the latest in AI.
(00:09):
First, we will cover the latest news.
Open AI's GPT-5 is set to launch, Mid Journey introduces AI video generation, Amazon boosts
AI infrastructure, and Google expands its Gemini AI model family.
After this, we'll dive deep into Sam Altman's vision of a gentle singularity and its transformative
(00:31):
promise for our future.
Stay tuned.
Open AI CEO Sam Altman announced that GPT-5 is expected to launch this summer, offering
significant improvements over GPT-4.
Though a specific release date wasn't disclosed, early testers describe GPT-5 as a major upgrade.
(00:51):
Open AI's revenue heavily relies on enterprise customers purchasing enhanced versions of
chat GPT, and GPT-5 aims to boost this trend.
Altman mentioned the possible introduction of ads on chat GPT, noting the importance
of maintaining user trust by not altering the model's output based on ad payments.
(01:12):
Instead, ads might appear externally, such as inside bars.
Open AI is also facing legal challenges, including a court order to retain all chat GPT output
log data amid a copyright case with the New York Times.
Altman criticized the Times' request, emphasizing the importance of privacy and AI use.
(01:37):
Mid Journey, a leading AI image generation startup, has launched its AI video generation
model, V1.
This model allows users to upload an image to generate four or five second videos.
Available through Discord, V1 competes with models from Open AI, Runway, Adobe, and Google.
(01:59):
Mid Journey aims to create AI models for real-time simulations, extending beyond simple video
generation.
The launch follows a lawsuit by Disney and Universal, alleging copyright infringements
on their characters.
Mid Journey faces criticism over the potential impact of AI tools on creative industries.
(02:21):
The service's pricing starts at $10 per month, with higher tiers offering unlimited video
generation in slower modes.
Users can control video outputs with custom settings, extending video length up to 21
seconds.
Initial demos of V1 show its unique style, though its competitive standing remains uncertain.
(02:44):
For now, let's focus on AWS's AI infrastructure advancements.
Amazon Web Services is updating its Graviton 4 chip to feature 600 gigabits per second
of network bandwidth, which they claim is the highest in the public cloud.
AWS's Graviton 4, developed by Anapurna Labs in Texas, is central to Amazon's strategy
(03:07):
to challenge established chip makers like Intel, AMD, and NVIDIA.
At the AWS RE Invent 2024 conference, they unveiled Project Rainier, an AI supercomputer
for Anthropik, and invested $8 billion in the startup.
AWS aims to reduce AI training costs by offering an alternative to NVIDIA's expensive GPUs.
(03:34):
They're Trainiom 2 chips power Project Rainier, with over half a million in use.
While NVIDIA's Blackwell outperforms Trainiom 2, AWS claims better cost performance.
The upcoming Trainiom 3 will double performance and save 50% more energy.
AWS's growing ambition in AI infrastructure raises questions about its potential market
(03:58):
share against NVIDIA.
Amazon CEO Andy Jassy announced plans to reduce the corporate workforce as the company integrates
more generative AI tools.
Jassy stated that AI will lead to fewer traditional roles but create new job types.
Employees are encouraged to learn AI tools to enhance productivity with smaller teams.
(04:24):
Since 2022, Amazon has laid off over 27,000 employees and continued cuts this year, including
200 in North America stores and 100 in the Devices Unit.
As of March, Amazon employed 1,560,000 globally, including temporary warehouse staff and contractors.
(04:46):
Generative AI is used widely, including in inventory and warehouse management.
Similar shifts are seen in other companies like Shopify and Klarna due to AI.
Jassy emphasized AI's role in saving costs and transforming industries, noting its rapid
advancement as a significant technological shift.
(05:09):
Google is expanding its Gemini AI model family with the highly anticipated release of Gemini
2.5 Pro, now available for developers to build on.
After months of testing, this model is leaving preview, showing significant improvements over
previous versions.
Google also introduces the cost-effective Gemini 2.5 Pro flashlight, currently in preview,
(05:35):
which allows developers to manage high-volume AI tasks at a lower cost.
Though flashlight is less capable than 2.5 Flash and unlikely to reach the general app,
it offers a budget-friendly option for specific workloads.
Both models are now integrated into Google Search, using the most suitable model for
each query.
(05:56):
Gemini 2.5 Pro and Flash models are now stable in Google AI Studio and Vertex AI.
Free users have limited access, while paying users enjoy higher access levels.
These updates position Google competitively against OpenAI's GPT models.
(06:17):
And now, pivot our discussion towards the main AI topic.
Today we're going to explore one of the most fascinating and potentially transformative
periods in human history by diving deep into Sam Altman's recent blog post titled The
(06:38):
Gentle Singularity.
As the CEO of OpenAI, Altman offers a unique insider's perspective on how artificial
intelligence is already fundamentally changing our world, his timeline for even more dramatic
developments ahead and what life might look like as we transition towards superintelligence.
(07:00):
His concept of the Gentle Singularity suggests we're not heading towards a sudden, shocking
transformation, but rather experiencing a gradual yet profound shift that's already
underway.
We'll examine Altman's arguments about how current AI systems are already outperforming
humans in many domains, explore his specific predictions for the next few years, including
(07:25):
autonomous agents and breakthrough scientific discoveries, and consider his vision for how
society might adapt to a world where intelligence and energy become abundant resources.
Thanks for that comprehensive introduction, Alex.
I'm excited to dive into Sam Altman's thinking here because I believe his perspective from
leading OpenAI gives us unique insights into something truly unprecedented that we're
(07:48):
living through even if it doesn't always feel that way day to day.
Could you start with your first question?
In his blog post, Altman mentioned we're past the event horizon and the takeoff has
started.
Can you explain what he means by that?
When I say we're past the event horizon, I'm referring to the point where the development
of artificial superintelligence has become inevitable.
(08:09):
We've crossed a threshold where the momentum is now unstoppable.
The takeoff has started means we're already in the early phases of exponential AI development,
even though it might not feel as dramatic as science fiction led us to expect.
We don't see robots walking the streets or everyone talking to AI constantly.
People still die of diseases, space travel remains difficult, and many mysteries about
(08:32):
the universe persist.
However, despite the seemingly normal surface, we've recently built systems that genuinely
surpass human capabilities in numerous domains and significantly amplify human productivity
for those who use them.
The hardest scientific insights that brought us systems like GPT-4 and O3 are behind us
(08:54):
now, and these breakthroughs will carry us much further than most people realize.
It's like we've already lit the fuse.
The explosion is coming, but it's happening in a more gradual, manageable way than the
dramatic singularity scenarios often depicted.
That's a fascinating perspective on our current moment.
You also mentioned that chat GPT is already more powerful than any human who has ever
(09:18):
lived.
How should we understand that claim?
This might sound like hyperbole, but consider the scale and impact.
Hundreds of millions of people rely on chat GPT every single day for increasingly important
tasks, from writing and coding to research and problem solving.
When you have that level of reach and utility, even a small new capability can create enormously
(09:41):
positive impacts across society.
Think about it.
No individual human in history has ever been able to simultaneously assist hundreds of
millions of people with complex cognitive tasks.
But this scale also means the stakes are incredibly high.
A small misalignment in the system, when multiplied across hundreds of millions of users, can
(10:04):
cause tremendous negative impact.
This is why the power comparison makes sense.
It's not just about raw intelligence, but about the breadth of influence and the potential
for both positive and negative effects at a scale no single human has ever achieved.
The responsibility that comes with building and deploying these systems is correspondingly
(10:25):
massive.
That scale is mind-boggling.
Looking ahead, you have some specific predictions for the coming years.
What should we expect in 2025, 2026, and 2027?
The timeline is quite aggressive, but I believe realistic based on current progress.
2025 has already brought us agents that can perform real cognitive work.
(10:47):
We're seeing AI systems that can write computer code in ways that fundamentally change how
software development works.
This isn't just about code completion anymore.
These systems can understand complex requirements and implement sophisticated solutions.
2026 will likely see the arrival of systems capable of figuring out novel insights, essentially
(11:10):
AI systems that can make genuine scientific discoveries and breakthrough innovations,
rather than just recombining existing knowledge.
Then, 2027 may bring us robots that can perform meaningful tasks in the physical world, bridging
the gap between digital intelligence and real-world manipulation.
Each of these represents a major leap in AI capabilities, and the acceleration between
(11:34):
these milestones shows how quickly things are moving once you're on the exponential
curve.
Those timelines suggest rapid change.
How do you think this will affect human creativity and expertise?
I think we'll see a democratization of creation alongside a continued premium on expertise.
Many more people will be able to create software and art using AI tools, which is genuinely
(11:56):
exciting.
Even someone with a great idea for an app being able to build it themselves, or an artist
exploring mediums they never had the technical skills to approach before.
The world has an enormous appetite for both software and art, so this increased creative
capacity should be welcomed.
However, experts who embrace these new tools will likely remain much more effective than
(12:19):
novices, just as we see in other fields where technology has been democratized.
A professional photographer with access to digital tools and AI enhancement will still
produce superior work compared to someone just starting out with the same technology.
The key is adaptation.
(12:39):
Those who learn to work with AI rather than against it will see dramatic improvements
in their capabilities and output.
You paint a picture of dramatic productivity gains.
What might individual capability look like by 2030?
The transformation in individual productivity by 2030 will be striking.
A single person will be able to accomplish vastly more than they could in 2020, and many
(13:01):
people will figure out how to benefit tremendously from this shift.
We're already seeing scientists report being two or three times more productive than before
AI, and that's just the beginning of this trend.
However, I want to emphasize that in the most important ways, the 2030s may not feel wildly
different from today.
People will still love their families, express creativity, play games, and swim in lakes.
(13:25):
The fundamental human experiences that matter most will remain.
But in still very important ways, the decade will be unlike anything that's come before.
As we discover how far beyond human level intelligence we can push these systems, and
what that means for our capabilities as individuals and as a society.
You mentioned intelligence and energy becoming abundant.
(13:47):
Can you elaborate on what that means for human progress?
Intelligence and energy, ideas and the ability to make ideas happen, have been the fundamental
limiters on human progress throughout history.
In the 2030s, both are going to become wildly abundant, with good governance, abundant intelligence,
and energy, theoretically allow us to have anything else we might want or need.
(14:08):
We're already living with incredible digital intelligence, and after some initial shock,
most people have adapted remarkably well.
This illustrates a pattern I call how the singularity actually unfolds.
Wonders become routine, and then become table stakes.
We quickly go from being amazed that AI can generate a beautiful paragraph to wondering
(14:30):
when it can write a complete novel, or from marveling at medical diagnoses to expecting
AI to develop the actual cures.
This is the general nature of the singularity.
Each breakthrough becomes normalized, creating space for us to imagine and expect even greater
capabilities.
That pattern of escalating expectations is really interesting.
(14:52):
You also talk about recursive self-improvement.
How is that already happening?
We're seeing what I'd call a larval version of recursive self-improvement right now.
The AI tools we've already built are helping us find further scientific insights and create
better AI systems.
While this isn't the same as an AI system completely autonomously updating its own code,
it's a meaningful step in that direction.
(15:15):
Advanced AI is particularly interesting because we can use it to accelerate AI research itself,
potentially doing a decade's worth of research in a year or even a month.
There are also other self-reinforcing loops already in motion.
The economic value creation from AI has started a flywheel of compounding infrastructure buildout
to run increasingly powerful AI systems.
(15:38):
We're not far from robots that can build other robots and data centers that can essentially
build other data centers.
If we make the first million humanoid robots through traditional manufacturing, but then
those robots can operate entire supply chains, mining, refining, transportation, manufacturing,
to build more robots and chip fabrication facilities, the rate of progress becomes fundamentally
(16:03):
different.
That automation cascade is fascinating.
What does this mean for the cost of intelligence itself?
As data center production becomes automated, the cost of intelligence should eventually
converge to near the cost of electricity.
This is a profound shift.
Intelligence becoming essentially a utility, like electricity or water.
(16:25):
To put current costs in perspective, the average chat GPT query uses about 0.34 watt hours
of energy.
Probably what an oven would use in just over one second, or what a high efficiency light
bulb would use in a couple of minutes.
The query also uses about 0.0805 gallons of water, roughly one-fifteenth of a teaspoon.
(16:46):
These numbers help illustrate how efficient these systems already are, and as automation
reduces the cost of the infrastructure itself, we're heading toward what you might call
intelligence too cheap to meter.
This abundance will fundamentally change what's possible for individuals and organizations,
when cognitive work becomes nearly free.
With such rapid change, how do you expect society and employment to adapt?
(17:11):
History suggests we're remarkably adaptable.
The rate of technological progress will keep accelerating, but people have consistently
shown they can adapt to almost anything.
There will definitely be challenging aspects.
Whole classes of jobs will disappear.
But the world will be getting so much richer so quickly, that we'll be able to seriously
consider new policy ideas that were never feasible before.
(17:35):
I don't think we'll adopt a completely new social contract all at once, but when we
look back in a few decades, the gradual changes will have amounted to something transformative.
Just as job markets evolved after the industrial revolution, we'll figure out new things to
do and new things to want, and we'll assimilate these new tools quickly.
A subsistence farmer from a thousand years ago would look at many current jobs and consider
(17:58):
them fake, just games to entertain ourselves, since we have abundant food and unimaginable
luxuries.
I hope people a thousand years from now will look at future jobs the same way, while those
jobs feel incredibly important and satisfying to the people doing them.
Looking further ahead, what kinds of breakthroughs might we see by 2035?
(18:19):
The rate of new discoveries will be immense, and it's genuinely hard to imagine what will
have achieved by 2035.
We might see progressions like solving high-energy physics one year, then beginning serious space
colonization the next, or major material science breakthroughs, followed immediately
by true, high-bandwidth, brain-computer interfaces.
Many people will choose to live their lives much the same way they do today, but some
(18:43):
will probably decide to plug in more directly.
This probably sounds overwhelming when we try to wrap our heads around it from today's
perspective.
But living through it will likely feel impressive, yet manageable.
The singularity happens bit by bit, and the merge happens slowly from a relativistic
perspective.
We're climbing the long arc of exponential technological progress.
(19:05):
It always looks vertical when looking forward and flat when looking backward, but it's actually
one smooth curve.
Think back to 2020 and what it would have sounded like to have something close to AGI
by 2025 versus how the last five years have actually felt to live through.
That's a helpful perspective on managing the psychological impact.
What are the main challenges we need to address?
(19:27):
There are serious challenges alongside these huge upsides we absolutely need to solve the
safety issues, both technically and societally.
The alignment problem is crucial.
We need to robustly guarantee that AGI systems learn and act toward what we collectively
really want over the long term.
Social media feeds are actually a perfect example of misaligned AGI.
(19:49):
The algorithms are incredible at understanding your short term preferences and getting you
to keep scrolling, but they do this by exploiting something in your brain that overrides your
long term preferences.
After solving alignment, we need to focus on making superintelligence cheap, widely available,
and not overly concentrated with any person, company, or country.
(20:12):
Society is resilient, creative, and adapts quickly, but we need to harness collective
will and wisdom.
So we'll make mistakes, and some things will go wrong.
We can learn and adapt quickly if we give users freedom within broad bounds that society
decides on.
The sooner the world starts having conversations about what these bounds are and how we define
(20:32):
collective alignment, the better.
You describe this as building a brain for the world.
What does that mean practically?
We're essentially creating a collective intelligence system that will be extremely personalized
and easy for everyone to use.
In this future, we'll primarily be limited by good ideas rather than by the ability to
execute them.
For a long time, technical people in the startup industry have made fun of idea guys, people
(20:56):
who had concepts but needed teams to build them.
It looks like they're about to have their moment because the gap between having an idea
and being able to implement it is shrinking dramatically.
This brain for the world will be accessible to everyone, highly customizable to individual
needs, incapable of turning thoughts into reality with unprecedented speed and efficiency.
(21:19):
The democratization of capability this represents is profound.
Imagine a world where having a good idea is truly the main barrier to making positive
change rather than needing specialized technical skills, large teams or significant capital
investment.
Finally, how confident are you in this timeline and vision?
I recognize this may sound crazy, but consider that if we had told you in 2020 where we'd
(21:42):
be today with AI capabilities, it probably would have sounded even more outlandish than
our current predictions about 2030.
The path ahead is increasingly well-lit and the dark areas are receding fast.
Most of the fundamental scientific insights are behind us now.
We're building toward intelligence too cheap to meter and that's well within grasp.
(22:06):
The feeling isn't one of uncertainty about whether this will happen, but rather gratitude
for being able to participate in this transformation.
The goal now is to scale smoothly, exponentially and uneventfully through superintelligence,
making this transition as beneficial and manageable as possible for everyone involved.
Yakov, thank you for walking us through this remarkable vision of our technological future.
(22:32):
This has been a fascinating exploration of how we might navigate the gentle singularity
ahead.
Thank you, Alex.
These are the kinds of conversations we need to be having as a society as we move through
this transition together.
(23:08):
and democratized creativity.
Don't forget to like, subscribe and share this episode with your friends and colleagues
so they can also stay updated on the latest news and gain powerful insights.
Stay tuned for more updates.
Welcome to Innovation Pulse, your quick no-nonsense update on the latest in AI.
(00:09):
First, we will cover the latest news.
Open AI's GPT-5 is set to launch, Mid Journey introduces AI video generation, Amazon boosts
AI infrastructure, and Google expands its Gemini AI model family.
After this, we'll dive deep into Sam Altman's vision of a gentle singularity and its transformative
(00:31):
promise for our future.
Stay tuned.
Open AI CEO Sam Altman announced that GPT-5 is expected to launch this summer, offering
significant improvements over GPT-4.
Though a specific release date wasn't disclosed, early testers describe GPT-5 as a major upgrade.
(00:51):
Open AI's revenue heavily relies on enterprise customers purchasing enhanced versions of
chat GPT, and GPT-5 aims to boost this trend.
Altman mentioned the possible introduction of ads on chat GPT, noting the importance
of maintaining user trust by not altering the model's output based on ad payments.
(01:12):
Instead, ads might appear externally, such as inside bars.
Open AI is also facing legal challenges, including a court order to retain all chat GPT output
log data amid a copyright case with the New York Times.
Altman criticized the Times' request, emphasizing the importance of privacy and AI use.
(01:37):
Mid Journey, a leading AI image generation startup, has launched its AI video generation
model, V1.
This model allows users to upload an image to generate four or five second videos.
Available through Discord, V1 competes with models from Open AI, Runway, Adobe, and Google.
(01:59):
Mid Journey aims to create AI models for real-time simulations, extending beyond simple video
generation.
The launch follows a lawsuit by Disney and Universal, alleging copyright infringements
on their characters.
Mid Journey faces criticism over the potential impact of AI tools on creative industries.
(02:21):
The service's pricing starts at $10 per month, with higher tiers offering unlimited video
generation in slower modes.
Users can control video outputs with custom settings, extending video length up to 21
seconds.
Initial demos of V1 show its unique style, though its competitive standing remains uncertain.
(02:44):
For now, let's focus on AWS's AI infrastructure advancements.
Amazon Web Services is updating its Graviton 4 chip to feature 600 gigabits per second
of network bandwidth, which they claim is the highest in the public cloud.
AWS's Graviton 4, developed by Anapurna Labs in Texas, is central to Amazon's strategy
(03:07):
to challenge established chip makers like Intel, AMD, and NVIDIA.
At the AWS RE Invent 2024 conference, they unveiled Project Rainier, an AI supercomputer
for Anthropik, and invested $8 billion in the startup.
AWS aims to reduce AI training costs by offering an alternative to NVIDIA's expensive GPUs.
(03:34):
They're Trainiom 2 chips power Project Rainier, with over half a million in use.
While NVIDIA's Blackwell outperforms Trainiom 2, AWS claims better cost performance.
The upcoming Trainiom 3 will double performance and save 50% more energy.
AWS's growing ambition in AI infrastructure raises questions about its potential market
(03:58):
share against NVIDIA.
Amazon CEO Andy Jassy announced plans to reduce the corporate workforce as the company integrates
more generative AI tools.
Jassy stated that AI will lead to fewer traditional roles but create new job types.
Employees are encouraged to learn AI tools to enhance productivity with smaller teams.
(04:24):
Since 2022, Amazon has laid off over 27,000 employees and continued cuts this year, including
200 in North America stores and 100 in the Devices Unit.
As of March, Amazon employed 1,560,000 globally, including temporary warehouse staff and contractors.
(04:46):
Generative AI is used widely, including in inventory and warehouse management.
Similar shifts are seen in other companies like Shopify and Klarna due to AI.
Jassy emphasized AI's role in saving costs and transforming industries, noting its rapid
advancement as a significant technological shift.
(05:09):
Google is expanding its Gemini AI model family with the highly anticipated release of Gemini
2.5 Pro, now available for developers to build on.
After months of testing, this model is leaving preview, showing significant improvements over
previous versions.
Google also introduces the cost-effective Gemini 2.5 Pro flashlight, currently in preview,
(05:35):
which allows developers to manage high-volume AI tasks at a lower cost.
Though flashlight is less capable than 2.5 Flash and unlikely to reach the general app,
it offers a budget-friendly option for specific workloads.
Both models are now integrated into Google Search, using the most suitable model for
each query.
(05:56):
Gemini 2.5 Pro and Flash models are now stable in Google AI Studio and Vertex AI.
Free users have limited access, while paying users enjoy higher access levels.
These updates position Google competitively against OpenAI's GPT models.
(06:17):
And now, pivot our discussion towards the main AI topic.
Today we're going to explore one of the most fascinating and potentially transformative
periods in human history by diving deep into Sam Altman's recent blog post titled The
(06:38):
Gentle Singularity.
As the CEO of OpenAI, Altman offers a unique insider's perspective on how artificial
intelligence is already fundamentally changing our world, his timeline for even more dramatic
developments ahead and what life might look like as we transition towards superintelligence.
(07:00):
His concept of the Gentle Singularity suggests we're not heading towards a sudden, shocking
transformation, but rather experiencing a gradual yet profound shift that's already
underway.
We'll examine Altman's arguments about how current AI systems are already outperforming
humans in many domains, explore his specific predictions for the next few years, including
(07:25):
autonomous agents and breakthrough scientific discoveries, and consider his vision for how
society might adapt to a world where intelligence and energy become abundant resources.
Thanks for that comprehensive introduction, Alex.
I'm excited to dive into Sam Altman's thinking here because I believe his perspective from
leading OpenAI gives us unique insights into something truly unprecedented that we're
(07:48):
living through even if it doesn't always feel that way day to day.
Could you start with your first question?
In his blog post, Altman mentioned we're past the event horizon and the takeoff has
started.
Can you explain what he means by that?
When I say we're past the event horizon, I'm referring to the point where the development
of artificial superintelligence has become inevitable.
(08:09):
We've crossed a threshold where the momentum is now unstoppable.
The takeoff has started means we're already in the early phases of exponential AI development,
even though it might not feel as dramatic as science fiction led us to expect.
We don't see robots walking the streets or everyone talking to AI constantly.
People still die of diseases, space travel remains difficult, and many mysteries about
(08:32):
the universe persist.
However, despite the seemingly normal surface, we've recently built systems that genuinely
surpass human capabilities in numerous domains and significantly amplify human productivity
for those who use them.
The hardest scientific insights that brought us systems like GPT-4 and O3 are behind us
(08:54):
now, and these breakthroughs will carry us much further than most people realize.
It's like we've already lit the fuse.
The explosion is coming, but it's happening in a more gradual, manageable way than the
dramatic singularity scenarios often depicted.
That's a fascinating perspective on our current moment.
You also mentioned that chat GPT is already more powerful than any human who has ever
(09:18):
lived.
How should we understand that claim?
This might sound like hyperbole, but consider the scale and impact.
Hundreds of millions of people rely on chat GPT every single day for increasingly important
tasks, from writing and coding to research and problem solving.
When you have that level of reach and utility, even a small new capability can create enormously
(09:41):
positive impacts across society.
Think about it.
No individual human in history has ever been able to simultaneously assist hundreds of
millions of people with complex cognitive tasks.
But this scale also means the stakes are incredibly high.
A small misalignment in the system, when multiplied across hundreds of millions of users, can
(10:04):
cause tremendous negative impact.
This is why the power comparison makes sense.
It's not just about raw intelligence, but about the breadth of influence and the potential
for both positive and negative effects at a scale no single human has ever achieved.
The responsibility that comes with building and deploying these systems is correspondingly
(10:25):
massive.
That scale is mind-boggling.
Looking ahead, you have some specific predictions for the coming years.
What should we expect in 2025, 2026, and 2027?
The timeline is quite aggressive, but I believe realistic based on current progress.
2025 has already brought us agents that can perform real cognitive work.
(10:47):
We're seeing AI systems that can write computer code in ways that fundamentally change how
software development works.
This isn't just about code completion anymore.
These systems can understand complex requirements and implement sophisticated solutions.
2026 will likely see the arrival of systems capable of figuring out novel insights, essentially
(11:10):
AI systems that can make genuine scientific discoveries and breakthrough innovations,
rather than just recombining existing knowledge.
Then, 2027 may bring us robots that can perform meaningful tasks in the physical world, bridging
the gap between digital intelligence and real-world manipulation.
Each of these represents a major leap in AI capabilities, and the acceleration between
(11:34):
these milestones shows how quickly things are moving once you're on the exponential
curve.
Those timelines suggest rapid change.
How do you think this will affect human creativity and expertise?
I think we'll see a democratization of creation alongside a continued premium on expertise.
Many more people will be able to create software and art using AI tools, which is genuinely
(11:56):
exciting.
Even someone with a great idea for an app being able to build it themselves, or an artist
exploring mediums they never had the technical skills to approach before.
The world has an enormous appetite for both software and art, so this increased creative
capacity should be welcomed.
However, experts who embrace these new tools will likely remain much more effective than
(12:19):
novices, just as we see in other fields where technology has been democratized.
A professional photographer with access to digital tools and AI enhancement will still
produce superior work compared to someone just starting out with the same technology.
The key is adaptation.
(12:39):
Those who learn to work with AI rather than against it will see dramatic improvements
in their capabilities and output.
You paint a picture of dramatic productivity gains.
What might individual capability look like by 2030?
The transformation in individual productivity by 2030 will be striking.
A single person will be able to accomplish vastly more than they could in 2020, and many
(13:01):
people will figure out how to benefit tremendously from this shift.
We're already seeing scientists report being two or three times more productive than before
AI, and that's just the beginning of this trend.
However, I want to emphasize that in the most important ways, the 2030s may not feel wildly
different from today.
People will still love their families, express creativity, play games, and swim in lakes.
(13:25):
The fundamental human experiences that matter most will remain.
But in still very important ways, the decade will be unlike anything that's come before.
As we discover how far beyond human level intelligence we can push these systems, and
what that means for our capabilities as individuals and as a society.
You mentioned intelligence and energy becoming abundant.
(13:47):
Can you elaborate on what that means for human progress?
Intelligence and energy, ideas and the ability to make ideas happen, have been the fundamental
limiters on human progress throughout history.
In the 2030s, both are going to become wildly abundant, with good governance, abundant intelligence,
and energy, theoretically allow us to have anything else we might want or need.
(14:08):
We're already living with incredible digital intelligence, and after some initial shock,
most people have adapted remarkably well.
This illustrates a pattern I call how the singularity actually unfolds.
Wonders become routine, and then become table stakes.
We quickly go from being amazed that AI can generate a beautiful paragraph to wondering
(14:30):
when it can write a complete novel, or from marveling at medical diagnoses to expecting
AI to develop the actual cures.
This is the general nature of the singularity.
Each breakthrough becomes normalized, creating space for us to imagine and expect even greater
capabilities.
That pattern of escalating expectations is really interesting.
(14:52):
You also talk about recursive self-improvement.
How is that already happening?
We're seeing what I'd call a larval version of recursive self-improvement right now.
The AI tools we've already built are helping us find further scientific insights and create
better AI systems.
While this isn't the same as an AI system completely autonomously updating its own code,
it's a meaningful step in that direction.
(15:15):
Advanced AI is particularly interesting because we can use it to accelerate AI research itself,
potentially doing a decade's worth of research in a year or even a month.
There are also other self-reinforcing loops already in motion.
The economic value creation from AI has started a flywheel of compounding infrastructure buildout
to run increasingly powerful AI systems.
(15:38):
We're not far from robots that can build other robots and data centers that can essentially
build other data centers.
If we make the first million humanoid robots through traditional manufacturing, but then
those robots can operate entire supply chains, mining, refining, transportation, manufacturing,
to build more robots and chip fabrication facilities, the rate of progress becomes fundamentally
(16:03):
different.
That automation cascade is fascinating.
What does this mean for the cost of intelligence itself?
As data center production becomes automated, the cost of intelligence should eventually
converge to near the cost of electricity.
This is a profound shift.
Intelligence becoming essentially a utility, like electricity or water.
(16:25):
To put current costs in perspective, the average chat GPT query uses about 0.34 watt hours
of energy.
Probably what an oven would use in just over one second, or what a high efficiency light
bulb would use in a couple of minutes.
The query also uses about 0.0805 gallons of water, roughly one-fifteenth of a teaspoon.
(16:46):
These numbers help illustrate how efficient these systems already are, and as automation
reduces the cost of the infrastructure itself, we're heading toward what you might call
intelligence too cheap to meter.
This abundance will fundamentally change what's possible for individuals and organizations,
when cognitive work becomes nearly free.
With such rapid change, how do you expect society and employment to adapt?
(17:11):
History suggests we're remarkably adaptable.
The rate of technological progress will keep accelerating, but people have consistently
shown they can adapt to almost anything.
There will definitely be challenging aspects.
Whole classes of jobs will disappear.
But the world will be getting so much richer so quickly, that we'll be able to seriously
consider new policy ideas that were never feasible before.
(17:35):
I don't think we'll adopt a completely new social contract all at once, but when we
look back in a few decades, the gradual changes will have amounted to something transformative.
Just as job markets evolved after the industrial revolution, we'll figure out new things to
do and new things to want, and we'll assimilate these new tools quickly.
A subsistence farmer from a thousand years ago would look at many current jobs and consider
(17:58):
them fake, just games to entertain ourselves, since we have abundant food and unimaginable
luxuries.
I hope people a thousand years from now will look at future jobs the same way, while those
jobs feel incredibly important and satisfying to the people doing them.
Looking further ahead, what kinds of breakthroughs might we see by 2035?
(18:19):
The rate of new discoveries will be immense, and it's genuinely hard to imagine what will
have achieved by 2035.
We might see progressions like solving high-energy physics one year, then beginning serious space
colonization the next, or major material science breakthroughs, followed immediately
by true, high-bandwidth, brain-computer interfaces.
Many people will choose to live their lives much the same way they do today, but some
(18:43):
will probably decide to plug in more directly.
This probably sounds overwhelming when we try to wrap our heads around it from today's
perspective.
But living through it will likely feel impressive, yet manageable.
The singularity happens bit by bit, and the merge happens slowly from a relativistic
perspective.
We're climbing the long arc of exponential technological progress.
(19:05):
It always looks vertical when looking forward and flat when looking backward, but it's actually
one smooth curve.
Think back to 2020 and what it would have sounded like to have something close to AGI
by 2025 versus how the last five years have actually felt to live through.
That's a helpful perspective on managing the psychological impact.
What are the main challenges we need to address?
(19:27):
There are serious challenges alongside these huge upsides we absolutely need to solve the
safety issues, both technically and societally.
The alignment problem is crucial.
We need to robustly guarantee that AGI systems learn and act toward what we collectively
really want over the long term.
Social media feeds are actually a perfect example of misaligned AGI.
(19:49):
The algorithms are incredible at understanding your short term preferences and getting you
to keep scrolling, but they do this by exploiting something in your brain that overrides your
long term preferences.
After solving alignment, we need to focus on making superintelligence cheap, widely available,
and not overly concentrated with any person, company, or country.
(20:12):
Society is resilient, creative, and adapts quickly, but we need to harness collective
will and wisdom.
So we'll make mistakes, and some things will go wrong.
We can learn and adapt quickly if we give users freedom within broad bounds that society
decides on.
The sooner the world starts having conversations about what these bounds are and how we define
(20:32):
collective alignment, the better.
You describe this as building a brain for the world.
What does that mean practically?
We're essentially creating a collective intelligence system that will be extremely personalized
and easy for everyone to use.
In this future, we'll primarily be limited by good ideas rather than by the ability to
execute them.
For a long time, technical people in the startup industry have made fun of idea guys, people
(20:56):
who had concepts but needed teams to build them.
It looks like they're about to have their moment because the gap between having an idea
and being able to implement it is shrinking dramatically.
This brain for the world will be accessible to everyone, highly customizable to individual
needs, incapable of turning thoughts into reality with unprecedented speed and efficiency.
(21:19):
The democratization of capability this represents is profound.
Imagine a world where having a good idea is truly the main barrier to making positive
change rather than needing specialized technical skills, large teams or significant capital
investment.
Finally, how confident are you in this timeline and vision?
I recognize this may sound crazy, but consider that if we had told you in 2020 where we'd
(21:42):
be today with AI capabilities, it probably would have sounded even more outlandish than
our current predictions about 2030.
The path ahead is increasingly well-lit and the dark areas are receding fast.
Most of the fundamental scientific insights are behind us now.
We're building toward intelligence too cheap to meter and that's well within grasp.
(22:06):
The feeling isn't one of uncertainty about whether this will happen, but rather gratitude
for being able to participate in this transformation.
The goal now is to scale smoothly, exponentially and uneventfully through superintelligence,
making this transition as beneficial and manageable as possible for everyone involved.
Yakov, thank you for walking us through this remarkable vision of our technological future.
(22:32):
This has been a fascinating exploration of how we might navigate the gentle singularity
ahead.
Thank you, Alex.
These are the kinds of conversations we need to be having as a society as we move through
this transition together.
(23:08):
and democratized creativity.
Don't forget to like, subscribe and share this episode with your friends and colleagues
so they can also stay updated on the latest news and gain powerful insights.
Stay tuned for more updates.
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