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THERE’S NEVER BEEN A MORE URGENT TIME TO SOLVE PROBLEMS THAT MATTER


THERE’S NEVER BEEN A MORE URGENT TIME TO SOLVE PROBLEMS THAT MATTER


THERE’S NEVER BEEN A MORE URGENT TIME TO SOLVE PROBLEMS THAT MATTER


THE
SUPER EVOLUTION

IS MADE POSSIBLE BY THE CONVERGENCE OF THREE TECHNICAL DEVELOPMENTS:

SENSE

Physical-world sensors by the billions are giving us high-resolution data that
was previously unavailable.

COMPUTE

Machine learning and edge computing make computational power stronger and more
affordable than ever, allowing us to discover complex patterns and make better
predictions.

ENGINEER

Advances in engineering, robotics, 3D printing, and CRISPR make it possible to
translate insights into physical and biological action quickly, effectively, and
affordably.

This phenomenon is giving rise to an exponential increase in the rates of
experimentation, iteration, and progress.

Learn More


THE
SUPER EVOLUTION

IS MADE POSSIBLE BY THE CONVERGENCE OF THREE TECHNICAL DEVELOPMENTS:

SENSE

Physical-world sensors by the billions are giving us high-resolution data that
was previously unavailable.

COMPUTE

Machine learning and edge computing make computational power stronger and more
affordable than ever, allowing us to discover complex patterns and make better
predictions.

ENGINEER

Advances in engineering, robotics, 3D printing, and CRISPR make it possible for
us to translate insights into physical and biological action quickly,
effectively, and affordably.

This phenomenon is giving rise to an exponential increase in the rates of
experimentation, iteration and progress.

Learn More


THE
SUPER EVOLUTION

IS MADE POSSIBLE BY THE CONVERGENCE OF THREE TECHNICAL DEVELOPMENTS:

SENSE

Physical-world sensors by the billions are giving us high-resolution data that
was previously unavailable.

COMPUTE

Machine learning and edge computing make computational power stronger and more
affordable than ever, allowing us to discover complex patterns and make better
predictions.

ENGINEER

Advances in engineering, robotics, 3D printing, and CRISPR make it possible for
us to translate insights into physical and biological action quickly,
effectively, and affordably.

This phenomenon is giving rise to an exponential increase in the rates of
experimentation, iteration and progress.

Learn More

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Computing Infrastructure

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Investing in visionary founders, transformational technology and emergent
ecosystems for a new world.
Using AI to create incredibly efficient solar cells: Our investment in Cosmos
Innovation


USING AI TO CREATE INCREDIBLY EFFICIENT SOLAR CELLS: OUR INVESTMENT IN COSMOS
INNOVATION

Dror Berman

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Published in

Innovation Endeavors

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5 min read
·
Nov 10

13



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By Dror Berman and Josh Rapperport



Technological progress is often described in terms of platform shifts. Personal
computing. The internet. Mobile. As these new paradigms gain steam, the
innovation that follows changes the course of our society. Our core thesis — the
Super Evolution — is focused on how platform shifts in data, compute and
engineering converge to produce rapid progress in domains where innovation has
been driven by human experts. We continue to be fascinated by the way this
intersection of technological and scientific methods creates combinatorial
magic.

Two of the most important platform shifts of the coming decades will be
artificial intelligence and the energy transition to carbon-free power. It goes
without saying the current rate of improvement in AI is one of the most exciting
moments in modern technology. Similarly, the talent and capital flowing into the
energy transition is only accelerating and will need to continue if we’re to
avoid dangerous climate change.

We know AI can transform how science gets done (as Eric Schmidt recently laid
out) and we know we need profound innovation in the way we produce electrons if
our climate goals are to become reality. This intersection of AI and renewable
energy is where our latest investment, Cosmos Innovation, is building. We’re
excited to have been a part of the team’s journey from the very beginning as
they apply the latest in AI to create a better, more efficient physical world.

In concrete terms, Cosmos Innovation is radically accelerating and improving the
process by which semiconductor development can produce optimal performance. For
semiconductors, like all processes of experimentation, development has
historically been human-driven. Researcher’s design-of-experiment (DoE) is
guided by their understanding of underlying science, and they make changes
through each iteration of an experiment to push toward some desired performance.
We can think of this process as traversing a design universe, essentially a
matrix of possible process conditions and material compositions, or a ‘recipe’,
in the hopes of arriving at the optimal design. The various process parameters
provide the ‘knobs’ that the researchers can turn, and they can only change one
or two at a time.

Cosmos Innovation is using machine learning/iterative learning to turn all the
knobs at once. In other words, they are using AI-driven experimental design to
enable optimized outcomes with far fewer experiments, and much better target
performance. They have demonstrated these capabilities with some of the largest
semiconductor companies in the world, and the results are extraordinary — Cosmos
Innovation can accelerate process optimization by 10x. The technology allows
semiconductor manufacturers to develop novel, optimized recipes in a fraction of
the time and cost of conventional methods.

Which brings us to the critical application of their technology — producing the
most efficient solar panels ever made. Specifically, unlocking the full
potential of perovskite-silicon tandem solar cells, a uniquely promising but
not-yet-commercialized design. Perovskite-silicon tandem is the ideal candidate
for AI-guided design because it is incredibly complex. While the underlying
science and performance of crystalline silicon (over 90% of today’s global solar
market) is well understood, adding a perovskite layer on top creates significant
challenges. Perovskite recipe optimization is hard because of very high
combinatorial complexity, somewhere around 5^72 possible permutations.
Perovskite tandem architectures can have 12 or more layers, and over 100 input
knobs, and there are no performant, physics-based models that researchers can
rely on. Furthermore, the “goodness” factors, or relationships between the
parameters driving ultimate performance, are also not well understood. This has
led to fundamental challenges around degradation of produced solar cells, with
stability and efficiency deterioration at elevated temperatures and humidity. In
short, making sense of the overwhelming amount of combinations is impossible —
it’s clear that human-driven approaches are too slow and won’t yield optimal
results.



Why is it so important that perovskite-silicon reaches its full potential, and
why is the market opportunity so big? Because of the incredible efficiency
potential of perovskites. Delivering cheap, abundant power (often described as
levelized-cost-of-energy or LCOE) from the sun requires optimizing two primary
levers — solar cell cost and solar cell efficiency. While the last decade has
seen an order of magnitude decline in costs, crystalline silicon efficiency has
increased by only a few percent, going from roughly 18% to 25%, with a
theoretical limit of 29%. Building on all the gains of silicon to date, the
theoretical limit of perovskite-silicon is 43%. This remarkable potential is why
the solar industry is so excited about perovskites.

The timing is ideal. Fundamental research in perovskites has been gaining
momentum for the last decade, with record efficiencies nearing 34%. The
fundamental science has been largely derisked, and now Cosmos Innovation is
ready to use AI to close the gap to reality. Not only is the team addressing
major challenges in designing solar cells, but they are also bringing
self-learning to the manufacturing stage. As First Solar showed, it takes
incredible execution and manufacturing prowess to produce novel solar
architectures at scale. In this stage, AI can also be a game-changer. Cosmos
Innovation intends to utilize next-gen capabilities to drive producibility,
quality control and root-cause analysis, allowing for repeatable production of
cells with commercial stability and performance. Automated, self-learning
process control and tuning — long the ideal among visionaries in solar and
semiconductors — has been largely out of reach over the last 50 years of solar
manufacturing. The first version will be ready for experimentation beginning
this fall at the Singapore-based fab.

We first partnered with Cosmos after hearing incredible things about the
founders from Demis Hassabis, the CEO of DeepMind and Tomaso Poggio, the
renowned MIT academic. It became clear to us that the founders, Vijay and Joel,
are world-class technologists bringing together decades of AI, semiconductor,
and solar expertise. Vijay led the AI effort at the Institute for Infocomm
Research at the Agency of Science, Technology and Research (A*STAR), where he
managed a portfolio of more than 50 AI projects across over 10 domains with a
key focus on semiconductors. Joel was the former group head at the Solar Energy
Research Institute of Singapore, where he led the development of an
award-winning PV manufacturing technology. He also served as an AI team lead at
A*STAR, where he led the development and deployment of AI solutions for Tier-1
manufacturers and R&D institutes in the semiconductor, material, and chemical
domains. It’s rare to see a team of such multidisciplinary depth, and we’re
proud to be supporting them alongside Xora and Two Sigma.

Solar energy is the single most impactful lever available to reduce planetary
warming in this decade. That is the conclusion of a highly anticipated report
from the Intergovernmental Panel on Climate Change (IPCC), which assessed the
various mitigation pathways to curb climate change between now and 2030. AI
provides an unprecedented opportunity to meet this moment and change the way we
produce electricity. We look forward to the journey!

 View on Medium
By Dror Berman and Josh Rapperport
 Read more
S3 as the universal infrastructure backend


S3 AS THE UNIVERSAL INFRASTRUCTURE BACKEND

Davis Treybig

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12 min read
·
Oct 24

261

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TL;DR

 1. Traditionally, infrastructure services such as databases have built their
    own storage layer on top of local disk storage (e.g. EBS Volumes). This is
    partially a holdover from the pre-cloud era.
 2. Increasingly, S3 is being used as the core persistence layer for
    infrastructure services (e.g. Snowflake, Neon, BigQuery, and WarpStream),
    rather than simply as a backup or tiered storage layer.
 3. This S3 as a Storage Layer architecture gives you so many advantages
    (especially as a startup), that it is likely to become a standard
    architecture for most cloud services moving forward
 4. There is a huge opportunity for startups using these ideas to disrupt large
    cloud infrastructure categories (especially databases and data systems)



Traditionally, cloud infrastructure services have primarily relied on local disk
storage as their source of truth storage layer. If you take a look at the
average cloud infrastructure service, you’re almost guaranteed to see a storage
model based around storing data on local SSDs such as EBS volumes (e.g. see
Elastic, Kafka, RDS, MongoDB, AWS Neptune).

Most of these services use local I/O to read and write data to local disk,
coupling compute and storage in a way that creates huge issues around
autoscaling and cost. A few, such as AWS Aurora, disaggregate by having compute
workers make networked RPC calls to a separate storage service, which then
reads/writes to local disk. But in either case — the service provider is writing
a custom storage layer and dealing with all the complexities of distributed
cloud storage, including durability, availability, fault tolerance, and similar.
Often, a lot of this complexity then leaks to the user of the service.

Much of this storage architecture is a holdover from historical on-premise
deployments where infrastructure was static and pre-provisioned, and customers
were not subject to the pricing model of the cloud. Yet, the cloud has not only
changed these dynamics, but also offers a new storage primitive that is
effectively infinitely scalable, available, and elastic: BLOB stores.

I am now seeing many infrastructure services build around these cloud BLOB
stores as their durable storage backend (not just as a backup layer). This “S3
as a Storage Layer” architecture gives you so much for free as an infrastructure
service — separation of storage and compute, time travel, fault tolerance,
infinite concurrency reads, fast recovery, better developer experience for your
users— that I think it will become the default architecture for a large
percentage of cloud infrastructure services over the next decade.

So, let’s explore what this architecture looks like, its benefits, and some of
the early examples of products built with this architecture in mind.

> “I mentioned in a talk in 2020 about building a cloud-native database. There’s
> a point: how well S3 could be leveraged would be key. I think this point is
> still valid today.” — Building a Database in the 2020s


THE S3 AS A PERSISTENCE LAYER ARCHITECTURE



The above image is a simplified illustration of the architecture I am
describing. The core idea is fairly simple — S3 is used as the primary storage
of the application, rather than local disk. There is then a stateless compute
layer which often includes local caching. Sometimes, there is a “memory layer”
which acts as a sort of hot data layer on top of the BLOB store (though
importantly, it is not the source of truth persistence layer).

Typically, there is also a disaggregated control plane which both manages
secondary metadata storage and controls other jobs (e.g. background processing
of BLOB file storage, etc).

Often, the data & compute plane resides in a customer’s cloud, simplifying the
deployment of a system like this, but the control plane resides in the vendor’s
cloud.

You can see reference implementations of this from Neon (slide 12), Snowflake
(page 220), Warpstream, Dremio, and Datadog. “Building a Database on S3” is also
a canonical read here.


BENEFITS OF S3 AS A BACKEND


SEPARATION OF STORAGE AND COMPUTE

The first and most foundational advantage of this architecture is that it
creates true separation of storage and compute, allowing for efficient and
simple autoscaling.

If you need to scale reads, you can just spin up new compute workers. Since they
are stateless, this takes almost zero time and requires no copying,
repartitioning, or rebalancing of data across workers. This means seconds-scale
autoscaling. If you need to scale writes, you don’t need to wait for
repartitioning or reshuffling of data across disks in order to properly balance
load. Failure recovery is easy because there is no need to rehydrate data if you
need to recover a compute worker that goes down.

The size of your compute layer can scale purely as a function of incoming
traffic, independent of the amount of data being stored. This means compute can
scale to zero, you pay for only exactly the compute & storage cost you are using
(vs. one always being over-provisioned in a disk architecture), and you never
need to think about things like upscaling a cluster that is about to run out of
disk space.

This also means that coordination requirements are massively reduced in the
worker pool — you don’t need special “leader” nodes responsible for coordination
or consensus because the compute layer is stateless. This ties into a broader
concept which is — this architecture lets you offload a lot of distributed
system and storage concerns to your cloud vendor.

> On the cloud, computing is much more expensive than storage, and if computing
> and storage are tied, there is no way to take advantage of the price of
> storage, plus for some specific requests, the demand for computing is likely
> to be completely unequal to the physical resources of the storage nodes (think
> heavy OLAP requests for Reshuffle and distributed aggregation) — PingCap CEO


OFFLOAD DISTRIBUTED SYSTEM & STORAGE CONCERNS

Large cloud vendors like Amazon have spent billions of dollars making their BLOB
stores effectively infinitely available, infinitely durable, and infinitely
elastic. Using them as a persistent storage layer means you get all of this for
free.

This reduces how much time and effort is needed to solve a large class of issues
traditionally important to solve in infrastructure products, such as quorum &
coordination (e.g. ZooKeeper/RAFT) as well as storage logic (e.g. replication
across availability zones, file management), because Amazon has already solved
them for you (likely better than you would have). Note that this architecture
does not fully obviate the need to consider these things — e.g. Neon still
implemented Paxos since they buffer writes to S3.

Cloud object stores also offer a lot of rich storage “features”. For example,
since BLOB stores use an immutable file structure where changes are simply
appended as new files, Neon was able to offer branching via a copy-on-write
architecture as well as “time travel” queries almost out of the box.


BUSINESS MODEL & COST

The S3 as a persistence layer architecture also creates profound advantages from
a cost perspective. This takes shape in few key ways.

The first is related to the decoupling of storage and compute — since you are no
longer over provisioned for one or the other, you will definitionally pay less
assuming all else is equal.

The second, and a more nuanced point, is that this architecture is much better
suited to the business model of the cloud. Cloud vendor pricing extracts a huge
premium on certain actions (such as data copies across availability zone) over
others (such as reading/writing to S3), in a way that extracts immense rent with
the local-disk storage architecture (e.g. see Warpstream’s blog). When you use
S3 as the “networking” layer which is replicating across availability zones, you
are effectively arbitraging the pricing model of the cloud vendors in some ways.

Third — cloud BLOB storage is exceptionally cheap (though there is a caveat here
I will get more into later that you need to be careful about how you manage this
architecture for lower-latency or high throughput systems that requires tons of
writes/reads).


DEPLOYMENT

Another elegant benefit of this architecture is that it solves a lot of
deployment issues as a managed service vendor out of the box.

In particular, using S3 as a storage layer makes it very easy to have your data
and compute plane run in your customers cloud (on top of their S3). Because you
are not storing the data yourself, but instead it is being stored in their own
S3 buckets, you immediately solve a large number of data security related issues
a customer might ask you about. Even better, you can still keep your control
plane and metadata plane in your cloud if you would like.

An analogous dynamic can be seen in many of the software vendors over the past
few years who use Snowflake as a backend, such as Panther and Eppo. It is so
much easier for such vendors to deploy to larger, more security conscious
customers as a result of this architecture.


DEVELOPER EXPERIENCE

The last thing worth calling out here is that using S3 as a backend can
typically greatly improve the developer experience of a product.

Local disk storage architectures tend to create a lot of complexity by requiring
the developer to reason about a stateful storage service. While managed services
can partially hide this, the abstraction tends to leak.

In general, a system which offloads all storage & distributed system concerns to
S3 and which has a stateless pool of compute workers requires far less
abstractions and a far smaller API surface for a developer to reason through.
Products architected in this way tend to be far simpler as a result — Snowflake
being a fantastic example vs. Redshift.


RECENT EXAMPLES OF THIS IN ACTION

 1. Neon is a serverless Postgres offering which separates compute and storage
    by using S3 as its persistence layer
 2. Warpstream is a kafka-compatible streaming service that uses S3 as its
    backend, rather than local disk based log storage
 3. LanceDB is a new vector database vendor that uses a custom storage format
    (Lance) and a disk-based approximate nearest neighbors algorithm, allowing
    for a serverless vector DB offering that runs on top of S3
 4. Motherduck uses DuckDB as an in-memory query engine that can run on top of
    S3 as the storage layer
 5. Husky is an internal logs storage engine used at Datadog that runs on S3.
    KalDB is a similar library out of Slack.
 6. Basically all modern cloud data warehouses and lakehouses use this
    architecture, including Snowflake, BigQuery, & Databricks
 7. Serverless query engines such as Dremio and Bauplan


CAVEATS & CHALLENGES

Importantly, the goal of this article is not to say that many of these benefits
can not be achieved building an infrastructure service with a custom storage
layer. For example, you can certainly achieve separation of storage and compute
without building on S3.

Rather, I think there are two key takeaways:

 1. Building on top of BLOB stores as a backend gives you all of these things
    for free (mostly — see challenges below). This gives you such higher
    velocity as a startup, and as a result opens up a new class of startup ideas
    to be built that otherwise would have required insane amounts of money and
    time to build the initial service (e.g. see how fast Neon has come to market
    with a serverless Postgres offering).
 2. It is hard to compete with the durability, availability, and scalability of
    BLOB stores, and as a result, unless you have a very good reason to design
    your storage system differently, this is like a suboptimal tradeoff to make

Of course, this architecture is not panacea. Indeed, there is a good reason why
all the initial adopters of this architecture (Snowflake, BigQuery, Procella)
were analytics-oriented, more “offline” systems — S3 is not optimized for high
IOPS and, if used naively, is very expensive to constantly write/read to on the
scale of seconds. This is part of why it is so interesting to now see very
operational product categories such as event streaming (Warpstream) and OLTP
databases (Neon) adopt this architecture.

Getting such product categories to work requires some additional work,
particularly within the following areas:


CACHING AND MEMORY

Typically, a sophisticated caching or “hot storage” design is required to make
an architecture like this work well. For example, Snowflake discusses caching
heavily in their original paper and Neon’s PageServer layer acts as a hot
storage/cache layer. See also this CMU presentation by Neon.

All of these designs leverage an in-memory cache or a local disk based “cache”
(e.g. essentially a “higher tier” temporary storage that is not seen as a
durable source of truth), or both, as a way to offset these issues. Sometimes,
this is coupled with the compute layer (e.g. Snowflake compute VMs have an
in-memory cache). Other times, it is an independent layer (e.g. Neon PageServer)
distinct from the compute workers.


READ/WRITE STRATEGY

You can’t naively map the same write/read strategy you would use in a local disk
design to an S3-backed design. The volume of reads and writes would lead to
insane costs, or create an I/O bottleneck in processing.

As such, careful consideration is required in how often and when you access S3
under this architecture. For example, do you bundle or batch requests, and under
what situations? Assuming you have a caching or hot storage layer, how do you
maintain cache coherence and under what situations do you go to the BLOB store
vs not? How do you mediate/minimize the number of times you need to query S3
while maintaining sufficient freshness or consistency guarantees?

Often, this is about leaning into S3’s strengths (e.g. pseudo-infinite
parallelism) and trying to mitigate it’s weaknesses (relatively high query
latency, etc).


STORAGE LAYOUT

How storage is laid out and organized within S3 also often requires a dramatic
rethinking relative to what might have been optimal with a local disk based
architecture. For example — you may not want to partition files in the same way,
or you may not be able to make the same assumptions about sequential disk
access.

Warpstream provides an interesting example of this here— completely changing the
way topics and partitions are stored on disk relative to what Kafka has
traditionally done, in a way that solves a lot of the barriers S3 introduces
regarding cost/latency.


METADATA MANAGEMENT & OFFLINE PROCESSING

Proper metadata management is critical to make architectures like this work. Key
ways this takes shape include:

 1. Optimizing how S3 is scanned
 2. Guiding offline processing of the data in order to continuously optimize its
    layout for the online system to perform well (e.g. compaction, file
    restructuring)
 3. Optimizing when data is queried from S3 vs. secondary sources (e.g. a cache
    on a compute worker or similar)

When to store metadata in S3 or a third party metadata storage layer, and
whether to cache metadata, are also important questions to consider.


COST

I touched on cost, but it it is worth calling out directly. A lot of the above
also relates closely to cost. While S3 is cheap as a storage layer, and using S3
as a “networking” layer for availability zone replication is very cheap, naively
making thousands of read/write API calls to S3 will create a huge cost burden.
As such, this architecture is not inherently more cost efficient unless you
think about the right way to implement it.


THE STARTUP OPPORTUNITY


Chris Riccomini

What I find particularly interesting is that, in spite of the immense benefits
of this architectural approach, it is still represents a relatively small
portion of cloud databases and data systems. The products I have thus far
mentioned plus a few others — Snowflake, Databricks, BigQuery, Procella,
Warpstream, LanceDB, Neon, Motherduck, Husky, Bauplan, Quickwit, Earthmover, and
Dremio — are the main products I am aware of that fit this architectural
paradigm.

There are so many huge infrastructure categories where these ideas could allow a
disruptive new entrant to emerge — search, graph databases, log analysis,
timeseries databases, OLAP (e.g. Clickhouse, Druid), etc. Doing many of these
right will require thoughtful consideration with respect to dealing with the
drawbacks of S3. But, if done correctly, you often have the oppurtunity to be
the first truly “serverless” offering to emerge in the category.

As the composable data stack continues to flourish, and open formats such as
Iceberg/Delta Lake (Table Formats), Parquet/Lance (File formats), and Arrow
(Memory format) continue to improve, it is only going to get easier to design
systems in this way.

As this pattern becomes more commonplace, there will likely also be interesting
second order effects. For example, if most infrastructure becomes a query layer
on S3, how will the role of ETL change? It will be a lot less important to move
or replicate data in between N different specialized storage systems (e.g.
Elastic, Druid, etc), but it may become more important to transform across data
formats within S3 to optimize for different workload characteristics (e.g.
Parquet to Lance).

Building on object storage also drastically lowers the bar for building a new
data system, which should allow for the rise of more “vertical” infrastructure
startups that differentiate more on developer experience than on pure
performance. Neon is a really good example of this.

I am deeply interested in investing in companies leveraging this architectural
pattern of S3 as a backend. If you are working on something in this space I
would be exceptionally interested in talking to you. Shoot me a note at davis
(at) innovationendeavors.com

Thanks to Chris Riccomini, Ciro Greco, Jacopo Tagliabue, and Chang She for
feedback on this.

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TL;DR
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Medicine reimagined: Philip Jeng, Co-Founder and COO, Think Bioscience


MEDICINE REIMAGINED: PHILIP JENG, CO-FOUNDER AND COO, THINK BIOSCIENCE

Innovation Endeavors

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Innovation Endeavors

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5 min read
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Oct 5

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Innovation Endeavors was founded on the thesis of the Super Evolution: that a
proliferation of data, computational capacity, and advanced engineering would
converge and translate into significant, fast changes for the world. We’ve seen
this firsthand.

But it’s people who make this era of innovation possible: changemakers working
to revolutionize computing infrastructure, engineering biology, climate,
intelligent software, and the physical economy. This interview series highlights
the stories of the standout founders and entrepreneurs bringing the Super
Evolution to life and gives you a firsthand look at what lessons they’ve learned
along the way and how they hope to change our world.

We hope their candid insights are helpful for anyone tackling meaningful
problems.



What initially attracted you to entrepreneurship?

As a third culture kid, I’ve always lived at the intersection of different
worlds — a sometimes challenging but always exhilarating experience. Looking
back, I realize I’ve always chased this feeling that led me to the
entrepreneurial journey today.

I chose to major in bioengineering and was often described as a “jack of all
trades” because I enjoyed all subjects of science. On top of that, I always knew
I wanted to build a career that combines science and business — despite not
knowing exactly what this meant. After finishing college, I was fortunate to
start my career in a rotational program at Genentech and saw how all the
different fields of science come together to make a drug. But I also saw that
science alone wasn’t enough to create a drug and run an organization to support
it. Eager to learn about the industry's business side, I became a management
consultant, where I had to learn a new way of thinking from my training as an
engineer. I then pursued an MBA at Harvard Business School, where I was exposed
to venture capital, startups, and the entrepreneurial spirit of Kendall Square.
After HBS, to the surprise of my peers, I created a role at biotech startup in
China because I was obsessed with learning about the biotech ecosystem there.
There was plenty of culture shock, to say the least.

With each of these experiences, I got to see a new facet of building a company
and inched closer to putting it all together. Whether cultural or technical,
I’ve also enjoyed bridging very different ways of thinking. I realized
entrepreneurship was the perfect way to operate at the nexus of many different
fields.

What inspired you to co-found Think Bioscience?

To be honest, serendipity. During COVID, I was introduced to Jerome Fox, a
professor at CU Boulder. Remarkably, it was through six degrees of separation.
At the time, I was part of the Harvard Business School Blavatnik Fellowship, a
program that supported alumni in life science entrepreneurship. In his academic
lab, Jerome was working on an idea he had conceived dating back to his post-doc,
an elegant process of letting nature solve our toughest drug design challenges.
After all, nature has evolved powerful machinery to access diverse and bioactive
molecules to solve its own ecological challenges. The science was innovative, to
say the least.

From my internships in VC, I saw how important it was to have the right team to
give innovative science the best shot as a life science business. Our
backgrounds complemented, and our workstyles matched well — it was the perfect
opportunity.

What’s the big problem you’re hoping to solve at Think Bioscience?

We’re working to develop small molecule drugs for challenging targets such as
protein tyrosine phosphatases, which have no FDA approved drugs. In short, we
solve two major small molecule discovery challenges.

Accessing novel chemical matter. Historical libraries are biased toward
historical drug targets and associated active sites. Furthermore, they only
cover a small fraction of the bioactive chemical space. And while natural
products constitute many FDA-approved small molecules, they were first designed
by nature for ecological solutions and can experience synthesis challenges.

Discovering functional binding sites. Proteins are complex. They operate in
signaling networks and exhibit dynamic motion. Despite significant advances in
computational approaches, identifying novel functional binding sites is
incredibly challenging.

Think Bioscience’s platform addresses both challenges by leveraging Nature’s
machinery. We encode a therapeutic objective into a microbe and instruct it to
“find a way to inhibit this target or die.” The target is expressed in a
cellular environment where more complex binding behaviors can be captured (not
just simulated). Instead of telling the platform how to solve the problem, we
simply define the functional activity desired. In parallel, we endow the
population of microbes with unique biosynthetic pathways that encode for natural
product scaffolds and let natural selection run its course. Those that survive
reveal both a bioactive starting point and a functional pocket to begin new drug
discovery campaigns.

What excites you the most about biotechnology?

The combination of intellectual stimulation and greater purpose. It’s a complex
and captivating industry that blends cutting-edge science, multi-disciplinary
teams, and careful capital allocation. There’s always so much to learn. It’s
exciting to work in an industry where the ultimate goal is to help patients, and
scientific advancements become the foundation for future work to be built upon.
Working daily with people who share these values is icing on the cake.

The biotech space is rapidly accelerating. What breakthroughs do you think will
be developed in the next ten years?

Not a scientific breakthrough per se… I’m excited about new company formation
models, particularly in new geographic hubs. Using NSF funding as a proxy for
scientific innovation, the substrate of innovative science is relatively
democratic across the country. Yet, funding is vastly disproportionately
allocated to the coasts — for both understandable historical reasons and
habitual pattern following. To me, there is an immense opportunity to catalyze
these regions with the right teams and capital. I’ve seen this first-hand in the
Colorado biotech community. Array Biopharma (acquired by Pfizer in 2019) has
created fertile ground for the next generation of startups. Think Bioscience
wouldn’t be where we are today without the expertise from ex-Array veterans and
the tight-knit biotech community here. From my vantage point, an important
rate-limiting step in biotech is coordinating the complex orchestra of science,
people, processes, and capital. In the next ten years, I believe more innovation
can be industrialized by continued advancements in enabling technologies,
flexible work models, founder training, and more evenly distributed investment
regions.

Do you have any takeaways from the fundraising process?

If you had asked me prior to Think Bioscience, I would have described
fundraising along the lines of a discrete evaluation of a polished deck after a
long period of being heads-down. Now, I see it as a more collaborative and
continuous process. Conversations with investors have helped us refine our
company strategy based on their own portfolio companies’ experiences and network
with other investors/pharma. Perhaps even more surprising is how human the
process is. There are a lot of calls/messages sharing ideas, questions, and
feedback — which works best on a foundation of trust.

Lastly, what piece of advice do you have for entrepreneurs looking to break into
engineering biology?

Build a support system for other founders going through the same journey. While
there is a lot of written material online, there are still countless details
about getting off the ground and operating. I’m incredibly grateful to all the
folks who’ve been a great sounding board (and sometimes therapeutic session).

 View on Medium
Innovation Endeavors was founded on the thesis of the Super Evolution: that a
proliferation of data, computational capacity, and advanced engineering would
converge and translate into significant, fast changes for the world. We’ve seen
this firsthand.
 Read more
Bridging technology, defense, and progress: Josh Berglund joins Innovation
Endeavors


BRIDGING TECHNOLOGY, DEFENSE, AND PROGRESS: JOSH BERGLUND JOINS INNOVATION
ENDEAVORS

Dror Berman

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Innovation Endeavors

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3 min read
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Sep 22

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In the ever-evolving landscape of technology, geopolitics, and economic shifts,
we at Innovation Endeavors are always on the lookout for leaders who can guide
our vision into the future. Today, we are thrilled to announce a new addition to
our team, someone who has charted new paths of leveraging technology to bolster
American progress — Josh Berglund, our newest Venture Partner.


THE GLOBAL CANVAS

As we stand at the crossroads of history, the world is witnessing unparalleled
geopolitical tensions. With the Russia-Ukraine conflict and escalating US-China
tensions, we’re led to a renewed “Space Race” where nations vying for the
forefront in cutting-edge technologies like AI, quantum computing, and
semiconductors are just the tip of the iceberg.

Concurrently, the urge for nations, especially the US and several European
countries, to be self-reliant is more prominent than ever. The pressing need to
develop infrastructures that promote self-sufficiency, especially in crucial
areas like energy, manufacturing, and supply chain, is paramount. This pivot
towards independence has been the catalyst for government-led investments,
evident in initiatives like the IRA bill and the Chips Act. As a result, a
growing number of startups are looking to partner with the Department of Defense
alongside other enterprise customers.


WELCOMING JOSH

In such a transformative era, who better to navigate these waters than Josh
Berglund? Josh spent seven years as a non-commissioned officer in Army special
operations, with deployments to combat zones in Syria, Iraq, South America, and
Eastern Europe. His role in the military included bridging connections between
In-Q-Tel, DIU, DARPA, various national labs, and the combat development
directorate of his unit. Before donning the uniform, he thrived in the business
realm, both as an entrepreneur and an investor, playing pivotal roles in the
creation and success of some of the most recognized companies in the last two
decades.

Josh’s multifaceted expertise spanning government, technology, and finance makes
him an invaluable ally for companies setting their sights on government and
defense sector contracts. At Innovation Endeavors, we’re not just about backing
companies; we aim to drive generational change and impact global policies with
the transformative solutions we support. Josh’s leadership and the trust he has
garnered over the years will undoubtedly be an incredible asset to our growing
team.


SHARED BONDS

One of the aspects that strengthens our synergy with Josh is our shared
experiences beyond the business world. Both of us have served in the Special
Forces — Josh with the U.S. Army and I with the Israeli Defense Forces. This
shared history instills in us an intrinsic understanding of the gravity behind
the missions of our companies. Serving in these elite units has not only honed
our sense of duty and purpose but also ingrained in us the ethos of navigating
through seemingly insurmountable challenges. It further informs how we approach
the challenges faced in the business realm, bringing a unique perspective that
blends military strategy with entrepreneurial tenacity.

Startups, in many ways, mirror the relentless, ever-changing terrains of Special
Forces missions. CEOs face a gauntlet of challenges, navigating unpredictable
landscapes, making tough decisions on the fly, and continually adapting to reach
their objectives. Our combined experiences have cemented our commitment to
guiding and supporting these CEOs, leveraging our shared backgrounds to help
them build truly meaningful and impactful companies.


ON WORKING TOGETHER

Josh will be the linchpin in our team as he mentors our portfolio companies,
shapes our investment strategies in government and defense, and expands our
network in these sectors, setting a new trajectory for our firm. He’s ready to
strengthen partnerships with companies that share our ambition of bolstering
American resilience and crafting a future that’s not just technologically
advanced but also safe and sustainable.

As we begin this exciting journey with Josh, we extend a warm and heartfelt
welcome to him. Together, we’ll make significant progress in a world rife with
challenges and opportunities. Welcome aboard, Josh!

 View on Medium
In the ever-evolving landscape of technology, geopolitics, and economic shifts,
we at Innovation Endeavors are always on the lookout for leaders who can guide
our vision into the future. Today, we are thrilled to announce a new addition to
our team, someone who has charted new paths of leveraging technology to bolster
American progress — Josh Berglund, our newest Venture Partner.
 Read more
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