Category: Innovation

How Top Universities Fuel Startups with Venture Capital

How Top Universities Fuel Startups with Venture Capital

Top Universities Driving Global Startups Through Venture Capital: A Data-Backed Overview

Universities play a pivotal role in nurturing talent and fostering innovation, and the success of alumni-founded startups is a testament to the entrepreneurial culture present in these institutions. A recent analysis of venture capital funding across top universities reveals the strong influence of academic ecosystems on startup success. This article dives into the top 50 universities based on the venture capital raised by their alumni, explores key geographical trends, highlights key sectors, and references publicly available data to give a comprehensive view.

The Global Leaders: U.S. Universities Dominate the Startup Landscape

Key Statistics (U.S.):

  • Total Dollars Raised: $194 billion
  • Number of Companies Founded: 4,000+
  • Key Sectors: Technology, Healthcare, FinTech, SaaS, AI

According to Crunchbase and PitchBook data, U.S. universities such as Stanford University, Harvard University, and the University of California, Berkeley lead the pack in terms of venture capital raised and the number of companies founded. These institutions have produced successful ventures in technology, artificial intelligence, and SaaS (Software as a Service). Stanford’s proximity to Silicon Valley has helped drive the innovation boom, particularly in tech startups.

Some of the most notable startups originating from these institutions include:

  • Stanford University: Renowned for its close ties to Silicon Valley, Stanford is the birthplace of giants like Google (founded by Larry Page and Sergey Brin), Yahoo (founded by Jerry Yang and David Filo), and WhatsApp (co-founded by Brian Acton).
  • Harvard University: With alumni like Mark Zuckerberg (co-founder of Facebook) and Bill Gates (co-founder of Microsoft), Harvard is a key player in tech, biotech, and healthcare sectors. Startups like Cloudflare (founded by Matthew Prince) also emerged from Harvard.

Europe: A Growing Hub for Innovation

Key Statistics (Europe):

  • Total Dollars Raised: $23 billion
  • Number of Companies Founded: 500+
  • Key Sectors: FinTech, Healthcare, DeepTech, Renewable Energy

Europe has seen rapid growth in FinTech, deep tech, and renewable energy sectors. INSEAD and Cambridge University stand out as key contributors to the startup ecosystem. According to Dealroom.co, FinTech is particularly dominant, with startups like Revolut and TransferWise leading the way.

INSEAD alumni have raised over $23 billion, with many startups thriving in FinTech and consulting sectors. A standout example is BlaBlaCar, a ridesharing platform co-founded by Frédéric Mazzella that has transformed travel across Europe by offering affordable long-distance ride-sharing options.

University of Cambridge has contributed significantly to deep tech and healthcare innovations, producing companies like Arm Holdings, the semiconductor giant. Mike Lynch, founder of Autonomy, is another Cambridge alumnus who has disrupted the tech industry.

Asia: A Rising Force in the Startup World

Key Statistics (Asia):

  • Total Dollars Raised: $15 billion
  • Number of Companies Founded: 1,200+
  • Key Sectors: Technology, Biotech, E-commerce, Mobility

Asia, led by universities like the National University of Singapore (NUS) and Tsinghua University, is rapidly becoming a hotbed for biotech, e-commerce, and mobility startups. NUS has seen its alumni raise billions in venture capital, particularly in the tech sector. According to TechInAsia, NUS-produced startups like Grab, co-founded by Anthony Tan and Tan Hooi Ling, have dominated the Southeast Asian ride-hailing market.

In China, Tsinghua University has been integral in fostering technological advancements, with alumni like Charles Zhang, founder of Sohu, shaping the Chinese tech landscape. The university has become synonymous with engineering and tech entrepreneurship.

Startups in India: The IIT Ecosystem

Key Statistics (India):

  • Total Dollars Raised: $10 billion
  • Number of Companies Founded: 800+
  • Key Sectors: E-commerce, FinTech, SaaS, Mobility

The Indian Institutes of Technology (IITs), particularly IIT Bombay and IIT Delhi, are pivotal in India’s e-commerce, FinTech, and mobility sectors. According to Inc42, startups like Flipkart (co-founded by Sachin Bansal and Binny Bansal, both IIT Delhi graduates) and Zomato (Founded by Deepinder Goyal, IIT Delhi) are reshaping the Indian market and attracting substantial venture capital.

Israel: A Thriving Startup Nation

Key Statistics (Israel):

  • Total Dollars Raised: $8 billion
  • Number of Companies Founded: 600+
  • Key Sectors: Cybersecurity, AI, FinTech, Defense Tech

Israel, often referred to as the Startup Nation, has made a name for itself with innovation in cybersecurity and AI. Universities like the Hebrew University of Jerusalem and the Technion – Israel Institute of Technology have been critical in producing world-class startups. For instance, Waze, the navigation app acquired by Google, was co-founded by Ehud Shabtai, an alumnus of Tel Aviv University. The country’s deep focus on cybersecurity is also reflected in companies like Check Point Software Technologies, founded by Gil Shwed, a Technion graduate.

South Africa: Emerging in FinTech and E-commerce

Key Statistics (South Africa):

  • Total Dollars Raised: $3 billion
  • Number of Companies Founded: 150+
  • Key Sectors: FinTech, E-commerce, Agriculture

While South Africa may not boast the same number of startups as Silicon Valley, it has a growing presence in FinTech and e-commerce. Universities like the University of Cape Town have played a significant role in this growth. One notable company is Yoco, a FinTech startup co-founded by Katlego Maphai, which provides payment solutions for small businesses across Africa. South Africa is also a key player in agri-tech, with startups focusing on modernizing the agricultural supply chain.

South America: A Rising Contender in E-commerce and FinTech

Key Statistics (South America):

  • Total Dollars Raised: $5 billion
  • Number of Companies Founded: 500+
  • Key Sectors: E-commerce, FinTech, PropTech

South America, particularly Brazil and Argentina, has seen a significant rise in e-commerce and FinTech startups. Universities like Universidade de São Paulo and Universidad de Buenos Aires have contributed to this burgeoning ecosystem. Companies like MercadoLibre, co-founded by Marcos Galperin (Universidad de Buenos Aires alumnus), are leading the e-commerce revolution in the region, while Nubank, a FinTech unicorn co-founded by David Vélez, is transforming banking in Latin America.

Why Are These Regions Underrepresented in the Data?

While regions like Israel, South Africa, and South America are seeing growth in venture capital-backed startups, the numbers are still significantly smaller compared to the U.S. and Europe. This can be attributed to a smaller pool of venture capital available, fewer universities with established entrepreneurial ecosystems, and the nascent state of the venture capital markets in these regions. However, they are catching up quickly, and with increasing global attention, these regions are likely to play a larger role in the global startup ecosystem in the coming years.

Conclusion

The data paints a clear picture of the crucial role universities play in fostering entrepreneurship and innovation globally. While U.S. institutions like Stanford and Harvard continue to dominate the startup landscape, the rise of universities in Europe, Asia, and emerging regions such as Israel and South America signals a significant shift toward a more diversified and competitive global startup ecosystem. This is no longer just a Silicon Valley story.

European universities are making strides in deep tech and FinTech, while Asian institutions are positioning themselves at the forefront of sectors like e-commerce, mobility, and biotech. These regions, once considered underrepresented in venture capital, are rapidly scaling their entrepreneurial impact, thanks to increasingly robust academic ecosystems, governmental support, and access to global venture networks.

However, as these newer hubs mature, it becomes clear that the presence of an established entrepreneurial culture, combined with strong alumni networks and well-supported innovation hubs, is key to sustaining long-term growth. For universities aspiring to drive the next generation of unicorns, investing in interdisciplinary research, fostering global collaborations, and creating pipelines between academia and industry will be critical in the years ahead.

The entrepreneurial landscape is rapidly evolving, and universities that align themselves with this shift will not only fuel economic growth but will also shape the future of technology, healthcare, and innovation on a global scale. As venture capital continues to flow into emerging markets, the next wave of disruptive startups may very well come from unexpected regions, further diversifying the global innovation economy.

References:

  1. CrunchbaseCrunchbase Venture Capital Database
    Crunchbase is a comprehensive database of startup companies, venture capital firms, and funding rounds, offering insights into global startup ecosystems and venture trends.
  2. PitchBookPitchBook Data
    PitchBook provides detailed reports on venture capital, private equity, and mergers & acquisitions, offering in-depth insights into sector-specific funding and university-driven startups.
  3. Dealroom.coDealroom European Startup Data
    Dealroom is a leading platform for discovering startups, scale-ups, and investment trends, particularly in the European startup ecosystem.
  4. TechInAsiaTech in Asia Startup Data
    A platform dedicated to startup news and insights from Asia, providing information about venture capital, company profiles, and technology trends across the region.
  5. Inc42Inc42 Indian Startup Ecosystem
    Inc42 is a leading source for insights on the Indian startup ecosystem, offering reports on funding, growth trends, and key sectors like FinTech, SaaS, and E-commerce.
  6. CB InsightsCB Insights Global Venture Capital
    CB Insights is a market intelligence platform that tracks venture capital investments, industry insights, and emerging trends, providing data-driven analysis on startups and sectors.
  7. NASSCOMIndian Tech Startup Ecosystem Report
    NASSCOM publishes reports on India’s growing startup ecosystem, covering key sectors, venture capital inflows, and the impact of technology-driven ventures.
  8. TechCrunchTechCrunch Global Startup News
    A leading news outlet for global startup and venture capital news, TechCrunch reports on funding rounds, sector trends, and university-linked startup initiatives.

Further Reading:

  1. “The Startup Playbook: Secrets of the Fastest-Growing Startups from Their Founding Entrepreneurs” by David Kidder
    This book provides insights into how successful entrepreneurs built their startups from scratch, with lessons applicable to university-driven ventures.
  2. “The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses” by Eric Ries
    A fundamental resource for aspiring entrepreneurs, this book explains how to develop successful startups using the Lean methodology, which has been widely adopted by university-driven startups.
  3. “Zero to One: Notes on Startups, or How to Build the Future” by Peter Thiel and Blake Masters
    Peter Thiel’s insights as a co-founder of PayPal and an investor in numerous startups, including Facebook, provide valuable lessons on startup growth and innovation.
  4. “Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies” by Reid Hoffman
    This book by LinkedIn co-founder Reid Hoffman focuses on the strategy of rapidly scaling companies, a key concept for university startups aiming for exponential growth.
  5. “Startup Nation: The Story of Israel’s Economic Miracle” by Dan Senor and Saul Singer
    This book dives deep into how Israel became a global leader in innovation, especially in sectors like cybersecurity and defense technology, driven by university programs.
  6. Global Startup Ecosystem Report (GSER) by Startup Genome
    This annual report highlights trends in global startup ecosystems, including the role universities play in driving innovation and venture capital flows.
  7. McKinsey & Company – Venture Capital’s Role in Innovation
    McKinsey’s reports provide a comprehensive overview of how venture capital supports startups and fosters innovation, with special focus on key regions like the US, Europe, and Asia.
The Future is Now: How Mojo🔥 is Outpacing Python at 90000X Speed

The Future is Now: How Mojo🔥 is Outpacing Python at 90000X Speed

Calling all AI wizards and machine learning mavericks! Get ready to be blown away by Mojo, a revolutionary new programming language designed specifically to conquer the ever-evolving realm of artificial intelligence.

Just last year, Modular Inc. unveiled Mojo, and it’s already making waves. But here’s the real kicker: Mojo isn’t just another language; it’s a “hypersonic” language on a mission to leave the competition in the dust. We’re talking about a staggering 90,000 times faster than the ever-popular Python! I wanted to share a minor disclaimer there, this is not the “Official” benchmark by The Computer Language Benchmark Game or anything institutional, it is all Modular’s internal benchmarking!

That’s right, say goodbye to hours of agonizing wait times while your AI models train. With Mojo, you’ll be churning out cutting-edge algorithms at lightning speed. Imagine the possibilities! Faster development cycles, quicker iterations, and the ability to tackle even more complex AI projects – the future is wide open.

Mind-Blowing Speed and an Engaged Community

But speed isn’t the only thing Mojo boasts about. Launched in August 2023, this open-source language (open-sourced just last month, on March 29th, 2024!) has already amassed a loyal following, surpassing a whopping 17,000 stars on its GitHub repository. That’s a serious testament to the developer community’s excitement about Mojo’s potential.

The momentum continues to build. As of today, there are over 2,500 active projects on GitHub utilizing Mojo, showcasing its rapid adoption within the AI development space.

Unveiling the Magic Behind Mojo

So, what’s the secret sauce behind Mojo’s mind-blowing performance? The folks at Modular Inc. are keeping some of the details close to their chest, but we do know that Mojo is built from the ground up for AI applications. This means it leverages advancements in compiler technology and hardware acceleration, specifically targeting the types of tasks that AI developers face every day (SIMD, vectorisation, and parallelisation)

Here’s a sneak peek at some of the advantages:

  • Multi-Paradigm Muscle: Mojo is a multi-paradigm language, offering the flexibility of imperative, functional, and generic programming styles. This allows developers to choose the most efficient approach for each specific task within their AI project.
  • Seamless Python Integration: Don’t worry about throwing away your existing Python code. Mojo plays nicely with the vast Python ecosystem, allowing you to leverage existing libraries and seamlessly integrate them into your Mojo projects.
  • Expressive Syntax: If you’re familiar with Python, you’ll feel right at home with Mojo’s syntax. It builds upon the familiar Python base, making the learning curve much smoother for experienced developers.

The Future of AI Development is Here

If you’re looking to push the boundaries of AI and machine learning, then Mojo is a game-changer you can’t afford to miss. With several versions already released, including the most recent update in March 2024 (version 0.7.2), the language is constantly evolving and incorporating valuable community feedback.

Dive into the open-source community, explore the comprehensive documentation, and unleash the power of Mojo on your next groundbreaking project. The future of AI is here, and it’s moving at breakneck speed with Mojo leading the charge! Go ahead and get it here

One Trick Pony

Just be warned that Mojo is not general purpose in nature and Python will win hands down on generic computational tasks due to,

  • Libraries –
    • Python boasts an extensive ecosystem of libraries and frameworks, such as TensorFlow, NumPy, Pandas, and PyTorch, with over 137,000 libraries.
    • Mojo has a developing library ecosystem but significantly lags behind Python in this regard.
  • Compatibility and Integration –
    • Python is known for its compatibility and integration with various programming languages and third-party packages, making it flexible for projects with complex dependencies.
    • Mojo, while generally interoperable with Python, falls short in terms of integration and compatibility with other tools and languages.
  • Popularity (Availability of devs)
    • Python is a highly popular programming language with a large community of developers and data scientists.
    • Mojo, being introduced in 2023, has a much smaller community and popularity compared to Python.
    • It is just now open sourced, has limited documentation, and is targeted at developers with system programming experience.
    • According to the TIOBE Programming Community Index, a programming language popularity index, Python consistently holds the top position.
    • In contrast, Mojo is currently ranked 174th and has a long way to go.
How to measure Engineering Productivity?

How to measure Engineering Productivity?

The fact that you clicked on this article tells me that you are leading/heading a Team, group or an entire Engineering function and most likely a fast-paced startup. Assume the following,

It was a regular weekday, and your CEO/CTO asked the most intriguing question.

Do we measure Engineering Productivity? How do we fare? What can we do to improve it?

Well, if your boss’s name is not Elon Musk or if you do not work for Twitter, you can still be saved. Go on and read through. I know it is a long read.

What is Engineering Productivity?

As with anything you’re trying to improve, it starts with measuring the right data. So, you can actually track the right metrics. This data will form the basis of your analysis and baseline. I strongly recommend you don’t change anything about your current engineering process before you can collect sex weeks’ worth of data about your processes. If you start working on processes, you could end up with a Survivorship Basis.

You should have sufficient historical data to make comparisons. On top of that, most teams work in sprints of two weeks, so six weeks of data allows you to collect data for at least three different sprints. This will give you the allowances for any spikes and eliminate any unusual stress or slack on the execution.

Next, you should make gradual changes to the engineering process to see what improves or impedes the value delivery. It’s ideal to only implement one change at a time, so you can see the effect of each change, with all other things being equal. (it never is :D)

For example, if your engineering squads suffer from significant technical debt, you may want to build an additional stub related to feature completion. Every time an engineer completes a new feature, they must document the new feature. This could mean describing the feature, how is it built, what are the outcomes, how it interacts with other functions and the reasoning behind the design decisions.

By continuously measuring engineering productivity metrics, you can determine if this change has positively impacted the developers’ productivity.

How Is Engineering Productivity Measured?

There are potentially 100s of metrics you can measure for an Engineering Org. Here are four key metrics that will help you to get started with measuring engineering productivity. And I have consciously excluded the Sprint Velocity.

4 Prime Directives of Engineering Metrics

1. The One Metrics to rule them all metrics – Cycle Time 

Software development cycle time measures the amount of time from work started to work delivered. It is a metric “borrowed” from lean manufacturing, and it is one of the most important metrics for software development teams. In plain speak, cycle time measures the amount of time from the first commit to production release.

2. The Oracle of an Engineering Leader – Release Frequency 

You should measure how often you deploy new changes to your customers (production). In addition, you can track deployments to various branches/instances, such as feature branches, hotfix branches, or QA branches. This data would show you how long it takes for a feature/fix to move through the different development stages. In addition, the Release Frequency reflects the throughput of your team. It’s a good stand-in replacement for Agile Velocity, so you don’t spook your Engineers and you are not blind as well.

3. The Guardrail – Number of Bugs

You should definitely track the number of bugs that your team has to resolve within 2 sprints of releasing a feature. This metric helps you to understand the quality of your code better. Higher-quality code should display fewer bugs after feature deployment.

While there are derivative and more evolved metrics like Defect Density, Mean Time to Detect (MTtD), Mean Time to Resolve (MTrR) and Code coverage, those onces makes sense after you’ve taken stock of and address the prime metric “ No: of Bugs” first.

If you want a more detailed list, methodology of QA metrics, refer the links given below. 

4. What is your “Blocker” – Review to Merge Time (RTMT)

This may look like a zoom-in on “Cycle time” metric we discussed earlier. But, in fact it is very different. In fact, it is an interesting metric suggested by GitLab’s development handbook. 

You should measure the time between asking for a pull request (PR) review and merging the PR. Ideally, you want to reduce the time a feature spends in the review state (or pending review state). A high RTMT prevents developers from progressing while they wait for feedback and encourages context-switching between different issues/features.

Arguably, Context-Switching is the highest productivity killer and should be avoided as much as possible

So, why would you measure all these engineering productivity metrics?

Why Is Measuring Engineering Productivity Important?

When you’re a “fast-growing startup”, it’s important to keep an eye on engineering productivity. It happens that these startups favour growth through feature delivery at the cost of effectively scaling the engineering team and ensuring the team’s efficiency.

I hear your question.

But, why does my CEO/VP/MD not understand?

Answer is simple

Assume you have to manage multiple VP’s expectations and outcomes (Sales, Marketing, Support etc), Company’s OKRs, and investors (or) board, will you have more time to dedicate to Engineering Productivity?

In these cases, technical debt can quickly grow, which will slowly kill your team’s productivity. Technical debt can have many negative consequences:

  • More bugs for your team to fix
  • Lower code quality—not only bugs but also worse code design
  • Harder to debug code
  • Scalability issues
  • A decline in overall happiness and job satisfaction

To avoid all of these scenarios, you should measure the engineering team’s efficiency and avoid technical debt buildup. Avoiding these problems before they occur is an excellent Occam’s razor.  But addressing them head-on will have a significant impact on your organisation, both materially and culturally. 

In addition to preventing your team’s productivity from going down, the engineering productivity approach allows you to experiment with various approaches to try and improve throughput & efficiency. 

So, the goal is to improve the engineering process itself. For example, introducing new tools or applying new techniques. Next, you can measure the impact of these changes on your team’s productivity.

In the next part, I will write down on how can measurement improve engineering productivity, Stay Tuned!

References:

  1. Survivorship Bias. 
    1. https://www.masterclass.com/articles/survivorship-bias
    2. https://en.wikipedia.org/wiki/Survivorship_bias 
  2. Cycle Time
    1. https://tulip.co/blog/cycle-vs-lead-vs-takt
  3. Release Frequency
    1. https://community.atlassian.com/t5/DevOps-articles/Why-should-we-start-measuring-the-Release-Frequency/ba-p/1786430 
  4. Detailed QA Metrics to ponder (in addition to No: of bugs)
    1. https://reqtest.com/agile-blog/agile-testing-metrics/ 
  5. Review to Merge Time
    1. https://about.gitlab.com/handbook/engineering/development/performance-indicators/#review-to-merge-time-rtmt 
  6. Context Switching 
    1. https://pacohq.com/blog/guide/the-high-price-of-context-switching-for-developers/ 
College Students Develop "Rocket Motors" In Tamil Nadu

College Students Develop "Rocket Motors" In Tamil Nadu

Applause 

Well, Its not really a rocket motor , but a motor used onboard rockets, 

And its by a team composing of faculty and students of VIT and AU. 

Appreciation and Critical Point
Yes its an achievement for the stdent folk and needs to be appreciated, but I’ll certainly say the staff of the universites shouldn’t get sloppy after 2-3 innovations to account for the DRDO/ISRO Collobration funds in addition to CSIR funds,  we need more and more turnkey solutions coming out of our universities to mainstearm Indian economy.
The News Piece:

Students of an engineering college here have developed for the first time in the country, two special brushless motors, which will form an important part in the soon to be launched GSLV rocket. These motors were previously being imported by Indian Space Research Organisation.

A prototype of this motor was displayed by the students of Sona College of Technology to ISRO scientists at the Vikram Sarabhai Space Centre (VVSC) and ISRO’s inertial systems unit (IISU) at Thiruvanthapuram.

The first motor, which will be placed in the rocket nozzle for controlling its direction, is a 32 newton metre, 1000 rotations per minute quadruplex brushless DC torque motor, Director of Sona Special Power Electronics and Electric Drives (SSPEED) said.

The second, for controlling the rotation of the panels in a satellite, is a 2 newton metre, 50 rotations per minute slotless brushless DC motor. It will be used in the scan mechanism of microwave analysis detection of rain and atmospheric structures for the Megha Tropiques Spacecraft.

ISRO’s inertial systems unit needed ‘cog free’ motors to enhance the performance of precision scanning mechanisms in spacecraft and SSPEED had met all the required parameters, he said.

Prof Kannan said this was a “unique” achievement by an institution, which designed and developed an aerospace quality component for actual use in ISRO’s satellites and rockets. “This would save precious foreign exchange and provide valuable technical know how,” he said.

Source: Press Trust of India



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