Toplyne is a smart sales enhancement platform that provides you advanced features and tools to increase your sales. It enables you to combine user signals from product use, billing, and support in order to find the hottest PQLs (product-qualified leads) for your sales crew to pursue.

Toplyne Pricing, Reviews and Features (February 2024) - SaaSworthy.com

An extension that plugs into your CRM of choice to display Toplyne stats.

Toplyne - Chrome Web Store

Toplyne - Cloud based platform offering lead management solutions for PLG companies. Raised a total funding of $17.5M over 2 rounds from 12 investors.

Toplyne - Company Profile - Tracxn

Compare the best business software and services based on user ratings and social data. Reviews for CRM, ERP, CAD, PDM, HR, and Marketing software.

Toplyne Pricing Plans & Cost 2023 - G2

Toplyne | 35,684 followers on LinkedIn. The behavioral lead scoring platform Canva &amp; Vercel use to generate sales pipeline from their self-serve funnel. | The behavioral lead scoring platform used by companies like Vercel and Canva to generate sales pipeline from their self-serve funnel.

Toplyne | LinkedIn

How do product-led growth companies get non-paying customers to subscribe? And how do they get paying customers to move to higher tiers? Toplyne wants to help.

Toplyne helps product-led growth teams tackle user conversion

California-based Toplyne, a startup that provides a plaform to improve SaaS conversions, today raised $15 million.

Toplyne raises $15M to help SaaS companies sell more | VentureBeat

Toplyne, a SaaS platform that lets companies to monetize their product-led growth flywheel, today announced a $15 million Series A funding round led by Tiger Global and Sequoia Capital India, with participation from existing investors Together Fund, Sequoia India’s Surge and angel investors ...

Toplyne raises $15 million Series A to monetize product-led growth ...

Explore how companies like Vercel, Notion, Voiceflow and Pitch use Toplyne to lift conversions, drive expansion, and prevent churn.Request a tour

Toplyne | Predictive AI for Sales and Marketing

Toplyne | Tracxn

Toplyne

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Darwin's law of SaaS go-to-market evolution Act 1: The 2000s and the reign of the salesperson! A dimly lit steakhouse. . There’s a limo parked outside. Enter a salesperson, engrossed in conversation with a CXO In the early 2000s, the SaaS go-to-market motion was entirely sales-led. It made sense to carve out a budget for your sales teams to wine and dine CXOs of potential customers because that was the only way large enterprise accounts were going to find out about your hot new product. The protagonist of the story was sales. Supporting actors were unheard of. Act 2: The early 2010s and the evolution of inbound marketing It is a decade later and Sales, by now a veteran name in the industry has stepped aside to let a protege take the lead, and a very capable one at that. Enter Marketing, to loud music and confetti everywhere. The 2010s saw the sales-led motions evolve to inbound marketing where focused, targeted campaigns replaced steak dinners and top-golf and widened the top of the funnel (ToFu), paving the way for Marketing Qualified Leads (MQLs) to be handed over to Sales. And as for good ol' Sales, the supporting act meant more potential buyers reaching out to them, and in turn, more conversions 🚀 Act 3: The 2020s - the Product strikes back . Whether it means creating top of the funnel (ToFu) through virality, guiding users through their activation and engagement journeys, or taking the first steps in an assisted sale, at product led growth companies, the product is the lead actor, and sales, marketing, and engineering are the supporting cast. We live in the era of product-led growth For those in the audience paying attention, this should come as no surprise. The continuing trend of a closing gap between the buyers and sellers of software was always going to result in both ultimately coming together. The era of the top-down determination of which software teams used left no room for experimentation among end-users and sometimes resulted in 7-figure software gathering dust on shelves. But why now? There are evolutions and then, there are revolutions. In the wake of Covid-19, we witnessed two years' worth of digital transformation in just two months. Every company is now becoming a software company, and a million applications are just a few clicks away. In a world where anyone can take your software for a spin, end-user adoption and retention are functions of how much value your application can deliver over your competition. And in the rainforest, only the very best survive. Product-led growth emerged as a Darwinian alternative mirroring survival in the wild. A low-risk environment where teams discovered and experimented with software was always likely to forge resilient strains of the product. Only the best emerge from the crucible because they provide the best experiences to the end-user. Coupled with Sales & Marketing becoming way more expensive than ever before, it only made sense for the product to become the protagonist, and for Sales and Marketing to step into supporting roles. A self-serve product is no longer a nice-to-have. It is an existential need. There are only two kinds of SaaS companies in the world today: Those who ARE product-led And, those who WANT TO BE product-led Product Led Growth (PLG) is the natural next step in an evolution that's been playing out for decades. And as for the other millions of software companies that are yet to be born into this world, the ink is dry on the product being the driver of growth. What separates the best from the rest How do you spot the best Product Led Growth companies in the wild? Canva, , , Calendly. What seperates them from the rest of the pack? Figma Notion Imagine running an algorithm to adapt your product to optimize for end-user delight, and imagine all your competitors doing the same thing. In this highly competitive space where the margins are so thin, the clear winners are those who go the extra mile. But what does that extra mile look like? Over the last year, we've spoken with 100s of PLG companies - all the way from seed stage to public market giants. And after all those conversations, our pattern matching spidey senses were tingling ✨ In classic McKinsey fashion - the following are ! the top 3 characteristics that separate the best from the rest P.S. You can take a consultant out of McKinsey but not McKinsey out of a consultant amirite? #1: The best PLG companies tell the best stories 💭 Imagine yourself as a young PLG company. You're up against the giants. The behemoths. You're up against Amazon! You need to convince merchants that your buggy little tool that helps them create e-commerce stores online is the solution to all their problems. It's going to change their lives. They ask you why they should believe you? You have no customer references, no reviews and Amazon works well enough. The way the best PLG companies approach this problem is step 1 to why they succeed. They appeal to their customer's emotions. Their core drivers. A merchant on Amazon often feels constrained to the fate the overlords have planned for him or her. And those are exactly the heartstrings a young Canadian startup called tugged on in its early days. It promised its customers independence! Shopify - “We help people by making it easier to start, run, and grow a business.” mission appeals to the indie spirit and freedom from being under someone else’s thumb. (looking at you, Spotify 😒) Shopify achieve independence Shopify’s - "Our mission is to ." In typical Patrick Collison fashion, Stripe appealed to the DNA of every young Silicon Valley founder building an online business. Stripe increase the GDP of the internet - “Help the world .” It's easy to take impact on your calendar for granted. But their mission is a stark reminder of the impact this glorious little Chrome extension can have on our lives. Clockwise make time for what matters Clockwise's - "...to make it so , we will create tools, networks, and incentives for coding at ever-larger scale." While 'code-search' is a succinct way to describe to a developer, their mission applies to all. Sourcegraph everyone can code Sourcegraph founders have said often that their long-term vision is to . Postman - Postman's add "net new developers" to the world - "Our mission is to by enabling anyone to build the tools that meet their needs." vision is much more grandiose than being the spiritual successor to MS Excel. Airtable democratize software creation Airtable's just - "...with a mission to anything and publish anywhere." Canva's narrative is the democratization of design. Canva empower everyone in the world to design #2:️ The best PLG companies deliver value... blazingly fast ⚡ The attention economy is a zero-sum game of time In the attention economy, every product is competing with some other product for end-user attention, and those who can deliver value without beating around the bush stand to pull ahead of the pack. Here's an example: How much time do you think it takes to build a Christmas card on Canva? Well, we timed it. Even without an account, it takes 🤯 to fire up the website, find a template, customize it, and export it to your desktop. all of 52 seconds Canva - From discovery to design to value in 52 seconds! ⚡️ However, not all tools can deliver value in 52 seconds. And that's totally okay! Some inherently serve more complex use cases than Canva, but even then, product-led growth companies guarantee that the outcome is made visible to the end-user within minutes of discovering the product. This is typically done through template galleries. Be it , , , or , at no point does the end-user have to engage in mental gymnastics to imagine the value proposition. Algolia Airtable Retool Notion Case in point: Here's 's standard template gallery. The decision to create a user-generated (UGC) templated gallery was notably the inflection in journey from growing fast to growing at a breakneck speed! Notion Notion's Sometimes, the amount of time it takes for a product to deliver value is high by design. Consider , where the value proposition is time itself. How do you deliver value fast? does this in two ways: Clockwise Clockwise By quantifying its potential impact upfront - in #hours of focus time created for your coworkers With a top-of-the-line onboarding experience. #3:️ The best PLG companies sell scientifically 🧬 Enter the product qualified lead (PQL) Having an abundance of product-usage data enables PLG companies to qualify leads based on product-usage signals. PLG companies move SQLs and MQLs into the background and instead focus on product qualified leads (PQLs): For Slack, a PQL was teams who sent over 2,000 messages in 2 weeks For Dropbox, it was users who uploaded 1 file in 1 hour of signing up And for Hubspot, it was accounts that used 5 (out of 25) new features within 60 days. Scientifically expressed, a PQL or a Product-Qualified Lead can be thought of as a who a certain in a certain segment of personas performed a set of actions number of times amount of time PQL definitions can vary based on the nature of the product, but at its core, it remains a function of a combination of persona, actions fired, # of times, and time period. PQLs are typically derived from a mix of product signals, demographic signals (country, size of company, industry, etc.), and technographic signals (software stack used). For , the product-usage signals considered for PQLs are a mix of activation events like creating components, inviting colleagues, exporting designs, etc., or propensity-to-pay-events like hitting the paywall, checking the pricing page. Figma Whereas, for the product-usage signals are instead # meetings created, # of concurrent meeting attendees, # of integrations used, # of features used, # of referrals sent, etc. Hopin Okay so PQLs are the answer to all our problems, right? Wellllll... we've not been entirely honest with you. While PQLs are easy to conceptualize, they're notoriously hard to implement 😬 Enter the (complex) infrastructure that enables the best PLG companies In the fast-paced, data-backed world of PLG, is where most companies falter. At over 95% of the companies we spoke with, the "PQL implementation project" was lost to a spider web 🕸 that can only be described as DIY hell! 👹 A chaotic criss-cross of spreadsheets, CRMs, product usage information held together by a disparate set of Excel or Google Sheet files... yikes! implementation of PQLs But the OGs of the PLG world like Slack, Asana, , and didn't live in this hell! Shopify Figma These companies have and have created an intricate infrastructure of engineering and data science, to implement and operationalize PQLs! gone the extra mile Just ask the sales team at who credit a large part of their success in getting to $100M ARR to their sales infrastructure (below) Figma In an ideal world, every Product-Led Growth company has the internal capabilities, the bandwidth, and the resources to set up a flow where a data science team defines PQLs in tandem with a data engineering + platform engineering team that funnels data from Billing, CRMs, Marketing, and Product Analytics databases while simultaneously streamlining the whole process to funnel the leads back into the CRMs. But the world is far from ideal, and not every company is as far along in its PLG journey as Slack, Asana, , or . Shopify Figma One tool to rule them all With tech transformation stepping into high gear in the wake of the pandemic and the clairvoyance that product-led growth is the way forward, most SaaS companies will be PLG companies and what's more... most of them are yet to be born into this world. For the up-and-coming PLG company, time and resources spent building infrastructure as complicated as Slack's or is time and resources better spent on the core product. Figma's We built to take this daunting task off the hands of companies who face the dilemma of massive top-of-funnel growth coupled with the inability to set up this massive infrastructure. Infrastructure that is indispensable to converting users from free to paid plans. Toplyne Setting up something similar to the scary stack shown here is a nightmare for a young PLG company, but all of this heavy lifting just sounds like ... with extra steps. 😉 Toplyne Here is a list of that have been pioneers of the product-led movement. ~300 companies Also published on: https://www.topofthelyne.co/p/product-led-growth-what-makes-canva

Why Canva, Figma & Shopify are Exploding like Mentos in Diet Coke

Recently I stumbled upon an incredible piece of marketing. Ruchin Kulkarni, Co-Founder of Toplyne.io shared a manifesto on why they are building a CRM for product-led-growth (PLG) companies. It was a masterclass in strategic storytelling. Here’s why. Five steps to strategic storytelling: When Andy Raskin published his article “The Greatest Sales Deck I’ve Ever Seen” he outlined the five steps to master strategic storytelling. They can be summarized as: Name a Big, Relevant Change in the World. Show There’ll Be Winners and Losers. Tease the Promised Land. Introduce Features as “Magic Gifts” for Overcoming Obstacles to the Promised Land. Present Evidence that You Can Make the Story Come. True Ruchin’s manifesto, on how software companies will acquire customers in a world dominated by PLG, is a masterclass in using the five-step framework to position products and win the hearts of a market. A new world order Ruchin first states there is a new world order by leading with this explanation: Instead of going straight into explaining what their product does, Ruchin tells a story to set the context as to why their product needs to exist. He then explains how this new world order came to be: Outbound sales died in the 2000’s ☠️ Inbound took off in the 2010’s 📈 Lower software costs mean freemium has become a highly successful acquisition model 🚀 Ruchin has presented the new world order and given context as to why it exists, and I understand there will be winners and losers. Those who fail to adopt PLG will lose. The promised land is presented as a place where you can acquire customers without cold calling prospects, you can acquire them cheaper than before, and freemium users will be early evangelists of your product since they’re already using it. Okay, so I’m hooked. PLG is the way to go. No more cold calls. Cheaper CAC. Evangelized customers. Easy upsell. Crushed competition. So, now what? Well, the promised land isn’t easy to get to. There are obstacles and challenges to overcome. too many signs up for sales people to manage conversion rates of free users to paying customers are low salespeople waste time on non-converting leads Time for a solution Ruchin then explains how the current winners of PLG use hyper segmentation to determine which customers should be contacted by a sales person to convert to a paying customer. And gives examples of Slack, Dropbox, and Hubspot. The best companies only prioritize reaching out to leads which fall into specific criteria. But in the ‘new world’ of PLG, accessing the data required to make such decisions is hard. Incumbent CRMs were built for the old world: outbound sales = salesforce inbound marketing = hubspot product-led-growth = ???? There is no solution that helps people play in the new world order. Data on how customers use product, interact with marketing, or which payment plan they’re on are stuck in siloed platforms that don’t talk to eachother. Enter Toplyne. They: Centralize data from siloed systems across product, billing, and CRMs to give a 360 view of users Prioritize which freemium users should be contacted by salespeople and increases conversion rates The details of how the product works is irrelevant at this point. Convincing your audience of the new world order is what matters first. Mario marketing Here’s a powerful tip from Mario. People don’t buy product, they buy what product enables them to do. Good marketing explains what product helps people do, but great marketing provides the context for why these superpowers are necessary. They present the stakes of winning or losing in the new world order. Once prospects are convinced the world has changed, it becomes much easier to accept they must adapt to survive in this new environment. ⭐️ Golden Rule: Never pitch a solution to someone who doesn’t think they have a problem. Wrapping it up 🌯 Toplyne’s manifesto was a masterpiece in strategic storytelling because they: ✅ Set the context
✅ Presented a new world order
✅ Helped us visualize what success looks like in the new world
✅ Outlined the challenges and obstacles people will encounter
✅ Provided a solution to getting over those challenges I’ll end with a question: What new world order does your product help people thrive in? Also published on . https://www.rocketgtm.co/blog/product-positioning-5-easy-steps

Product Positioning: 5 Easy Steps to Strategic Storytelling

Google Search Ads in the U.S.: Why They Compose a Relevant Market

The “Why GenAI” problem It is now without question that GenAI is the transformative technology of our times. While the business world is grappling with policy, change management, and “general technophobia,” as this HBR article calls it, I see it as being similar to the early days of the internet. At first, it seemed like a novel technology. Then, people were scratching their heads at its application beyond academia, defense, and other niches, followed by an outpour of startups as infrastructure developed and costs reduced, a dot-com crash, and up to where we are today as a society that runs mostly digitally. All that is to say, GenAI has the footprint of a disruptive trend that will be integrated into all aspects of society. What remains to be seen is the pace at which this integration happens in various parts of our lives. A lack of this understanding is probably why we’re seeing businesses say that they’re unclear about investment priorities when it comes to GenAI: In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI So, businesses are unsure why they should be using GenAI even though they are interested in the technology.  I think that startups and teams working in this space have the opportunity to educate and evangelize the technology in a way that businesses understand the value directly. Topline and bottom line approach While considering this, I wanted to explore how businesses should be thinking about the technology: As a way to enhance customer value and add new revenue streams (top-line growth)


As a way to enhance operational efficiency and reduce costs (bottom-line growth) I think it's important to think of top-line and bottom-line growth when adopting GenAI. For businesses, it helps to be very clear on what capabilities of the technology they need to hit the specific growth area. Topline GenAI Businesses are often looking to add new lines of revenue by innovating with their products and services. If a business has customers that have these needs, then GenAI is a good fit to solve those needs and add a new source of revenue to them: Customers want to synthesize the information they create on the business’s platform
Customers want to search and find information without having to do mental gymnastics
Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message.
Customers want to be less involved in doing mundane and routine tasks on the business’s platform. Some examples of topline GenAI integrations are beautifully captured by the folks at Obvious Ventures: Almost all of the products and services above have integrated GenAI to enhance the value they deliver to their customers. In doing so, these businesses seek to increase user engagement and, therefore, have a solid “why GenAI” answer. Bottomline GenAI While growing user engagement and revenue streams are always great, businesses need to sustain topline growth by thinking about their bottom line, i.e., costs. In fact, I think GenAI is most obvious for internal deployment within a business to make operations vastly more efficient and cost-effective. McKinsey agrees: We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office.\ Plenty of research has been done on what sectors have the potential to benefit, as shown in the PWC report. However, the use cases aren’t always obvious from reading reports such as these. If you are a developer or a founder, you would want to know more about the value proposition that you can build, which you will not always find in reports like these. So, let’s talk about the use cases briefly…. Any business has these needs where GenAI will be a great fit: Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads)
Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management
Assisting employees in their training, learning, and development programs
Synthesizing customer feedback efficiently
Assisting in the creation of impactful marketing assets easily If you focus on these needs and the GenAI capabilities, you should be on the right path to delivering a compelling value-prop to your potential customers in the sectors mentioned above, and possibly even more. GenAI Feature - Business growth fitness summary I’m not going too in-depth with how I rate the problems and how capable GenAI is at solving them, but I think this table is useful for thinking about what problems you’d want to solve for businesses. This will help your team craft a value-proposition that businesses find immediately valuable: GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples



Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management



Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication



Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing



Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation



Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response Takeaway So, if you are thinking about a B2B GenAI idea or are someone looking to adopt GenAI for your own business, I hope this is useful to build a mental model of where GenAI can be leveraged in your case and which specific capabilities deliver the highest ROI for you and your customers. The “Why GenAI” problem It is now without question that GenAI is the transformative technology of our times. While the business world is grappling with policy, change management, and “general technophobia,” as this HBR article calls it, I see it as being similar to the early days of the internet. At first, it seemed like a novel technology. Then, people were scratching their heads at its application beyond academia, defense, and other niches, followed by an outpour of startups as infrastructure developed and costs reduced, a dot-com crash, and up to where we are today as a society that runs mostly digitally. the this HBR article this HBR article All that is to say, GenAI has the footprint of a disruptive trend that will be integrated into all aspects of society. What remains to be seen is the pace at which this integration happens in various parts of our lives. A lack of this understanding is probably why we’re seeing businesses say that they’re unclear about investment priorities when it comes to GenAI: In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG BCG | So, businesses are unsure why they should be using GenAI even though they are interested in the technology.  I think that startups and teams working in this space have the opportunity to educate and evangelize the technology in a way that businesses understand the value directly. Topline and bottom line approach While considering this, I wanted to explore how businesses should be thinking about the technology: As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) I think it's important to think of top-line and bottom-line growth when adopting GenAI. For businesses, it helps to be very clear on what capabilities of the technology they need to hit the specific growth area. Topline GenAI Businesses are often looking to add new lines of revenue by innovating with their products and services. If a business has customers that have these needs, then GenAI is a good fit to solve those needs and add a new source of revenue to them: Customers want to synthesize the information they create on the business’s platform Customers want to search and find information without having to do mental gymnastics Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message. Customers want to be less involved in doing mundane and routine tasks on the business’s platform. Customers want to synthesize the information they create on the business’s platform synthesize synthesize Customers want to search and find information without having to do mental gymnastics search search Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message. creative creative Customers want to be less involved in doing mundane and routine tasks on the business’s platform. routine routine tasks Some examples of topline GenAI integrations are beautifully captured by the folks at Obvious Ventures: Almost all of the products and services above have integrated GenAI to enhance the value they deliver to their customers. In doing so, these businesses seek to increase user engagement and, therefore, have a solid “why GenAI” answer. Bottomline GenAI While growing user engagement and revenue streams are always great, businesses need to sustain topline growth by thinking about their bottom line, i.e., costs. In fact, I think GenAI is most obvious for internal deployment within a business to make operations vastly more efficient and cost-effective. McKinsey agrees: We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office.\ We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office .\ savings opportunities of $1.4 trillion to $2.6 trillion across operations impact on the back office Plenty of research has been done on what sectors have the potential to benefit, as shown in the PWC report. However, the use cases aren’t always obvious from reading reports such as these. If you are a developer or a founder, you would want to know more about the value proposition that you can build, which you will not always find in reports like these. So, let’s talk about the use cases briefly…. Any business has these needs where GenAI will be a great fit: Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads) Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management Assisting employees in their training, learning, and development programs Synthesizing customer feedback efficiently Assisting in the creation of impactful marketing assets easily Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads) summarizing, Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management creating Assisting employees in their training, learning, and development programs Assisting Synthesizing customer feedback efficiently Synthesizing Assisting in the creation of impactful marketing assets easily creation If you focus on these needs and the GenAI capabilities, you should be on the right path to delivering a compelling value-prop to your potential customers in the sectors mentioned above, and possibly even more. GenAI Feature - Business growth fitness summary I’m not going too in-depth with how I rate the problems and how capable GenAI is at solving them, but I think this table is useful for thinking about what problems you’d want to solve for businesses. This will help your team craft a value-proposition that businesses find immediately valuable: GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples



Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management



Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication



Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing



Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation



Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response GenAI features How big of a problem is it today? How well does GenAI natively do it? Top-line examples Bottom-line examples GenAI features GenAI features GenAI features How big of a problem is it today? How big of a problem is it today? How big of a problem is it today? How well does GenAI natively do it? How well does GenAI natively do it? How well does GenAI natively do it? Top-line examples Top-line examples Top-line examples Bottom-line examples Bottom-line examples Bottom-line examples Automated summarization Low Very well Publishers, Content platforms Business reporting, Meetings, and internal communication, Knowledge Management Automated summarization Automated summarization Automated summarization Low Low Very well Very well Publishers, Content platforms Publishers, Content platforms Business reporting, Meetings, and internal communication, Knowledge Management Business reporting, Meetings, and internal communication, Knowledge Management Creation High Good enough Marketing businesses, content creation platforms Documentation, internal communication Creation Creation Creation High High Good enough Good enough Marketing businesses, content creation platforms Marketing businesses, content creation platforms Documentation, internal communication Documentation, internal communication Synthesis of information High Very well Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning and Development, Business Analytics, Research, Feedback processing Synthesis of information Synthesis of information Synthesis of information High High Very well Very well Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning and Development, Business Analytics, Research, Feedback processing Learning and Development, Business Analytics, Research, Feedback processing Search in natural language Medium Very well Broad applicability Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Search in natural language Search in natural language Search in natural language Medium Medium Very well Very well Broad applicability Broad applicability Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Automated Actions High Additional skills and fine-tuning to be added for each new use-case Productivity use-cases, payments, home automation, community moderation Customer support, Meetings, Internal communication, Incident response Automated Actions Automated Actions Automated Actions High High Additional skills and fine-tuning to be added for each new use-case Additional skills and fine-tuning to be added for each new use-case Productivity use-cases, payments, home automation, community moderation Productivity use-cases, payments, home automation, community moderation Customer support, Meetings, Internal communication, Incident response Customer support, Meetings, Internal communication, Incident response Takeaway So, if you are thinking about a B2B GenAI idea or are someone looking to adopt GenAI for your own business, I hope this is useful to build a mental model of where GenAI can be leveraged in your case and which specific capabilities deliver the highest ROI for you and your customers.

Make Your GenAI Idea Obvious to Businesses

আপনার GenAI আইডিয়াকে ব্যবসার কাছে সুস্পষ্ট করুন

Machen Sie Ihre GenAI-Idee für Unternehmen offensichtlich

Haga que su idea GenAI sea obvia para las empresas

Rendez votre idée GenAI évidente pour les entreprises

अपने GenAI आइडिया को व्यवसायों के लिए स्पष्ट बनाएं

GenAIのアイデアを企業にわかりやすく伝える

귀하의 GenAI 아이디어를 기업에 분명하게 알리십시오

Torne sua ideia GenAI óbvia para as empresas

Сделайте свою идею GenAI очевидной для бизнеса

GenAI Fikrinizi İşletmelere Açık Hale Getirin

Làm cho ý tưởng GenAI của bạn trở nên rõ ràng với doanh nghiệp

让你的 GenAI 理念对企业显而易见

Look, I don't know where you fall on the spectrum of "does emulation promote piracy?", but the idea that a video game company like Nintendo can let off its army of lawyers on a bunch of hobbyists/programers/geeks like a pack of hungry wolves and force them to shut down AND pay over 2 million in dollars just because it thinks emulation promotes piracy should alarm any and every one in the tech space. To quote Steam Founder Gabe Newell: "Piracy is not a pricing issue. It’s a service issue." Amen to that, brother, AMEN! Microsoft's Xbox understands this. Sony's PlayStation understands this. But somehow, the memo was lost on Nintendo which thought it was absolutely ok to force Tropic Haze, the company behind Switch emulator Yuzu and 3ds emulator Citra, to shut down its websites, take down its code repositories AND pay $2.4 million because, apparently, its software allowed people to illegally play Zelda. Nevermind the fact that thousands of people legally pay for Nintendo titles and emulate them on a different hardware because it plays better or because it provides more accessibility. But of course, that's the kind of rhetoric that would fall on deaf ears at Nintendo HQ. Instead, you have Nintendo coming out of the woods to protect its iNtElLeCtUaL pRoPeRtY whenever someone makes a fan project, a ROM website full of abandonware titles, or literally dozens of instances where people just want to do cool and different things with its video game titles. Ironically, except for a handful of titles that Nintendo offers through its online subscription service, there is no "legal" way to play games from the yesteryears without actually emulating them. Emulating video games also ties into video game preservation, but that's a whole different topic altogether. On top of that, Nintendo won't even port its major titles to the PC. Perhaps because it doesn't want to share revenue with others OR it wants to keep selling the Switch consoles. Whatever the case may be, it still SUCKS that Nintendo would just blatantly shut down a project like Yuzu. In some ways, one could argue that this is an attempt by one of the most successful companies in the world to make an example out of a project that is born out of passion. But what does that get the company ultimately? It's already massively rich, and all it's achieving out of these intimidation tactics is to scare off gamers and those who create emulators for them. There's no telling where the scales will tip for emulators for older Nintendo consoles like the Wii or the Wii U, but it wouldn't be surprising if more and more developers decide to not create emulator software because they don't want to fall foul of Nintendo's legal antics. So, yeah. Nintendo sucks and this is the world we live in. 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. Biden Administration to Dole Out Billions in Federal Grants to Chipmakers🤑🤑 The who's who of the semiconductor manufacturing industry have put a gun to the US government's head and are trying to milk billions of taxpayer money to build their projects on American soil. OK, that's a bit of an exaggeration. The truth is that the Biden administration really, REALLY wants companies like Taiwan Semiconductor Manufacturing Company, Intel and Micron Technology to build their products in the US, so much so that it's willing to throw billions of dollars at them. How much, exactly? Well, the US government plans to dish out over $28 billion in federal grants to each of the companies plus Samsung Electronics to entice them to boost semiconductor manufacturing in the United States. Taiwan Semiconductor Manufacturing is expected to win more than $5 billion in federal grants to support the development of two fabrication facilities in Arizona. Topline numbers for the other companies continue to fluctuate but an announcement for the major advanced chipmakers is expected by the end of this month. Keep your eyes peeled for that development. Elon Musk Takes Exception to OpenAI's Company Charter🤖 Wow, it's been quite a while since we reported on Elon Musk. But we're happy to let you know that the world's richest man is still up and about with his antics and this time, it involves his old nemesis Sam Altman's OpenAI. Yes, everyone's favorite billionaire has decided to hold OpenAI's company charter to court. He’s arguing in a lawsuit that the (former?) non-profit company, of which he apparently was a co-founder, abandoned its charge to benefit humanity by developing powerful artificial intelligence. Instead, the company rolled out generative AI products like ChatGPT that were essentially Microsoft products to make big bucks. Of course, Musk knows all about benefiting humanity given that he owns Tesla; in fact, so concerned was he with humanity's benefit that he decided to burn his own money to buy X (formerly known as Twitter) for reasons that are not yet clear (but should be in a decade or two). OpenAI is certainly not having Musk's BS, so it has hired a law firm that Elon Musk really hates, to have his lawsuit thrown out. Just another day of tech bros duking it out against each other. Sigh! In Other News.. 📰 Ethereum Finalizes 'Dencun' Upgrade, in Landmark Move to Reduce Data Fees — via CoinDesk
Startups are hiring fewer workers and paying out less in equity comp — via TechCrunch
EU approves landmark AI law, leapfrogging US to regulate critical but worrying new technology — via CNN
US House passes bill to force ByteDance to divest TikTok or face ban — via Reuters
The road map to AI's next level could be nature — via Axios
Palantir CEO says short sellers bet against great companies ‘so they can pay for their coke’ — via CNBC – And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Look, I don't know where you fall on the spectrum of "does emulation promote piracy?", but the idea that a video game company like Nintendo can let off its army of lawyers on a bunch of hobbyists/programers/geeks like a pack of hungry wolves and force them to shut down AND pay over 2 million in dollars just because it thinks emulation promotes piracy should alarm any and every one in the tech space. it To quote Steam Founder Gabe Newell: "Piracy is not a pricing issue. It’s a service issue." Amen to that, brother, AMEN! Microsoft's Xbox understands this. Sony's PlayStation understands this. But somehow, the memo was lost on Nintendo which thought it was absolutely ok to force Tropic Haze, the company behind Switch emulator Yuzu and 3ds emulator Citra, to shut down its websites, take down its code repositories AND pay $2.4 million because, apparently, its software allowed people to illegally play Zelda. Nevermind the fact that thousands of people legally pay for Nintendo titles and emulate them on a different hardware because it plays better or because it provides more accessibility. But of course, that's the kind of rhetoric that would fall on deaf ears at Nintendo HQ. Instead, you have Nintendo coming out of the woods to protect its iNtElLeCtUaL pRoPeRtY whenever someone makes a fan project, a ROM website full of abandonware titles, or literally dozens of instances where people just want to do cool and different things with its video game titles. Ironically, except for a handful of titles that Nintendo offers through its online subscription service, there is no "legal" way to play games from the yesteryears without actually emulating them. Emulating video games also ties into video game preservation, but that's a whole different topic altogether. On top of that, Nintendo won't even port its major titles to the PC. Perhaps because it doesn't want to share revenue with others OR it wants to keep selling the Switch consoles. Whatever the case may be, it still SUCKS that Nintendo would just blatantly shut down a project like Yuzu. In some ways, one could argue that this is an attempt by one of the most successful companies in the world to make an example out of a project that is born out of passion. But what does that get the company ultimately? It's already massively rich, and all it's achieving out of these intimidation tactics is to scare off gamers and those who create emulators for them. There's no telling where the scales will tip for emulators for older Nintendo consoles like the Wii or the Wii U, but it wouldn't be surprising if more and more developers decide to not create emulator software because they don't want to fall foul of Nintendo's legal antics. So, yeah. Nintendo sucks and this is the world we live in. 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. You’re reading HackerNoon's Tech Company News Brief , a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. Tech Company News Brief Tech Company News Brief here complete Biden Administration to Dole Out Billions in Federal Grants to Chipmakers🤑🤑 Biden Administration to Dole Out Billions in Federal Grants to Chipmakers🤑🤑 The who's who of the semiconductor manufacturing industry have put a gun to the US government's head and are trying to milk billions of taxpayer money to build their projects on American soil. OK, that's a bit of an exaggeration. The truth is that the Biden administration really , REALLY wants companies like Taiwan Semiconductor Manufacturing Company, Intel and Micron Technology to build their products in the US, so much so that it's willing to throw billions of dollars at them. really How much, exactly? Well, the US government plans to dish out over $28 billion in federal grants to each of the companies plus Samsung Electronics to entice them to boost semiconductor manufacturing in the United States. Taiwan Semiconductor Manufacturing is expected to win more than $5 billion in federal grants to support the development of two fabrication facilities in Arizona. Topline numbers for the other companies continue to fluctuate but an announcement for the major advanced chipmakers is expected by the end of this month. Keep your eyes peeled for that development. Elon Musk Takes Exception to OpenAI's Company Charter 🤖 Elon Musk Takes Exception to OpenAI's Company Charter Wow, it's been quite a while since we reported on Elon Musk. But we're happy to let you know that the world's richest man is still up and about with his antics and this time, it involves his old nemesis Sam Altman's OpenAI. Yes, everyone's favorite billionaire has decided to hold OpenAI's company charter to court. He’s arguing in a lawsuit that the (former?) non-profit company, of which he apparently was a co-founder, abandoned its charge to benefit humanity by developing powerful artificial intelligence. Instead, the company rolled out generative AI products like ChatGPT that were essentially Microsoft products to make big bucks. Of course, Musk knows all about benefiting humanity given that he owns Tesla; in fact, so concerned was he with humanity's benefit that he decided to burn his own money to buy X (formerly known as Twitter) for reasons that are not yet clear (but should be in a decade or two). all OpenAI is certainly not having Musk's BS, so it has hired a law firm that Elon Musk really hates, to have his lawsuit thrown out. really Just another day of tech bros duking it out against each other. Sigh! Sigh! In Other News.. 📰 Ethereum Finalizes 'Dencun' Upgrade, in Landmark Move to Reduce Data Fees — via CoinDesk Startups are hiring fewer workers and paying out less in equity comp — via TechCrunch EU approves landmark AI law, leapfrogging US to regulate critical but worrying new technology — via CNN US House passes bill to force ByteDance to divest TikTok or face ban — via Reuters The road map to AI's next level could be nature — via Axios Palantir CEO says short sellers bet against great companies ‘so they can pay for their coke’ — via CNBC Ethereum Finalizes 'Dencun' Upgrade, in Landmark Move to Reduce Data Fees — via CoinDesk CoinDesk CoinDesk Startups are hiring fewer workers and paying out less in equity comp — via TechCrunch TechCrunch TechCrunch EU approves landmark AI law, leapfrogging US to regulate critical but worrying new technology — via CNN CNN CNN US House passes bill to force ByteDance to divest TikTok or face ban — via Reuters Reuters Reuters The road map to AI's next level could be nature — via Axios Axios Axios Palantir CEO says short sellers bet against great companies ‘so they can pay for their coke’ — via CNBC CNBC CNBC – And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings

I Hate Nintendo

আমি নিন্টেন্ডোকে ঘৃণা করি

Ich hasse Nintendo

odio nintendo

Je déteste Nintendo

मुझे निनटेंडो से नफरत है

私は任天堂が嫌いです

나는 닌텐도를 싫어한다

Eu odeio Nintendo

Я ненавижу Нинтендо

Nintendo'dan Nefret Ediyorum

Tôi ghét Nintendo

我讨厌任天堂

In case you haven't been keeping up with the latest economic news, the US recently achieved its so-called soft landing, avoiding recession and seeing continued growth. That doesn't mean, however, that the pandemic and its economic upheaval didn't leave a lasting mark on the US economy. In fact, it wrought quite a few changes that seem to now be permanent features of the economic landscape. For one, it made remote and hybrid work not only acceptable for any business that wants to attract top talent. For another, it drove an of AI and automation technologies. But for the average worker, it did something far more profound—it created a . but an absolute necessity explosion in the adoption record wave of entrepreneurship The thing is, however, if you dig down beneath the topline entrepreneurship data, you'll find another interesting tidbit. It's that the majority of the new businesses in the US—a full eight in ten—consist of an owner and zero employees. In other words, what we're really seeing is a wave of taking root. As a solopreneur myself, that's something I can immediately identify with. And as a dedicated technology evangelist, I thought it would be helpful to help others wishing to take the solopreneurship plunge increase their odds of success. With that in mind, here are four types of digital tools every solopreneur should have in their arsenal before launching a solo business. solopreneurships 1. A Unified Communications Tool One of the biggest challenges associated with running a solo business is that you never have enough bandwidth to do your actual revenue-generating work while keeping in touch with clients and potential clients. As it turns out, though, there's a solution for that. It's called a . Unified Communications as a Service (UcaaS) platform They're cloud-based solutions that put multichannel communications tools like voice, video, SMS, Email, and chat into a single interface. This eliminates the time you'd otherwise waste switching from app to app, trying to make sure you don't leave a customer hanging when they reach out to you. Plus, there's a UcaaS platform for virtually every communication style. And due to intense market competition, you won't go bankrupt by subscribing to the one you want. Personally, I use , but that might be overkill for someone who's just starting out. For a great entry-level option with room to grow, check out , too. NextivaONE 8x8 2. Accounting Software If there's one thing you can't afford to do wrong as a solopreneur, it's keeping track of your business's finances. This is because new solopreneurs often struggle with setting prices and understanding operating costs. Without rock-solid accounting data to guide your decisions, you run the risk of without even realizing you're doing it. operating at a loss The good news here is that many of today's best accounting software providers have products and subscriptions aimed at solopreneurs—and they're quite inexpensive. There are even some, like the solution offered by , that don't cost anything at all for just the essentials. However, other options, like the one from , might be a better fit with its beginner-friendly tools and interface. Wave Accounting FreshBooks 3. A Document Management Solution Next is a digital tool type that advisory articles like this one often skip over. However, it's one that you will certainly miss if you don't have it: a solution. The reason is simple. As a solopreneur, you're responsible for managing every aspect of every client project. That includes the intake of customer data, documents, and other assets. Plus, you have to keep them organized and available at a moment's notice. You'd be shocked at how much of your time this single task will take up. document management To avoid wasting your precious time, you can choose and adopt a document management solution that suits your needs. If you do the kind of work that only requires basic client data collection, you might use something like a combination of for file sharing and for information intake. However, if you're offering something like legal/accounting services or web design, where client information exchange is more important, sensitive, and/or intense, check out a instead. Dropbox Google Forms solution like Gatheroo 4. Project Management Software Another digital tool you won't want to live without as a solopreneur is software. It's the only way you'll be capable of keeping track of multiple jobs at once without getting your wires crossed. Here again, you won't have any difficulty finding a solution that fits your specific line of work and preferred workflow. project management For starters, you could opt for a . It's infinitely adaptable, and you can configure it to support virtually any kind of project or task. Or, you can go with a tried-and-true solution like , which you may already be familiar with if you've worked for a mid-sized to large business in the past decade. It even has a free tier, which might be perfect for you as you begin your solopreneurship journey. solution like ClickUp Trello Let Your Solopreneur Adventure Begin If you arm yourself with the right tools and assistive technologies before you launch your solo venture, you stand a better chance of having a rewarding experience, both professionally and financially. Plus, you'll have an easier time managing your workload and avoiding burnout, which is an all-too-common way for solo businesses to come to an end. I can't tell you how much I wish someone had tipped me off to how much easier running a one-man operation could be with the above tools. So, enjoy the benefit of my experience, and best of luck as you begin your solopreneur adventure!

4 Must-Have Types of Digital Tools for New Solopreneurs

If you haven’t been living under a rock (or been on this platform at all), you may (read: will) be aware of the following events: Elon Musk bought Twitter for $44bn. He then renamed it X, a throwback to the X.com domain he has owned since the 90’s and the precursor to PayPal Now that that’s covered, some thoughts on why he did this, where it’s headed and how every user might get involved. Why Twitter? The party line in the lead-up to and ever since the Twitter takeover has been that Twitter is the world’s “digital town square”. Prior to acquisition, some stated concerns that the purchasing party had with the platform were its restrictions on free speech. In an ideal world, the town square is a place for open communication, serendipitous interaction and open commerce. Under the prior regime, Twitter displayed restricted characteristics of the first two features of the town square. My bet in writing this is that the vision for X will try and comprehensively and unapologetically service all 3. Twitter has a number of characteristics that make it uniquely ripe for a platform mission of this scale. It is a unique source of real-time news - no other platforms (with possibly the exception of TikTok) are as uniquely geared to delivering a stream of takes as soon as action happens in the real world. As far as social platforms go, it is a unique source of understanding people’s unconscious thoughts - there is less filter (and substantially more shitposting) on Twitter than other platforms. These two characteristics bear a lot of similarities to WeChat, a platform for which Musk has expressed in the past. Similar to Twitter, WeChat’s origins were in an unfiltered* feed of people’s messages benefitting from ‘gotta-have-this’ network effects. admiration Through this stream of low-effort, low-barrier user generated content, WeChat was able to easily become a place for the breaking of news in real time. These elements created an extremely valuable network of data. The logical next step from here was to introduce the launch of ‘mini-apps’ - platform-native applications built by third-party developers to utilise WeChat’s network to offering augmented possibilities within the platform to compete with, well, every other tech company. X could be a vision of WeChat built for a more independent, more global audience. As such, while the platform structure may remain similar, the apps may look very different. This, in my opinion, is what so many fail to understand about western ‘replications’ of these super-apps being ill-fated (excuse the meme below). What’s been done so far? Since Elon’s takeover, a few defining changes have been made to the platform that may be indicative of longer-term strategic moves. Creators can charge whatever fee they want for their ‘true fans’ to access exclusive content. Creator Subscriptions. A decentralised form of moderation that allows users to provide additional context to potentially misleading Xeets. This Community Notes. Twitter extended the long-form tweet limit to 10,000 characters. It also expanded its video capabilities, as best exemplified by Tucker Carlson’s relocation of to the platform. Extended Content Limits. his content Payouts to creators based on their contribution to platform ad revenue, denominated as a % of ad revenue derived from ads placed in the replies to their Xeets. Ad-sharing agreements. This is the big one. Reducing the prominence of scams and bots is the obvious first-order effect. However, it also could serve a broader economic purpose. The first generation of consumer-ready LLMs benefitted from having free reign over the internet to train their models over whatever data they wanted. This has become the subject of major copyright cases. X’s API restrictions force those looking to scrape the platform for data to pay for the contributions X’s rich dataset makes to their LLMs (or similar), creating a fairer economic model for all involved. The logical next (creator-friendly) step would be, as they have done with ad sharing, to payout network participants for what percentage of their data is being paid for as part of these API charges. API Restrictions & Rate Limits. What’s next? I don’t hold out hope for X to become a fully decentralised protocol in the manner of the Dorsey-era Bluesky initiative. However, given Elon’s ambition starting this foray into social, I do expect it to become platformised. This (fairly aggressive) assumption is based off of two things: 1) Elon’s long-stated ambition to build a super-app of some variety, and 2) the re-application of the X.com domain to open up pursuit of old goals. ‘X-as-a-Platform’ in this sense isn’t referring to you just being able to Xeet whatever you want, whenever you want on the platform (as has always been the case). Instead, I refer to either of these platform definitions of choice: A venue for creating and facilitating interactions between different groups (e.g. buyers and sellers, but not limited to this) A stage from which applications can be built. Based on what we’ve seen and heard so far, a lot of the plumbing will be done by Twitter themselves. Already we have heard of them allude to in-house features that will “allow people to conduct their entire financial world” from within the platform. It has arguably made the greatest leap of any mass-scale social platform in terms of rewarding creators via the ad revenue sharing system introduced this month. As talented as the remaining engineers at Twitter may be, the scope for their delivery of novel products & tools is no match for the widespread creativity and productive powers of As such, I believe that to build the ‘financial world’ of Musk’s imagination, a real platform strategy is in order. Where is the low hanging fruit to be built for this ecosystem? What might be built for the X economy? Novel transaction rails for novel interactions The idea of “conduct(ing) your entire financial world” from within X is a headline grabbing concept. To enable this first and foremost requires rails for processing these financial transactions. Cryptocurrencies (coin-agnostic) offer a perfect manner for doing this in a censorship-resistant, self-custodial and programmable fashion for users on the platform. Payments for creator subscriptions or Reddit-style rewards do not have to be done via some meaningless platform-native token, but rather in the self-custodial currency of choice that users can hold and transact via X. Rather than having to deal with the muddle of SWIFT codes, bank account numbers or even wallet addresses, users will be able to direct transactions simply via account handles. Feature Markets Got a request for feature on Twitter? Say, for example, I want to be able to create debate markets within comments sections, whereby comments are sorted by Agree/Disagree sentiment to a statement tweet. The most liked comments on either side of the argument are presented side-by-side at the top. I want the winner of this debate to be able to extract a reward. Even if it is not a central priority for Twitter to develop internally or rollout across the platform, why shouldn’t the developer be able to deploy these kinds of features in a sandbox environment to a group of opt-in users that are interested in seeing these kinds of i) experimental features or ii) balanced idea markets? After deploying from a developer suite (which could be a paid service based on its potential economic upside), developers stand to win rewards based on any of i) revenues from any features that they create with some kind of financial element or ii) the portion of total Twitter revenue derived from engagement with these features (including ads). makes this idea sound a bit more novel than it really is - the reality is that they are extremely analogical to marketplaces on platforms like WordPress, Shopify or even Google extensions. For whatever reason, such functionality has been limited to date in the world of social. Outside of social, it is the backbone of gargantuan markets: while maybe a bit broader in scope as a platform, Apple’s App Store contributed $86.8bn in platform revenues in 2022. Feature markets IP & Data Licensing Mechanisms Beyond the ad revenue sharing scheme that is in place at the moment, there should be other ways for creators to monetise their content (particularly for creators whose content is ripe for being repurposed elsewhere). Further, in order for developers to benefit from the potential programmability of X’s data, there need to be responsible, legally viable frameworks in place for use of customer data. Arweave’s Universal Data License ( ) represents a good proxy of these kinds of systems in that it allows creators to dictate how any given piece of content can be used or remixed whilst earning rewards when it is used for external purposes. UDL Custom algorithms For all the work that X’s algorithm puts into keeping you delighted, it is inevitable that everyone gets annoyed with their stream. If I have a day where I want to look at golf content, I don’t want to be tagged as wanting to view that for the rest of my life. If I’m using Twitter for professional purposes but got easily distracted by entertainment news every now and then, I don’t want these 5-minute forays to destroy Twitter as a professional tool for the rest of my career. Sure, features like Lists etc all serve to give the user input into what enters their feed, but this then has the effect of limiting the amazing serendipitous post that pops up in your feed that makes Twitter such a great platform. Enter custom algos. Developers can play around and deploy different thematic algorithms for users to test. In one example, developers may able to create a proxy of what any given influencer’s feed may look like. I, for one, would be very curious to see what Elon’s doomscrolling experience looks like compared to mine. At the cross-platform level, this would likely be a read-only function. It is hard to imagine X giving up the reigns to what kinds of content get rewarded or distributed at scale. However, once advanced community tools are built-in, why not allow developers to play with different reward mechanisms for different communities? The average Crypto Twitter user likely gets value out of very different styles of Tweets/replies than, say, someone on Movie Twitter. Once these communities can be effectively delineated, a lot of design space opens up for novel reward mechanisms. To date, Twitter’s own pre-Elon development has been the first and only mover in developing these kinds of ‘algorithm marketplaces’. This will be an interesting space to watch. How big this market could be is dependent on your views towards what portion of the $231bn annual social media market is determined by algorithms versus other factors such as marketing or community. Bluesky Cogsec Apps Ever since the events of 2016, misinformation and informational integrity have been core themes in the social space. How can we better protect people against this kind of misinformation? Do people even want to be protected or have balanced opinions? Creating an open market for cogsec applications that act as a form of ‘cybersecurity’ against misinformation would be a boon to any platform’s claims of being a bastion of free speech. These tools can adopt a variety of different features to de-bias certain pieces of information. Much of this is very speculative, and would require user opt-in to avoid being entirely Orwellian: content filters to repackage tweets with less emotional appeal whilst maintaining integrity of the original message or reputational slashing for accounts that are repetitively tagged as producing misleading content by Community Notes contributors are two examples. Community Notes is actually a step in the right direction as far as cogsec goes on the platform - the most obvious extension here is to allow (and possibly even encourage) users to moderate i) Community Notes themselves and ii) any measures taken by X as a platform to censor certain content on the platform. Beyond this, the idea of rewards for reputable and reliable user contributions to Community Notes seems a likely extension of this first-phase of platform revenue sharing mechanisms. Commerce Infrastructure For most, buying useless shit is a key tenet of the operation of their financial world. As such, ‘arm-the-rebels’ style commerce tools are likely to creep into the X platform (apologies, Shopify). I believe that, first and foremost, we are not far away from centralised features that allow people to sell directly from their profile or embed checkouts within tweets. Better Community Tools Community building on X today is mostly a function of its content algorithm. I like startup content, I fall in with startup people on Twitter. How can this be made more proactive, and how can these small subcultures on the platform create their own mini-apps without having to secede from the platform to places like Discords or Slacks? To the above, I think it is likely that X will enter the work communications space for remote teams. With this comes the opportunity to rebuild the kind of plugins and additional apps seen on Slack, Discord and their ilk from the ground up. Prediction markets & other voting mechanisms In these early days of Elon-era Twitter/X, we have seen many company-level decisions be informed by simple Yes/No polls from his handle. How much further can these kind of trends go across a broader range of potential applications? Prediction markets have been a polarising topic since the first large scale, skin-in-the-game markets were launched around the time of Ethereum (Augur being the first mover in this case, followed by successors such as Polymarket, and to name a few). Manifold Markets Kalshi If these products failed to achieve breakout growth, what makes a prediction market on X any different? The first factor is . The aforementioned existing prediction market platforms are used by small, esoteric audiences seeking gains from supposed informational advantages. X.com has an inbuilt audience of ~330mm monthly active users who are on the platform because they have opinions (or want to find new ones). What better way to prove your value in the market of opinions than creating financially staked markets from it? distribution The second factor is X’s now proven ability to . Similar to the ad revenue sharing scheme, prediction markets features on Twitter could work by allowing users to create their own markets based on assertions they make in their Xeets. If users make sufficiently provocative or challenging assertions, one would assume that the prediction markets for these assertions are particularly hot and might allow the original author to take a share of the market for their provoking thoughts. share revenue with its creators This creates a financial incentive for creators to publish thought-provoking mini-content on the platform, and allows ‘reply guys’ to also get in on the action by providing opposing/supporting evidence to influence their side of the prediction market. In this model, these prediction markets can be organised more organically and allow for second-order pieces of information supporting or opposing the cause to play an influential role in market outcomes. Novel platform analytics One of the things that web3 has done best (and has been best for crypto twitter) is the depth of analytics openly available to anyone in the space. Data from block explorers can be repackaged and contorted into easily readable formats for anyone interested to easier digest exactly what is happening on chain. Nothing like this has achieved success at scale for social. Sure, anyone with the requisite level of skill can develop their own sentiment analysis programs or data visualisation techniques based on what is happening, but platforms don’t make it easy for the everyday user to play around with and explore different elements of platform activity. Why not introduce -style analytics platforms for granular data on social platform activity? At the topline, there would be full transparency around platform activity - for example, how likes/RTs/replies are distributed among power users of the platform or how bot activity is trending over time. Where it gets very interesting is if these platforms can then open-source sentiment analysis tools. In this world, we could see analysts developing publicly available dashboards displaying changes in tone from different account categories over time, common triggers of trends and much much more. Dune

The Economy of Elon Musk's X

ইলন মাস্কের এক্স এর অর্থনীতি

Die Wirtschaft von Elon Musks X

La economía de la X de Elon Musk

L'économie du X d'Elon Musk

एलोन मस्क की एक्स की अर्थव्यवस्था

イーロン・マスクのXの経済

Elon Musk의 X의 경제

A economia do X de Elon Musk

Elon Musk'un X'inin Ekonomisi

Nền kinh tế của Elon Musk's X

埃隆·马斯克 X 的经济

Metrics in are a widely debated subject. What are the key product metrics? How do we navigate the trade-offs between them? The straightforward answer is, naturally, "it depends" :). Above all else, the answer hinges on the product's purpose and its stage in the lifecycle. product management Product mission Mission-driven businesses define their purpose through serving a particular mission. A narrow view on business is that it is about making money (which is true) but that view does not help answer the question how (you are going to make that money). The mission, or purpose, of the business or product is what defines the how. Missions can be as broad and aspirational as Patagonia’s “ ” or as specific and down to earth as IKEA’s “ ” What makes a good mission is clarity and focus. A well-articulated mission should make it easier to choose the right metrics because they should help track progress towards the vision - a desired state of the world when the mission is accomplished. Save our home planet To offer a wide range of well-designed, functional home furnishing products at prices so low that as many people as possible will be able to afford them. For products that charge their customers directly for goods or services, success in fulfilling the product mission usually goes hand in hand with the product’s financial performance, and the success metrics will reflect both aspects. However, for products that monetize through advertising achieving the mission's success and financial success may involve a certain degree of trade-off - e.g. aggressive advertising will drive revenue but it will also interfere with user experience and drive some users away. It becomes crucial to monitor and maintain a delicate balance between the two throughout the product's lifespan. Product lifecycle is a framework to describe typical stages of a product or business from its inception to decline. Similar to a live organism, a product is conceived and born (introduced to the market), then it grows, matures and eventually declines. Product lifecycle The diagram below marries two popular ways to represent the business cycle: one as a chart of sales vs product lifecycle stage and the other is the that maps a product’s or a business’s competitive position in the market (product market share vs market growth rate) and gives them distinct names: BCG matrix 1. : a new product is introduced to a fast-growing (and probably also new) market Question mark 2. : a product conquered a large share of a fast-growing market Star 3. : the product is dominant on a slow-growing (mature) market Cash cow 4. : a product has a low share of a slow (mature) market Dog : Source Business-to-you We can map the “Question mark” stage to the introduction of a new product to an attractive, fast-growing market. This is the point where the product needs to find - i.e. prove its appeal to the customers of that market and potential to further growth. If it succeeds, it will open a path to become a and conquer a large share of the market; otherwise it will just keep growing with the market as a small part of it till the market declines, and the product becomes a . product-market fit Star Dog Assuming the product found its fit with the market, the next stage is all about . : you want to have a nice return on your investment but not at the cost of missing new customers. A is a product that managed to gain a large share of an attractive growing market. growth: number of customers, sales volume, market share Profitability at this stage is important but secondary to growth Star Finally, any market reaches maturity and slows down; at this stage a previous turns into a . . At this point the product / business would have a large customer base which it would try to exploit to the fullest to generate cash for new ventures and hopefully create new products. Star Cash cow The goal of a Cash cow is to bring as much revenue as possible till the market declines and shrinks Star In the original BCG framework, a is a product that never really took off, i.e. never gained a sufficient share of a market. In that sense, a is not a final life stage of a successful product. However, if we ignore the market share consideration, we can say that after the market has shrunk so much that a does not turn good profit anymore, it is time to divest or pivot that product or business, and focus on new ventures. Since it is an advised approach to Dog products, we can call the last stage of any product lifecycle a as well. Dog Dog Cash cow Dog Key metrics at different product life stages Now that we discussed the importance of product mission and the main stages of a product life cycle, let’s have a look at the key product metrics. Metrics are a reflection of our beliefs of what is important, either in a good or a bad sense, and thus they should map to broader goals that we are trying to reach (mission and vision) as well as the business’s financial well-being - without money we are not going to drive much impact and achieve our mission. Before we dig further into the lifecycle stages again, I would like to talk a little about the types of metrics that you need to have a holistic view of your product performance. There are three types of metrics that I recommend tracking for that purpose: - this is your success definition: number of active customers, growth rate, profitability, etc. Topline metrics are fundamental characteristics of your product that define its viability at a particular lifecycle stage. Another way to look at topline is that these are the numbers that you want to track in order to gauge how well you invested your money when developing and marketing your product. Topline metrics - these metrics help you understand what is happening or is going to happen to your topline. They describe the process that leads to your topline - e.g. sales funnel metrics, user behavior metrics, etc. These metrics help you “debug” what’s not working and fix it, or to identify what’s working well and double down on that. Supporting metrics - these are counterweight / checks and balances metrics. They track an important “health” dimension of your customer base (e.g. retention), customer experience (e.g. service downtime) or your finances (e.g. costs). Adhering to your guardrails ensures sustainability of your product’s success. Guardrails Product introduction To recap: at the product introduction stage, our goal is to During the validation, it is vital to bring an MVP - minimum viable product - to market as soon as possible, start gathering feedback and tweak the product and its value proposition to capture the right demand and get traction with the right customers. Since trial and error is a natural approach at this stage, you don’t want to over-invest into a particular solution - you may have to abandon it and start from scratch. Agility and scrappiness is what you need to succeed during this time. prove or find product-market fit. Two-sided marketplaces such as a ride-hailing platform have an additional “cold start” challenge: you need to have sufficient drivers available so that riders could actually book a ride with a reasonable wait time, and you need to have enough interested riders for drivers to earn money and continue using the app. However we decide to tackle that challenge, from the product success - and metrics - standpoint what’s important is our ability to attract and retain both drivers and riders for the platform to work. So what are the metrics that will help you navigate this stage? . Typically the success at this stage is defined as that belong to a larger segment into which your product can continue growing. Since it is a very early stage of the product / business lifecycle, you would also want to track how fast the number grows week-over-week, and ideally you would want to see that growth rate to be double-digit. TOPLINE reaching a certain number of active / paying customers . When your product is very new, and barely anyone knows about it, your key challenge is to create awareness, and attract and convert the right kind of customers before you run out of money (whether your personal funds or a corporate budget for that project). During this process the is your best friend: you want to be able to track prospective customers at each stage and work on increasing the funnel throughput at each of the stages, from awareness (prospects) to intent (leads) to transaction (customers) and, with any luck, repeat transactions (loyal, returning customers) and advocacy. I SUPPORTING METRICS classic sales funnel mage source: SalesRecipe . . Two things that you need to be careful about is the amount of money you have and how well you converted customers are doing after the conversion. At the product-market fit stage you have a limited budget, and you need to find the fit before you run out of it. and are the metrics that capture this consideration. The burn rate measures the rate at which a startup is spending money compared to the rate at which it is generating revenue. Runway is the length of time a startup can sustain its operations based on its current cash reserves and burn rate. Since the key expense at this stage is marketing, is the metric that you need to be aware of because it determines how viable, or not, your current model is - together with . For example, if you pay $30 to get a new customer, and they buy from you for $20 and never come back, you lose $10. GUARDRAILS Burn rate runway Customer acquisition cost (CAC) customer retention Growth Kudos! You proved the product-market fit, and your next job is to grow your product - meaning grow your active/paying customer base, transactions (or other meaningful actions) and ultimately revenue. At the growth stage two key factors define success: . The MVP that you presented to the world at the introduction stage now needs to turn into a full-featured product that continues addressing your target customer needs in an effective and distinct way that other products / businesses cannot easily match. Product performance can be driven by unique aesthetics, superior technology, exclusive content, etc - anything that helps you differentiate from other products in a way that attracts more customers and makes them loyal to your product or brand. product quality, or performance, and sales effectiveness However, it is rarely enough to just have a great product to win over the market. High quality needs to come hand in hand with to let potential customers learn about your product, try it and hopefully continue using it. Whether you use performance marketing, promotional partnerships or cold calls, it will require specialist time and needs to be paid for. Finding the balance between how much you spend on attracting new customers and how much you are able to earn from them, while maintaining a steady and preferably double-digit growth rate, is the challenge of this stage. effective marketing and sales . The (orders, rides, subscriptions, etc) are the metrics that allow you to watch your growth trajectory. Depending on the nature of your product and how often you expect your users or customers to use it, you may want to track your metrics on a daily, weekly or monthly basis, or maybe all together. TOPLINE number of active users / paying customers, and the volume and value of transactions The above mentioned sales funnel remains your best friend at the growth stage. Another important family of metrics to track are about how your customers use your product and what can be improved in their experience. (e.g. DAU/MAU, daily sessions per user, daily time spent) and they derive from it (CSAT, NPS) are examples of such metrics. Check out this to read more about the usage metrics; and about transactional vs. relational customer satisfaction. SUPPORTING METRICS. Frequency and intensity of use satisfaction Sequoia capital post this article . The key metrics at this stage are related to - evaluation of the profitability and viability of a business model on a per-unit basis. Unit in this case refers to a user or customer, and the goal is to understand how much profit the product brings on average compared to the average cost of acquiring a customer. is used for estimating the long-term revenue generated by an average customer, and typically the is computed. There exists a notion of , meaning an average customer generates at least three times the money that was spent to acquire them. GUARDRAILS unit economics (Customer) lifetime value, or (C)LTV LTV-to-CAC ratio “ideal” LTV:CAC ratio of 3:1 Maturity Your product grew fast and became a large player in the market: you have a large and stable customer base that keeps coming back and is happy with your product. Now is the time to get the most out of your investment into the product. Your key levers are marketing and sales, and optimization of maintenance cost of your product. Marketing and sales should be more effective now since you already acquired a lot of customers, and you only need to offset the natural churn of the customer base. The product has already won over the market so you may want to look into automation and other ways to optimize the cost of its upkeep, and redirect your resources to new promising business opportunities. is now your top of mind: after a long time of pouring money into the product and a loyal customer base, you want to see that investment paying off. There are many different profitability metrics that take into account different types of costs (gross profit margin, net profit margin, EBITDA) but the most generic one is . ROI measures the return generated from an investment relative to its cost, and is computed as Net Profit is calculated by subtracting the total expenses associated with the investment from the total revenue or income generated. Cost of Investment represents the total cost incurred to acquire, develop, or implement the investment. It includes expenses such as purchase costs, production costs, marketing expenses, and any other costs directly related to the investment. Both Net profit and TOPLINE. Profitability Return on Investment (ROI) Net Profit / Cost of Investment. . Beyond , at this stage supporting metrics become very specific to your product and operating model, and become heavily skewed toward business performance measurement. On the revenue generation side, your aim is to drive high marketing ROI - e.g. you could choose to grow organic sales (and thus measure them). Minimizing operational costs could be about increasing automation in some functions, e.g. customer support, and you would want to track progress in that area. SUPPORTING METRICS net profit and cost of investment . Even reaching your product maturity, you still need to monitor . Turbocharging your marketing efforts, or taking an overly zealous approach to cost optimization may affect customer experience and cause churn, hence the need to monitor the health of your customer base, one of your most precious assets. Other guardrails would be specific to concrete actions you take to boost your revenue or cut costs. GUARDRAILS active users / paying customers as well as volume and value of transactions In conclusion The table below captures the key points discussed above about product lifecycle stages, and associated goals and metrics: I hope you found this article insightful and that it empowers you to make informed decisions in your product management journey. If you have any questions or comments, please leave below and I will do my best to follow up!

Key Product Metrics and Where They Come From

AI drug discovery is exploding. Overhyped or not, investments in AI drug discovery from $450 million in 2014 to a whopping $58 billion in 2021. All pharma giants, including Bayer, AstraZeneca, Takeda, Sanofi, Merck, and Pfizer, have stepped up spending in the hope to that will bring cost efficiency, speed, and precision to the process. jumped create new-age AI solutions Traditional drug discovery has long been notoriously difficult. It takes and costs to bring a new drug to the market. And this is only the case for drugs that succeed in clinical trials (only one in ten does). at least 10 years $1.3 billion Hence, the interest in finding new ways we discover and design drugs. AI has already helped identify promising candidate therapeutics, and it didn’t take years, but months or even days. In this article, we will explore how AI drug discovery is changing the industry. We will look at success stories, AI benefits, and limitations. Let’s go. How drugs are discovered The drug discovery process typically starts with scientists identifying a target in the body, such as a specific protein or hormone, that is involved in the disease. Then they use different methods to find a possible solution, a drug candidate, including: : Scientists can screen libraries of compounds (natural products or chemicals) they made before, to check if any of them have the desired activity or interaction with the target. Screening existing compounds : They can use computer modeling and simulation to develop novel chemical compounds that can do the job. This approach is used to create small molecule drugs, which are chemically synthesized compounds less than 1,500 daltons in size. De novo drug design : Researchers can also generate biological molecules like antibodies, enzymes, or proteins to act as drugs. This involves isolating or synthesizing molecules from living organisms that can interact with the target. Compared with small molecules, such molecules are typically larger and more complex. Biologics : Scientists can take a look at compounds that were developed for something else and see if they have therapeutic potential for the disease in question. Repurposing Once a potential drug candidate (called lead compound) is found, it is tested in cells or animals, before moving on to clinical trials which include three phases, starting with small groups of healthy volunteers, and then proceeding to larger groups of patients suffering from the specific condition. How AI is applied that involve using sophisticated computational methods to mimic elements of human intelligence such as visual perception, speech recognition, decision-making, and language understanding. Artificial Intelligence covers various technologies and approaches AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. Since the advent of deep learning in the 2000s, have expanded. AI applications in healthcare A few AI technologies are empowering drug design. Machine Learning focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. Machine learning (ML) ML solutions encompass a diverse array of branches, each with its own unique characteristics and methodologies. These branches include supervised and unsupervised learning, as well as reinforcement learning, and within each, there are various algorithmic techniques that are used to achieve specific goals, such as linear regression, neural networks, and support vector machines. ML has many different application areas, one of which is in the field of AI drug discovery where it enables the following: Virtual screening of compounds to identify potential drug candidates Predictive modeling of drug efficacy and toxicity Identification of new targets for drug development Analysis of large-scale genomic and proteomic data collected from living organisms (DNA sequences, gene expression levels, protein structures, etc.) Optimization of drug dosing and treatment regimens Predictive modeling of patient responses to treatment Deep Learning Deep Learning (DL) is a subset of ML based on using artificial neural networks (ANNs). ANNs are made up of interconnected nodes, or “neurons,” that are connected by pathways, called “synapses.” Like in the human brain, these neurons work together to process information and make predictions or decisions. The more layers of interconnected neurons a neural network has, the more “deep” it is. Unlike supervised and semi-supervised learning algorithms that can identify patterns only in structured data, DL models are capable of processing vast volumes of and make more advanced predictions with little supervision from humans. unstructured data In AI drug discovery, DL is used for: Improved virtual screening of compound libraries to identify hits with a higher probability to bind to a target Image-based profiling to understand disease-associated phenotypes, disease mechanisms, or a drug’s toxicity More accurate prediction of how a drug will be absorbed, distributed, metabolized, and excreted from the body (pharmacokinetic properties) Prediction of drug-target interactions and binding affinity Prediction of the structure of proteins that account for most of the currently identified drug targets Generation of novel drug-like compounds with the desired physical, chemical, and bioactivity properties and protocol design Automation of clinical trial processes Natural Language Processing (NLP) NLP relies on a combination of techniques from linguistics, mathematics, and computer sciences, including DL models, to analyze, understand, and generate human language. AI drug discovery research often uses NLP to extract information from both structured and unstructured data to accomplish the following: Text mining of scientific literature to identify associations between chemical/drug entities, their targets, and novel disease-related pathways Extracting structured information from unstructured electronic health records (EHRs), such as patient demographics, diagnoses, and medications Identifying adverse drug events by analyzing text data from social media, news articles, and other sources Determining clinical trial eligibility criteria based on protocols and matching patients to trials Summarizing drug information Why AI drug discovery is the talk of the town now In the last couple of years, companies across the pharmaceutical sector have taken steps to incorporate AI into their research methods. This includes building in-house AI teams, and , backing startups with an AI focus, and teaming up with technology firms or research centers. hiring AI healthcare professionals data analysts A combination of factors is driving this trend. The increasing power of computers and new AI developments Recent tech advances have shifted the traditional focus of AI drug discovery research. As the majority of companies in the sector (around 150 in 2022 according to ) continue to be busy with designing small molecules, which are easy to represent computationally and compare at scale, there is also a growing interest in new applications of AI in drug discovery. BiopharmaTrend AI Report Many companies are beginning to embrace AI for designing biologics (77 companies) and discovering biomarkers that indicate the presence or progression of a disease (59). Others are focused on building all-embracing AI drug discovery platforms, identifying new targets, or creating ontologies — structured representations of relationships between different entities such as chemical compounds, proteins, and diseases. Widening access to AI tools As the shortage of AI talent shows no sign of abating, the entry barriers to AI drug discovery have actually reduced. Tech vendors and pharma giants are releasing increasingly sophisticated AI platforms, including ready-to-use no-code and drag-and-drop systems that enable non-AI experts to integrate artificial intelligence into their research. These developments are playing a major role in the accelerated adoption of AI by the industry. AI-enabled success stories AI drug discovery projects pursued in academia and the industry have already produced the first successful results across the value chain of drug discovery. Examples include: DeepMind has built the AI system AlphaFold that can predict a protein’s 3D structure from its one-dimensional amino acid sequence in seconds rather than months or years that it would normally take. The system was used to predict over 200 million protein structures belonging to animals, plants, bacteria, fungi, and other organisms. University of Washington researchers have developed a deep learning model that uses gaming computers to calculate protein structures within 10 minutes. Deep Genomics has used AI technologies to screen more than 2,400 diseases and 100,000 mutations to predict the exact disease-causing mechanism in a Wilson disease mutation and create a DG12P1 drug in 18 months. Aladdin has released a proprietary AI drug discovery platform for commercial use in virtual screening, hit-to-lead, lead optimization, and the preclinical phase. This platform helped Aladdin identify a number of drug compounds for a potential treatment of age-related diseases. IBM has developed the Watson system with cognitive computing capabilities that is used by the pharmaceutical industry for matching patients to the right-fit clinical trials for their condition. In a clinical trial for breast cancer, the platform demonstrated an increase of 80% in enrollment and a reduction in trial matching time. It has taken less than three months for AbCellera to develop a monoclonal antibody for neutralizing viral variants of COVID-19 and obtain approval from the US Food and Drug Administration (FDA). BenevolentAI has combined its knowledge graph with AI tools to uncover baricitinib as a potential COVID-19 treatment in several days. BioXcel Therapeutics has accelerated the discovery of dexmedetomidine as a sedative for patients with schizophrenia and bipolar disorders. The company obtained FDA approval for its proprietary sublingual film of dexmedetomidine (IgalmiTM) in less than four years after its first-in-human trials. Using AI, Exscientia has designed three small molecules to enter clinical trials over the span of two years (for the treatment of Alzheimer’s disease psychosis, obsessive-compulsive disorder, and immuno-oncology). In early 2023, Insilico reported positive topline results in a Phase 1 clinical trial of the first AI-designed novel molecule for an AI-discovered novel target to treat idiopathic pulmonary fibrosis (IPF). In 2021, reached the clinical stage, with their therapy areas including COVID-19, oncology, and neurology. 13 AI-derived biologics Benefits and challenges in AI drug discovery AI is a powerful tool that holds the promise of revolutionizing the pharmaceutical industry. With its ability to analyze vast amounts of data and make predictions, artificial intelligence can help researchers overcome the obstacles that have long hindered the drug discovery process by enabling: Reduced timelines for discovery and preclinical stages More accurate predictions on the efficacy and safety of drugs New, unanticipated insights into drug effects and diseases New research lines and new R&D strategies Cost savings through quicker analysis and automation According to Insider Intelligence, AI can save the pharmaceutical industry up to 70% of drug discovery costs. The potential of AI in drug discovery is truly exciting, but there are a few roadblocks that need to be tackled first to exploit it to the fullest. Data When it comes to AI, it always comes down to input data. Data silos and legacy systems that wouldn’t allow their consolidation are big hurdles to AI research in any domain. In the pharmaceutical industry, the problem may be even more pronounced. , be it results from clinical studies or de-identified patient information, while the troves of data they have may provide answers to questions that the original researcher never considered. Pharmaceutical companies have traditionally been bad at sharing data When it ultimately comes to sharing data, it’s often incomplete, inconsistent, or biased, used for predicting protein-ligand binding affinities that are crucial for drug discovery. In some cases, the data may not even be reflective of the entire population and the AI model may fall short in real-world scenarios. as is the case with datasets Complexity The sheer complexity of biological systems makes AI-enabled analysis and predictions of time and spatial changes in their behavior hard. There is a vast number of complex and dynamic interactions within biological systems where each element such as proteins, genes, and cells can have multiple functions and be affected by multiple factors, including genetic variations, environmental conditions, and disease states. Interactions between different elements can also be non-linear, meaning that small changes in one element can have a great impact on the entire system. For instance, a single gene that controls cell division can be responsible for the growth of a tumor, or interactions between multiple proteins can lead to the development of highly specific and complex structures such as the cytoskeleton of a cell. Another challenge is a lack of qualified staff to handle AI drug discovery tools. Interpretability The use of neural networks in AI drug discovery has pushed the boundaries of what is possible, but a lack of their interpretability poses a significant challenge. Referred to as black boxes, such AI models might produce the most accurate predictions possible but even engineers can’t explain the reasoning behind them. This is particularly challenging in deep learning, where the complexity of understanding the output of each layer escalates as the number of layers grows. This lack of transparency can lead to flawed solutions and reduce trust in AI among researchers, medical professionals, and regulatory bodies. To address this challenge, there is a growing need for the development of explainable, trustworthy AI. Wrapping up New drugs that are changing the game for patients continue to emerge. Just 15 years after HIV was identified as the cause of AIDS in the 1980s, the pharmaceutical industry has developed a multi-drug therapy that allows people affected by the virus to live a normal life span. Novartis’ Gleevec prolongs the lives of leukemia patients. Incivek from Vertex Pharmaceuticals has doubled hepatitis C cure rates. Keytruda from Merck reduces by 35% the risk of cancer coming back after patients had surgery to excise melanoma. But not all new drugs are created equal. conducted in Germany has revealed that only 25% provided significant advantages over existing treatments. The remaining drugs yielded either minimal or no benefits, or their impact was uncertain. A recent analysis of over 200 new medicines Given the costly and time-consuming nature of drug discovery, it’s clear the pharmaceutical industry needs major changes. And that’s where AI drug discovery could play a role. There is every chance that artificial intelligence can make a transformational contribution going beyond accelerating time-to-clinic. Thinking about your own AI drug discovery project? . With years of experience in creating AI solutions for healthcare, we are your right partner. Drop us a line

AI Drug Discovery: How It’s Changing the Game

Descubrimiento de fármacos por IA: cómo está cambiando el juego

Découverte de médicaments par IA : comment cela change la donne

एआई ड्रग डिस्कवरी: हाउ इट्स चेंजिंग द गेम

AI創薬：ゲームをどのように変えるか

Descoberta de drogas de IA: como isso está mudando o jogo

Khám phá thuốc AI: Nó đang thay đổi cuộc chơi như thế nào

人工智能药物发现：它如何改变游戏规则

Edge Computer Vision applications for Enterprises Computer vision (CV) is a subdomain of the field of Artificial Intelligence that is aimed at helping machines to identify and understand content in digital video or images. Simply put, CV enabled machines to “see” the world like we humans do and use that knowledge to augment human efforts. This technology uses cameras and computers instead of the human eye to identify, track and measure targets for further image processing. Examples of computer vision applications include self-driving cars that use multiple cameras to perceive the environment around them such as other cars, bicyclists, pedestrians, obstacles, etc., and navigate around them without any human intervention, or how your iPhone uses facial recognition to identify that it is really you to unlock your phone. Computer Vision is an active area of research and development around the world with better CV algorithms and hardware processors and hosting environments coming out almost every week! Benefits from Computer Vision for Enterprises As mentioned above, CV allows machines to perceive the world as humans do, but because they are machines, they can operate non-stop 24x7 and at extremely high speeds much faster than humans can, and do it objectively without any human biases or oversights, and even do it with higher accuracy. And with the advancement of technology, running CV applications on edge devices has also become inexpensive and widely adopted around the world. CV is being used in a wide range of applications (the rest of the article is a deep dive into use cases), with more applications becoming possible very soon as the technology matures. According to a 2021 report of , the AI in Computer Vision Market size was valued at USD 7 Billion in 2020 and is projected to reach USD 144 Billion by 2028, growing at a staggering CAGR of 45% from 2021 to 2028! Verified Market Research Enterprises can use CV to reduce costs and drive operational efficiencies by automating repetitive, time-consuming, subjective, laborious, and tedious tasks and transforming them into tasks that can be handled well by machines in an objective, non-invasive manner and run 24x7 without any need to stop at any time for any break and deployed at any scale. Enterprises can implement better Quality Control processes at a pace and scale that humans can never perform. Using CV, Enterprises can drive massive productivity gains, increase topline and accomplish more in an exponentially shorter time with significantly fewer resources. Benefits of running CV applications on Edge devices Edge CV is simply running CV computation on edge devices. As opposed to cloud computing, edge computing moves workloads closer to the users and closer to the source of data. Doing so gives huge advantages: real-time data processing and analysis (since data doesn’t have to travel all the way to the cloud servers and back, saving network latency concerns), minimizes network bandwidth consumption, reduces energy consumption, improves computation efficiency, and improves cybersecurity posture. Traditionally, edge devices weren’t powerful enough for CV computation and hence most of the processing must be delegated to clusters of machines in the cloud. But with the recent advances in edge technologies, the processing power has increased tremendously while being energy efficient. Additionally, Computer Vision on the edge can address any privacy concerns by guaranteeing that the data never leaves the edge device helping with changing privacy regulation landscape across the world. CV applications by Industry Brick and Mortar Retail The physical retail industry is always looking to provide its customers with the best in-store experience to maximize basket size, acquire and retain customers and ultimately boost customer loyalty. CV can be used to generate critical insights and analytics in an automated fashion without the need for time-consuming techniques, employing human observers or expensive physical tracking devices, and doing it round the clock without any downtime or mistakes. Acquire repeat has needs… store owners want to know footfall, type of customers (age, gender, group size, etc.), and correlate with so they can maximize the time spent in-store and maximize basket size detect and track individual people in the real-time video for analysis of customer traffic. Counting people became especially popular with the COVID-19 pandemic, with stores restricting the number of people inside to prevent the spread of the pandemic. People counting can also be used to count the number of people waiting in line and notify managers to allocate more people to check out stations. People counting: track the length of time customers spend in a specific section of the store and looking at specific items (aka dwell time). CV-based applications can also be used to annotate regions of interest or boundaries and track how many people cross the boundary, how long people spend in the region of interest, and track where people are going to a visualization of how people move across the store. Such insights can also reveal peak hours and bottlenecks. Once such metrics exist for all stores, management can start comparing key metrics across different locations to identify and improve store performance. Customer behavior analysis: refers to the visualization of individuals’ movement across physical spaces over time. By assigning colors to areas that see more movement relative to others and visualizing that as a contour plot, store owners can better understand points of interest, which sections see more foot traffic compared to others, and use that information for optimized product placement or mitigating bottlenecks People heatmaps: Manufacturing and Industrial Manufacturing workplaces consist of moving machinery, equipment, and people and are a hotspot for accidents. CV applications can help increase worker safety, identify dangerous situations, and prevent accidents in real time while increasing productivity, and efficiency, and reducing the risk of business interruptions. For example, CV can help detect awkward human postures so they can be notified and corrected to prevent injuries. Occupational safety: CV can be used to detect and monitor the usage of helmets, eye protection gear, vest detection, and more. Detection of PPE (personal protective equipment): such as excavators, cranes, generators, or tractors, and detect when workers are operating in such dangerous areas. These automated detections can be documented, and any violations recorded so that recurring violators can be trained better or fired and safety protocols in plants or on construction sites can be updated Locate heavy construction equipment Public / Smart cities Governments around the world are trying to use technology to keep people safe, detect offenders and perpetrators, and make public amenities and services easier and more convenient to use. uses CV technology to read license number plates on vehicles from video feeds in real time. The latest advances in LPR technology have enabled license plate reading even for vehicles at high speeds under complex scenes and illumination. LPR is already widely in use in toll collection and parking lots management systems, detecting and identifying traffic violations, law enforcement with more applications, and higher performing, scalable and lower cost solutions being developed around the world. License Plate Recognition (LPR): Instead of relying on timer-based logic, CV can be used to supercharge traffic movement by automatically detecting traffic movement, and counting vehicles in a queue so lights can be optimized dynamically to increase traffic throughput during peak as well as non-peak hours. Optimized traffic lights: CV technologies can be used to identify suspicious objects that have been left behind in public spots and authorities alerted so people can be evacuated if needed and the area secured without loss of human life and property. Weapons including guns and knives can also be detected in real-time and authorities are alerted instantaneously for speedy response and loss of human life. Abandoned and suspicious objects detection: Using CV technology for vehicle counting, parking spots can be brought online so availability in a lot is known before cars enter it, preventing the endless looping and countless minutes spent looking for a parking space, especially during rush hours. Such information could be relayed and searchable by the public and will lead to more efficient traffic flows and predictable transportation. Parking lot occupancy: Physical Security and Intrusion Detection Monitoring the physical spaces for people and personnel safety, critical infrastructure, and equipment protection have been one of the older applications of CCTV surveillance camera systems. CV has supercharged the system’s ability by autonomously detecting and responding to intrusion in real-time, without any human intervention. Modern physical security systems are intelligent enough to call for the dispatch of emergency services automatically or, in cases of confusion, request human verification. The CV algorithm running on the camera (or cloud) can automatically detect and tag all objects (people or vehicles) seen by the camera, including storing a snapshot of the people. The operator can tag one or more people or vehicles as Persons of interest (PoI) or Vehicles of Interest (VoI). The next time the person or vehicle is seen by the camera, the operator can be automatically alerted for an adequate response. Person and vehicle detection: Being able to search people by the color of clothes, gender, accessories on them, and vehicles by color, make and model makes it exponentially easy to find the people in the recorded footage instead of scrolling through days of footage. Attribute-based search: CV technology can allow people and vehicles to be searched across all cameras in a location which is invaluable in creating a forensic timeline of events for speedy investigation resolution. Search across multiple cameras: CV models can be used to match faces to determine similarity and identify if 2 pictures are of the same person. This can be used to find all instances where a given person was spotted by CCTV cameras across multiple days. Face match: Smart face blur can be used to detect, and blur the faces of all non-relevant people in a security tape, protecting their privacy Face blur: Farming and Agriculture CV plays a critical role in enabling smart farms and ranches, specifically for livestock detection, tracking, and even monitoring their health! There is increasing pressure on the limited land and resources to continue feeding the expanding world population. At the same time, the demand for sustainable livestock products, coupled with the rapidly increasing cost of labor and worker shortage has drastically increased the demand for technologies that can deliver higher operational efficiencies increase productivity with a significantly smaller worker base and at a lower cost. One can use multiple static cameras or use UAVs as well. That depends on the size of the area to be monitored viz. Livestock monitoring solutions know where each animal is at any time, if they are in safe zones or if they need to be guided Animal counting and tracking: Differentiate between different types of animals on a farm and maintain a count of animals of each type Animal classification: Detect when animals cross that that zone and send alerts Define regions of interest: Identify behavior such as drinking, feeding, sleeping, birthing, and nursing and behaviors such as aggression, laziness, stress, etc. Analyzing animal activities and behavior: Crop monitoring solutions by placing cameras in the right areas, growth trends can be tracked, and anomalies identified in real time Measurement of plant growth: analyze the impact of water, light, fertilizers, etc. on plant health to find optimal levels of input to achieve the highest efficiencies Precision agriculture: Using CV models trained on different types of pests, weeds and occurrence of diseases, such unwanted development can be identified in real-time and humans alerted for fast response to contain its spread before a loss of output Pest/weeds/disease detection: CV technologies can be implemented to identify the highest quality crops and produce and separate them from those that don’t meet the quality bar to better plan for shipments, locations, and markets. Sorting of crops: Healthcare CV has massive benefits in a wide range of healthcare applications — from patient health monitoring to environmental analysis to commercial uses such as fitness, training, and workout coaching. The benefits are that this can be done 24x7 without worrying about sleeping attendants, human oversight, and late-night labor. Best of all, the privacy of the patients can be protected through on-device processing with no visuals leaving the camera at any time. Falls are a major problem worldwide with 30% of falls causing severe injuries hence it is no surprise that fall detection is seeing rapid adoption in today’s aging population. Cameras placed in patient spaces can monitor patients’ movements including sudden changes such as falls and slippages. When a fall is detected, personnel can be notified immediately instead of relying on human input and routine check-ins to catch the mishap, leading to speedy treatment and faster recovery. Best of all, CV-based fall detection doesn’t need any setup nor do they need any expensive wearables or worry about those devices being invasive or need charging or simply forgotten. Fall detection: detect the human body in different postures such as lying down, sleeping, sitting, walking, standing, etc. Posture detection can be used to identify suspicious behavior such as violence or aggression, incidents such as fall detection or injuries, and miscellaneous healthcare applications that need to measure time spent in different activities such as walking, sitting, and standing for comprehensive health and activity reporting. Posture recognition: with the recent pandemic most government and regulatory bodies recommended maintaining 6 feet distance and wearing masks in public spaces. CV-based technologies can easily detect human violations of these regulations and warn the people involved or automatically alert authorities. Social distancing monitoring and mask detection: A surge of recent home-based workout devices rely on CV to detect posture during different workouts and recommend corrections to prevent injuries and get the most out of the workout Fitness and online workout coaching: CV can be used to detect human faces and selectively blur them to protect their privacy. Such tools are invaluable for an incident response when sharing video and evidence with authorities so that the privacy of those who are not involved with the incident is protected. Detect and blur faces in real-time: Also published here.

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