Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl

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Peter Udo Diehl
Predictions to guide and time tech innovation
May 2024

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I'm excited to share my latest predictions on how AI, robotics, and other technological advancements will reshape
industries in the coming years. The slides explore the exponential growth of computational power, the future of AI and
robotics, and their profound impact on various sectors.
Why this matters:
▪ The success of new products and investments hinges on precise timing and foresight into emerging categories
▪ This deck equips founders, VCs, and industry leaders with insights to align future products with upcoming tech
developments
▪ These insights enhance the ability to forecast industry trends, improve market timing, and predict competitor
actions
Highlights:
▪ Exponential Growth in Compute: How $1000 will soon buy the computational power of a human brain
▪ Scaling of AI Models: The journey towards beyond human-scale models and intelligent edge computing
▪ Transformative Technologies: From advanced robotics and brain interfaces to automated healthcare and beyond
▪ Future of Work: How automation will redefine jobs and economic structures by 2040
With so many predictions presented here, some will inevitably be wrong or mistimed, especially with potential external
disruptions. For instance, a conflict in Taiwan could severely impact global semiconductor production, affecting
compute costs and related advancements. Nonetheless, these slides are intended to guide intuition on future
technological trends.
Peter Udo Diehl – Future Visions 2024

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Processing power is the basis of AI advances, and compute per US Dollar
has been exponentially increasing for over 80 years
Human brain
computation
equivalent
Mouse brain
computation
equivalent
Source: created from historical data and bottom-up estimates

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$1000 buys the computational power of a human brain in 2030, but
memory might be the limiting factor compared to humans
Computation
scaling
Operations per $1000 (order of magnitude)
2025 2030 2040
▪ 1015 OPS / $1000 ▪ 1016 OPS / $1000 ▪ 1018 OPS / $1000
Limiting hardware factor might be memory/DRAM:
• In 2024 DRAM costs $2/GB = 2$/109 byte
• Human neocortex ca. 50 billion neurons * 4k connections per neuron * 1 byte per connection
= 200 TB (2*1014 byte)
• In 2024 this costs ca. $400’000 for DRAM
• Given ca. 7 years for 10x decrease in DRAM cost, it will take until ca. 2042 to get to $1000 cost of human brain-
sized DRAM
▪ Systems scale well, at least up to human level
– Another 10x scale, or 1000x scale above human level will likely have more emergent properties
▪ Calculation of human brain compute:
– Human neocortex ca. 50 billion neurons * 4k connections per neuron * 100 operations per second
– Therefore c.a. 5*1010 * 4*103 * 102 = 2*1016 Ops
▪ Note: our current systems might be more or less efficient than human brains, measured by intelligence/operation,
i.e. human-level intelligence with current approaches might require more compute than human brains
Number of human brains compute capacity per $1000
▪ 0.1 ▪ 1 ▪ 100

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Models will scale beyond human size, simultaneously operations per watt
will continue to fall exponentially, enabling intelligent edge computing
Computational
efficiency
Operations per Watt (especially in AI accelerators)
▪ 10 TeraOPS / Watt ▪ 50 TeraOPS / Watt ▪ 1000 TeraOPS / Watt
▪ The diminishing cost of computation will make it vastly easier possible for many people to deploy powerful models
– For example, complex super-human sensory systems (e.g. vision systems identifying scenes, objects, people
and abstracting from that), will be available on mobile systems 2030
▪ The low power consumption of AI accelerators will enable processing directly at sensors (cameras, microphones,
IMUs), creating intelligent sensors (e.g. cameras recognizing events & actions, IMUs knowing how they are moving,
how they were manipulated and what possible causes could be)
Biological vs. machine computation
▪ Largest neural network models
have more than 5trn parameters
(vs. ca. 80trn in human brains)
▪ $10’000 buys the comp. power of
a human brain
▪ Large neural network models
exceed nr. of parameters of
multiple human brains
▪ A supercomputer has the comp.
power of a city of humans
▪ Large neural network models
exceed nr. of parameters of a
‘village’ of human brains
▪ A supercomputer has more comp.
power than all humans combined
Intelligent edge computing
Computational
power
Neuromorphic chips mature, providing
similar energy efficiency to human brains
Operations per $1000 (order of magnitude)
2025 2030 2040
▪ 1015 OPS / $1000 ▪ 1016 OPS / $1000 ▪ 1018 OPS / $1000

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The number of operations used to train models increase
by ca. 4x per year, i.e. doubling every 6 months
Training compute doubles every 6 months and training data should scale
accordingly, creating a possible bottleneck for further scaling…
Source: https://epochai.org/blog/compute-trends; Training compute optimal large language models Mar. 2022
Will we run out of data soon?
▪ “The stock of high-quality language data is between 4.6e12 and 1.7e13 words”
– Almost all of this is already being used in current models, especially the high-quality data that is crucial
for the later parts of training that determine the final performance
→ The answer is yes, at least for existing language data
Takeaway: performance scaling when training
larger models largely depends on more data
(lighter areas correspond to higher performance)

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…but possible solutions are “hypermodality”, interaction, and self-play
Hypermodal data will lead to hypermodal models:
▪ A large variety of data types, combined into one model, allowing cross-learning
– Visual, video, audio (incl. but not limited to speech)
▫ Ca. 50% of our brains involve visual input processing
– Brain interface data, satellite & weather data, genetic data, financial data, temperature sensors, IMUs, touch,
smell
– Interaction: both digital (internet) and physical (robots)
▫ Self-play/communication
But a possible data prohibition due to lawsuits/regulations might slow down progress:
▪ Lawsuits against OpenAI, Stability, Github
→ This might push back development a few years
Source: Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning, Oct. 2022
This likely leads to a massive increase in “inference”
that is used to synthesize training data
Vision data is still largely untapped but can help
to develop commonsense (e.g. for physics)

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Machines will manage human interactions effortlessly, and the Turing test
will be passed by 2030
AI
tests
and
capabilities
2025 2030 2040
▪ 10min Turing tests passed but
nothing serious due to simplified
conditions (e.g. non-expert tester)
▪ Turing test passed (hours, expert
AI tester)
▪ Feigenbaum test in many areas
passed (medical doctor, chemist,
mathematician, mech. engineer,
artist, software engineer)
▪ Turing test passed (weeks/months
long, all types of testers)
▪ Feigenbaum test passed in all
interesting fields
Turing and Feigenbaum Test
▪ AI agents will be able to surf the
internet and perform many
tasks that a human would be
able to do; starting from
“simple” things like booking a
vacation to more complex tasks
like creating well-researched
pitch-decks for an idea
▪ Coding-assistants are powerful
enough to write larger parts of
functions, outline a code base
structure, but cannot yet build
complex programs on their own
▪ AI assistants will be available in
most fields to answer any
reasonable question and make
complex plans, most of them with
super-human comprehension
▪ Highly capable multi-modal models
and agents are open-source and
can solve a PhD level problem with
a few months of compute on a GPU
cluster
▪ Coding-assists write entire
programs, complex functions,
structure code bases
▪ Ideas and mental work become
“cheap” to the point that any
idea a human can produce is
almost worthless
▪ Personal assistant AIs can run the
(digital) lives of people, their
decision making and planning, and
everyone who does not use them
will fall behind drastically
AI capabilities
The Feigenbaum test evaluates
if a machine can simulate a
field-specific expert Multi-step planning and execution can be
directly trained and will naturally improve
with general model capabilities
By then, the difference in compute
between a powerful AI and a human is
comparable to a human and a mouse

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Robotics foundation models will lead to ChatGPT moment within 2 years,
triggering a wave of further improvements and real-world applications
Source: Metaculus, May 2024
▪ Robotics foundation models are likely appearing in 2024/2025
– They will massively improve dexterity
▪ Visual-language models will enable medium-term planning (i.e. task level)
→ Combining those will yield high dexterity and world understanding, sufficient for cooking
I think the
probability
is ca. 80%
By then, software enables
robots to perform many
household tasks

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$2000 will buy a robot arm with human-comparable object manipulation
skills in 2030
Robotics
Applications
2025 2030 2040
▪ €5000 (arm and software have
limited capability, e.g. grasping
tasks with some planning)
▪ €2000 (software is sufficient for
complex object manipulation and
medium-term planning, e.g. how
to make a meal from ingredients)
▪ €500 (3D printed or automatically
machined, arm and software have
capabilities beyond human skills)
Price of robotic arm (incl. software)
▪ Logistics are capable of being
almost completely automated
(including arbitrary grasping,
opening, and repackaging
packages)
▪ Co-bots with robotics foundation
models are released, that can take
over more jobs in manufacturing,
due to better recognition, planning,
and ease of ‘programming’/
teaching, e.g. by natural language
▪ First household chores, including
cooking, will be performed by
robots
▪ Factories that are built will go
towards full automation (incl.
meat & food processing)
▪ Autonomous cars are working in
most environments
▪ Farming will see another increase
in automation for harvesting and
crop management
▪ Surgical robots proliferate, aided
by advanced surgical planning
software
▪ Skills and mobility of robots in a
wide range of environments are
beyond human level
▪ Carpenting and any type of
crafting can be done by robots
with results better than any human
→ the price of physically
manufactured goods reduces
▪ Household robots that use AIs will
be broadly available and are
beyond human intelligence and
perception (their price will be
mostly dependent on value of the
AI and costly raw materials, i.e.
pretty low)
▪ Construction in developed
countries mostly done by robots
Re-designing existing factories or surgical
theaters will not be worth the cost yet,
therefore the first solutions will build
around human working environments
Robotics foundation
models drastically reduce
the complexity of robot
control, but they are only
used for a few selected
robots at this point The biggest difference in 2040 is
the widespread deployment

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Brain interfaces will enable high data-rate transmission in the 2030s,
but input speeds are limited by adaption rate of our brains…
Brain-interfaces
2025 2030 2040
Writing capabilities
Read-out capabilities
▪ 1000 electrodes recorded could
have 1 bit/s each if the coding is
known, which it is currently not
→ likely less than 100B/s
▪ Probably between 100B/s and
1kB/s but large amounts will not
interpretable or need long training
(from ML and human)
▪ Between 10kB/s and 1MB/s
▪ Enough to reconstruct videos,
audio and thoughts if sensors are
distributed enough
▪ 1000 x 100 spikes/s gives 100k
spikes/s writing
▪ 10% transmission of retinal input
or 100 x 100 video
▪ 1m spikes/s writing
▪ 1% transmission of full retinal
input (given 1000 x 1000 retinal
resolution) or 100 x 100 video
▪ 100m spikes/s writing
▪ Transmission of full retinal input
Available brain interfaces (BI)
▪ Tests with 1000 electrode
implants with up to 100B/s
transfer capability
▪ Tests with 10’000 electrode
implants with approx. 1kB/s
transfer capability
▪ Commercially available 10x 100’000
electrode implants with approx.
100kB/s transfer capability
Source: Cochlear Implantation in Adults with Prelingual Deafness, 2012, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3429129/
100 spikes/s per electrode can encode
12.5 byte/s theoretically but coding is
typically sparse such that
1-4 bits/s per electrode are more realistic
With proper brain pattern recognition,
this already outperforms human
speech and conscious information
rate (at ca. 39-50 bits/s)
Still significantly below
unconscious information
rate at ca. 40 Mbit/s
Limiting factor for writing is likely adaptability of the human brain past adolescence:
• e.g., Cochlear implants work much better when implanted in the first 1-2 years after birth

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…enabling superhuman communication speed that will leave non-users at a
disadvantage
Brain-interfaces
2025 2030 2040
Most applications will be
medical and focus on partially
restoring missing functions
▪ Limited computer
control/video game control
(e.g. writing speed below
keyboard typing, but
directional controls work
well)
▪ Motor cortex interface (e.g.
for wheelchair, robotic arm,
prosthetic limbs,
exoskeleton)
▪ Neurological/psychological
diseases (OCD, PTSD,
obesity)
Human-enhancing applications will come
in the mid 2030s due to required software
setup and brain training on top of the
hardware with high-ish bandwidth
▪ Direct language output interfaces start
to work, possibly matching keyboard
typing speed with the neural interface
▪ An integration with AI assistants where
the neural lace wearer can think about
topics or plans, and the assistant gives
answers or creates a plan → this will be
read-out based as input will still be
limited
▪ First prototypes of visual prosthesis for
the blind work in early stages (but not
comparable to full vision)
▪ Complex and fast control of machines
(e.g. games like Minecraft, possibly
StarCraft; smart homes) possible but
not widely embraced outside existing
medical needs due to required
neurosurgery and still limited upside
Trainings will be available to fine-
tune the readout AI to the user (e.g.
watching videos or thinking about
certain concepts)
▪ Interfaces with consumer devices
significantly surpass human
output speed (typing, speaking,
moving)
▪ Thought and language interface
with AIs that can literally fulfill your
dreams
▪ Enabling telepathy: possibly
fastest way to interact with
humans (via AI translation: human
<--> AI <--> human) but difficult for
adults as capability to adapt to
new types of inputs diminishes
▪ Full VR technologically possible
but the brain would need to learn
how to deal with the new type of
input (high uncertainty about
feasibility)
▪ Non-invasive portable BIs will not significantly go beyond 5 byte/s read-out
speed and writing speed due to the limited precision
▪ fMRI has ca. 0.2-0.5 bit/s/voxel, a voxel size of around 3x3x3mm3, average
brain size of 1300cm3 (ca. 50’000 voxels), i.e. ca. 1.25 - 3.125 kB/s max. rate
▪ Non-invasive mobile fMRI
scanners, combined with EEG
enable a few kB/s maximum read-
out speed

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Healthcare receive a range of new tools, including automated diagnostic
systems, improved drug development, and automated robotic surgery…
▪ Given all sensory input (patient description, video, audio, medical tests), AI
diagnostic systems outperform all humans
▪ Most applications for medical image analysis are at least supported by AIs
but likely a specialized doctor will still confirm the diagnosis
▪ AI medical advisors will take off, especially for less serious conditions since
AI chatbots match human diagnostic skills (especially remote)
– Doctors confer with AI assistants for possible diagnosis and treatments
▪ Drug discovery and simulation of efficacy and safety are drastically faster and
accurate, but clinical trials remain the bottleneck despite AI support
▪ In surgery, robotic arms are becoming widely adopted
– A large part of surgeries is pre-planned using anatomical software,
combined with medical imaging
– Robotic surgeries fill the spectrum from fully-autonomous, to remote
controlled by a surgeon (even via tele-surgery), including hybrid
approaches where parts of the procedure are automated (e.g. suturing)
Primary
care
2030s 2040
▪ Highest level of care is achieved
by machines and AI, including
diagnostics, treatment, and
rehabilitation
▪ Highly automated care-centers
are available in developed
countries
– Ensuring objective evaluation,
low rate of pathogens and
cross-contamination, and
exact execution
Source: created from FDA databases; https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMA/pma.cfm May 2024

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…but they come with slower adoption cycles due to regulatory and trust
issues, still requiring human oversight
▪ Specialized doctors still make diagnoses and almost always supervise/
confirm/check AI diagnosis of dedicated medical apps, but are generally they
are driven towards treatment
▪ Many people seek lower cost medical advise that is not supervised, since the
tools are not dedicated to or labeled as medical tools (e.g. from general
assistant AIs)
– General practitioners will see a large reduction in patient visits
– Specializations where people are ashamed (proctology, urology,
gynecology) are likely to see a higher impact than other specializations
– Automated diagnosis facilities (with scanners, testing equipment) emerge
▪ More widespread adoption of robots in surgery
– The impact on the number of surgeons is limited since even fully
automated surgery requires supervision by a surgeon
Impact
2030s 2040
▪ In a few first-mover countries and
developing countries, more
doctors are substituted by
automated healthcare
▪ In developed countries, oversight
from doctors is still usually
required from a legal perspective
Longer timescales:
▪ 1) Medicine requires high levels of trust, usually preferring humans to make life-changing decisions or at least their
supervision
▪ 2) Clinical approval timelines are long, especially for potentially lethal applications like robotic surgery
▪ 3) Machines autonomously making decisions or performing surgery might require changing the legal framework,
i.e. who is liable if something goes wrong

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Tasks are already highly automatable, but are 1) not yet profitably so or 2)
jobs are singletons and can’t be substituted
Near-term future of automation:
▪ Percentage of replaceable tasks
– Given current tech, 35%-65% of tasks can be automated
– Other increases in automation of tasks with the maturity of new technologies:
▫ 10-20% due to NLP (ca. 2030)
▫ 10-30% when robotics match human dexterity and planning (ca. 2030)
▪ Substitution of human with machines
– Jobs large number of workers with the same profile that are not singletons can be automated, closely
matching the timeshare of tasks being replaced, e.g.:
▫ Bank customer service is very similar and highly scaled, therefore the (for example) 80% of the tasks that a
machine with sufficient language skill replace, will lead to close to 80% elimination of the jobs; the other 20%
are left for more complex answers if the machine can’t help or the customer is high-value
▫ A head of finance is usually a singular person and needs 100% automation to be substituted by machines
Long-term future of automation:
▪ Machine-driven GDP:
– In 2040 (+/- 5 years) ca. 50% of the GDP is mostly driven by robotics or AI, where machines dominate costs,
including investment and maintenance (as opposed to industries where human labor is a major cost driver)
▪ Implications
– Shortly after the GDP of tech-products and non-tech products is matched, the economy is mostly driven by
machines, and humans are mainly prospering by owning those machines
– Existing jobs will change dramatically, possible substitutions are human-centric professions where humans
value seeing other humans (e.g., entertainment, care)
Source: McKinsey Global Institute - Future of Work; The Futurist; Vanguard – The Future of Work; World Economic Forum – Future of Jobs
Tech-driven
productivity
Replacement of jobs will often
occur due to bankruptcies,
since startups with lower cost-
structures take over

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The value of many physical goods and services declined sharply due to the
machines substituting physical labor…
▪ Cost decreases here are largely caused by
substitutions of physical labor by machines, vehicles,
or similar technologies
▪ The y-axis is in log-scale, i.e. a straight line implies an
exponential decrease in cost and an exponential
decrease implies a double exponential (𝒆𝒙𝒚
) cost
reduction

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…and information technology reduces labor for information-based goods
and services, while AI is starting to substitute intelligence-based labor
▪ Historical cost decreases shown here are due to the
substitutions in labor for information processing, i.e.
tasks that require working with information, but that
can be processed using fixed rules
▪ Similarly, AI will decrease cost for intelligence-based
labor (e.g. understanding how a M&A transaction
affects all parts of the balance sheet), eventually
undercutting human intelligence-based labor cost

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Areas where humans outperform machines will go towards zero in 2040,
but physical products and human services will remain valuable
2025 2030 2040
Applications
▪ Highly structured intelligence-
based products continue to
decline in value (e.g. news,
financial reporting, translation,
graphics design, music)
▪ Foundation models in robotics
lead to breakthroughs in planning
of tasks, enabling robots to be
adopted in more environments
▪ Niche applications with high-value
are flooded by targeted software
solutions
– Drug discovery increases in
automation (what used to take
a full PhD can be done by a
few GPUs in a day)
– Mathematicians start to utilize
theorem solvers to support
solving complex theorems
– Engineers use software to
automate design (everything
from parts of chips, to
aerodynamic designs)
– Coding assistants approach
50% share of code written in
some labs
▪ Most back-office jobs that are
mostly work on a computer can be
performed by AI agents (including
accounting, paralegal work,
customer service, compliance,
procurement, marketing, IT
Support, …)
▪ Robotics advanced enough to be
able to take over almost 100% of
jobs in factories for manufacturing
▪ Image/video/speech analysis are
ubiquitous, almost free, and
superhuman (e.g. video
surveillance, medical image
analysis, psychological profiles,
robotics vision, translation, ...)
▪ Speech and video synthesis
(currently done by professional
speakers, actors, and designers) is
indistinguishable from humans
and essentially free
▪ Logistics prices drop further due
to significant improvements in
automation and coordination
▪ Robots dominate physical tasks
and AIs dominate knowledge
tasks
▪ Physical materials and energy
largely determine the cost of
goods in many industries since
computing and robotics hardware
prices are also determined by
them (despite highly automated
manufacturing of both)
▪ Food prices decrease slightly due
to higher automation but have a
limited decrease due to required
physical growth of products
▪ Drugs become much cheaper
since discovery, simulated testing,
and synthesis is much easier
▪ Nanobots are not yet capable of
creating new objects or
manipulating them at will, which
keeps the prices of physical goods
from decaying at the same speed
as intelligence-based goods and
services
▪ People pay a premium for services
provided by humans, due to strong
human bonds

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