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S&P Europe 350

2,072.07

-0.03%

S&P 500

6,032.38

0.56%

DJIA

44,910.65

0.42%

S&P/TSX 60

1,538.75

0.37%

S&P/TOPIX 150

2,390.7

1.38%

S&P/ASX 200

8,447.9

0.1%

S&P Global BMI

416.56

0.05%

S&P/BMV IPC

49,812.64

-0.26%

S&P Latin America 40

2,328.18

-1.58%

S&P Asia 50

5,374.98

1.13%

S&P China 500

2,535.22

1.13%

S&P 500 Bond

515.03

0.4%

S&P 500 VIX Short-Term Futures

11,920.31

-0.05%

S&P Europe 350

2,072.07

-0.03%

S&P 500

6,032.38

0.56%

DJIA

44,910.65

0.42%

S&P/TSX 60

1,538.75

0.37%

S&P/TOPIX 150

2,390.7

1.38%

S&P/ASX 200

8,447.9

0.1%

S&P Global BMI

416.56

0.05%

S&P/BMV IPC

49,812.64

-0.26%

S&P Latin America 40

2,328.18

-1.58%

S&P Asia 50

5,374.98

1.13%

S&P China 500

2,535.22

1.13%

S&P 500 Bond

515.03

0.4%

S&P 500 VIX Short-Term Futures

11,920.31

-0.05%



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23 Oct 2024

TOKENIZED PRIVATE CREDIT: A NEW DIGITAL FRONTIER FOR REAL WORLD ASSETS



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ARTIFICIAL INTELLIGENCE INSIGHTS

The artificial intelligence landscape is ever-changing. These technological
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Special Reports

Oct 01, 2024


CRYPTO AND AI: SHAPING THE FUTURE OF THE INTERNET

1 October 2024 Crypto and AI: Shaping the Future of the Internet By Todd
Kanaster, Andrew O'Neill, Rebecca Mun, Josh Stokesberry, Matta Uma Maheswara
Reddy, and Ava Yang This is a thought leadership report issued by S&P Global.
This report does not constitute a rating action, neither was it discussed by a
rating committee. Highlights AI's adoption is aligned to a rapid expansion of
online information that will facilitate significant technological opportunities
but also comes with risks relating to centralization around big technology
firms, information traceability, identity management, cyber security, and energy
consumption. Blockchain and cryptographic technologies (collectively referred to
as crypto) provide decentralized network solutions, and information ownership
and security tools that could mitigate some of those risks. The combination of
AI and crypto is nascent and rapidly evolving, yet even modest applications
should offer important power and network optimization opportunities, while
accelerated adoption over the longer term could lead to a crypto-supported
decentralized internet. Over the last three decades, the emergence of web-based
communication, online publication, and e-commerce has driven the exponential
growth of online information. More recently, AI’s harnessing of advanced data
analytics has enhanced our ability to identify patterns, create new information,
and to derive meaningful insights from large datasets, providing new capacity
for businesses and consumers to create, store, and share data (see figure 1).
This explosion of information brings technological development opportunities,
but also risks around information traceability, identity management, cyber
threats, and data center energy consumption. Crypto offers tools that could
mitigate those risks by offering the means to identify, track, and protect data.
And in doing so it could also open paths to new ways of operating decentralized
networks. Developments at the intersection of AI and crypto technologies will
have important implications for the evolution of the internet. To better
understand that potential, S&P Global studied the related benefits and issues
likely to emerge over the coming five to 10 years based on three forward-looking
scenarios: Incremental advancements in AI and crypto. A rapid expansion of AI
that exacerbates centralization risks. Decentralized internet powered by crypto
and AI. For each of these three development scenarios, we assessed the effects
that synergistic applications of AI and crypto could have on business, the
economy, and the environment across five key areas: cyber security, financial
markets, computational resources, Internet of Things (IoT) and networking smart
devices, and supply chains. How AI and crypto could shape the internet: three
scenarios Scenario 1: Incremental advancements in AI and crypto. AI and crypto
technologies evolve but result in only modest efficiency gains for enterprises.
The growth of internet-of-things (IoT) applications is limited while
Decentralized Physical Infrastructure Networks (DePINs) leverage blockchain
tokenomics (currency that can be used automatically within blockchain code) to
enable autonomous real-time interactions within physical or online networks.
Even with limited AI expansion, these networks could play an important role in
optimizing usage of already-scarce computational resources and become more
efficient as they expand with improved training data. DePINs, in 2024, are
seeing some application of these technologies. Scenario 2: A rapid expansion of
AI that exacerbates centralization risks. AI is centralized within a few, large
technology incumbents, leading to a concentration of power. AI models play a
dominant role in how businesses and consumers communicate and transact over the
internet, intensifying the importance of data ownership and identity
verification. Blockchain technology is used to decentralize information and
facilitate privacy maintenance but with limited effect. AI applications extend
beyond crypto into traditional financial markets, with associated and
significant risks. AI’s concentration within a few corporations with potentially
opaque governance structures engender reduced transparency and diminished
accountability and give rise to risks that threaten to erode public trust (see
figure 2). Scenario 3: Decentralized internet powered by crypto and AI. A
decentralized internet emerges that uses blockchain architecture to distribute
data and decision-making across multiple nodes, reducing the risk of bias,
censorship, and privacy that are associated with centralization (see figure 3).
Blockchain’s transparency underpins the integrity, immutability, and
traceability of data and AI decisions, with blockchain providing the means to
record and later audit both. This enhances accountability and reduces the
likelihood of personal data exploitation. Blockchain could also be used to
verify the authenticity of AI-generated content and distinguish between humans
and bots, potentially mitigating the risks of deepfakes and misinformation.
Blockchain’s ability to enhance data privacy while ensuring regulatory
compliance allows AI models access to larger volumes of training data and
enhances its security. The potential of these cryptographic technologies to
enhance privacy and security, while also enabling AI-driven insights and
collaboration, will have wide-ranging uses. For example, in the handling of
medical data: homomorphic encryption could allow sensitive information to be
processed and analyzed without being decrypted, meaning it can be used by AI
models to predict disease outcomes and personalize treatments while protecting
patient confidentiality; multi-party computation could enable medical networks
to combine research data for the training of AI models and joint analysis for
medical research; and zero-knowledge proofs could ensure an AI-generated medical
images do not contain any embedded personally-identifiable information. AI and
crypto's combination could be transformational The intersection of AI and crypto
offers potential for improvements across a range of industries, notably those
that face complex (and sometimes currently intractable) challenges, or where
there exists significant (including currently unrecognized) potential to unlock
new efficiencies. The extent of the potential benefits from the new technologies
will however vary across different uses and will be dictated by advancements in
the two technologies, as detailed (earlier) in our three scenarios. A summary of
the relevance of AI and crypto technologies across five key areas, and under our
three scenarios, is provided in the table below (see figure 4), while a more
detailed review follows. Cyber security The problem The increasing
sophistication of cyber threats (coupled with greater use of AI by malevolent
actors) means organizations face an increased cadence of attacks, which threaten
financial and reputational damage, as well as rising cyber insurance costs.
Opportunities AI threat detection and automated responses can significantly
enhance cyber security in blockchain networks, and especially in DePINs and
decentralized finance (DeFi) applications. AI can also help ensure the security
of code, which is particularly important for DeFi applications, including by
assisting in smart contract validation and through code testing and
verification. For example: deep-learning models can analyze large datasets for
unidentified vulnerabilities, known as zero-day vulnerabilities; and large
language models can be trained on libraries of malware to detect patterns of
attack and pinpoint vulnerabilities in existing code. Risks and challenges Data
bias in training sets can affect AI performance, potentially leading to false
negatives. AI models could be targeted and manipulated to compromise their
effectiveness. To mitigate these risks and maintain AI-driven security,
continuous updates and validation are essential. Financial Markets The problem
Traditional financial markets' exposure to inefficiencies, the risk of fraud,
and manual oversight increases costs, can hinder or delay real-time transactions
due to reliance on intermediaries, and raises the potential for errors and
criminality. Addressing inefficiencies through automation can lead to additional
risks. Opportunities Smart contracts (coded sets of rules and conditional
actions stored on a blockchain) could improve financial markets' transparency
and efficiency. The contracts automatically execute based on pre-set conditions
and can be integrated with verified real-world financial data through
information bridges, called oracles. AI's ability to process and analyze large
datasets provided by oracles can be used to efficiently generate pertinent
inputs for smart contracts. The combination of smart contracts and AI could
streamline markets by automating routine tasks, such as financial settlement,
contract execution, and compliance checks, reducing the need for manual
oversight and minimizing human error. Multi-party computation protocols can be
used in the creation of decentralized oracles that ensure the security and
accuracy of data across blockchains operating in a trustless system. AI
compliance tools can play an important role in enhancing security in automated
financial markets by identifying anomalies and potential fraud. They can monitor
transactions in real-time and automatically trigger smart contracts to take
preventive actions, such as halting suspicious transactions. Crypto wallets can
allow AI agents to transact with each other through on-chain payments. This
could, for example, enable an AI trading bot to acquire inferences from another
AI model that is trained on a data set that is not generally available.
Obviously AI agents do not have access to bank accounts in the traditional
payment system, but they can be set up with crypto wallets and smart contracts,
allowing them to exchange with each other, for example using tokens to pay for
data. In September 2024, Coinbase’s CEO announced the first such AI-to-AI
transaction. Potential applications of such interactions could also be much
broader than in financial markets. Risks and challenges Vulnerabilities in
oracles, such as susceptibility to data manipulation, can compromise the
integrity of oracle-connected financial systems. Determining liability in such
scenarios can be complex, due in part to the uncertainty of the legal framework
and regulatory environment for crypto and AI-driven financial systems,
particularly across international borders. AI models' complexity can render
decision making opaque, posing audit and accounting challenges. Smart contract
enforceability may be legally uncertain, potentially limiting their application
in traditional financial systems. Computational resources The problem Compute
power (the capability of a computing system to perform computations and process
data) and data storage are often siloed, leading to inefficient resource
utilization. The expanding use of AI is creating significant additional demand
for compute power to run data centers, and the trend is set to continue (see
figure 7). This additional demand could strain already scarce energy resources
and further challenge efforts to reduce emissions. Opportunities DePINs can
facilitate peer-to-peer resources exchanges, including of storage and processing
power, and incentivize use by rewarding participants with crypto tokens. This
enables the monetization of excess capacity/ the purchase of resources, leading
to optimized hardware use and reduced idle time. Tokenization of machine
learning data and compute power may enhance distributed and collaborative AI
systems that utilize high-speed and low-cost blockchain architecture. AI-driven
pricing can effectively match infrastructure with demand, reducing computing
costs and improving efficiency. Crypto is often portrayed as a drain on energy
resources due to its use of the blockchain that underpins bitcoin, the largest
cryptocurrency by market capitalization. It is important to note that energy
consumption is specifically a feature of Bitcoin’s proof-of-work consensus
mechanism and not inherent to all crypto. That means not all crypto is designed
to consume as much energy. That said, bitcoin miners’ large computing
infrastructures are often located in areas where energy costs are low, and
particularly in locations (such as Texas) where they can use excess energy
generated by solar and wind sources. Some bitcoin mining companies are using
their access to cheap energy to offer AI services to diversify their revenue
streams. AI data center firms are also looking to acquire or collocate with
miners’ infrastructure to access cheap energy. Risks and challenges Token price
volatility can affect the reliability and attractiveness of DePINs, potentially
deterring participation and hindering data collection. Aggregating responses
from multiple AI models may reduce errors and increase reliability, but can be
complex, especially for sophisticated tasks such as integrating with blockchain
platforms. Bitcoin mining and AI computations have different hardware
requirements. Miners access cheap and clean energy because they have flexibility
and economic incentives to adjust their demand according to the needs of the
grid, increasing demand at times of low usage (when energy prices are cheaper)
and decreasing at times of peak usage (when energy is expensive). Data centers
for AI use do not have as much flexibility to switch units on and off in
response to externalities. This limits the potential synergies between bitcoin
mining and AI services. IoT and networking smart devices The problem Smart
devices have so far mainly generated convenience benefits for individual
consumers and are yet to fulfil their potential to build smart networks. At a
municipal level, inefficient use of collective data and underutilized resources
can lead to higher operational costs and suboptimal services. Effective networks
of smart devices could help (see The Rise of AI-Powered Smart Cities, May 18,
2024). Opportunities By leveraging crowd-sourced sensors (dashcams, energy
meters, toll road monitors, and water pipe flow monitors) municipalities can
gather decentralized data, while rewarding contributors with tokens as payments.
AI-driven data analysis can be used to optimize infrastructure management
software, including mapping of energy grids, traffic flow, and water/sewer
systems, resulting in cost savings for government agencies. AI can also
dynamically adjust resource allocation to match real-time demand, reduce waste,
and predict maintenance needs. For wireless infrastructure, AI can improve
efficiency and security by reducing latency and mitigating cyber risks through
intelligent traffic rerouting. Risks and challenges Tamper-proofing is critical
to ensure data integrity and operational reliability when securing physical
assets and sensors within DePINs. Privacy concerns can arise due to the
processing of large volumes of data, especially where information is sensitive,
necessitating compliance with data protection regulations. As the number of IoT
devices grows, managing and scaling network infrastructure becomes increasingly
complex. Maintaining high data quality and accuracy is crucial to avoiding
inaccurate AI predictions and operational inefficiencies. Supply chains The
problem Manual processes and a lack of real-time data integration mean supply
chains face costly inefficiencies and operational risks due to delays and excess
inventory. Opportunities AI can be used to predict delays and dynamically
(including in real-time) adjust operations to reduce excess inventory and
optimize routes. That would be underpinned by smart contracts, which can
automate instant payments, facilitate compliance, and offer real-time tracking
with the aid of DePINs, resulting in a seamless and transparent logistics system
with immutable records. Risks and challenges Evolving legal frameworks across
different jurisdictions introduces regulatory uncertainty and creates compliance
difficulties. The complexity and opacity of AI models can pose audit and
accounting challenges, while the enforceability of smart contracts remains
legally ambiguous. Data privacy and security concerns must be addressed to
protect sensitive information and the integration of new technologies with
existing legal systems can be costly and complex. Scalability issues may arise
as supply chains expand and ethical considerations regarding job displacement
and decision-making biases need to be managed. The futures of crypto and AI may
go hand in hand Over the last three decades, the birth and evolution of the
internet has shaped how businesses and consumers communicate and transact. It is
a fact, sometime obscured by hype, that crypto and AI technologies are (at their
core) information technologies and will thus have a role to play in the
continued evolution of information, communication, and economic networks.
Synergies between the technologies should support their growth, mitigate
centralization risks, and give rise to impactful applications ranging from
supply chain management to smart cities. The rate at which those applications
will emerge, and the pace of their adoption, remains uncertain. Yet we believe
that the question is not if adoption will happen, but when it will occur. From
there, the key issues will be how the combination develops and the extent of its
effects (including due to emerging synergies between AI and crypto that will act
as a force multiplier). We will, in particular, monitor the growth of
decentralized physical infrastructure networks, which are already beginning to
optimize power consumption to mitigate the increasing energy demand from AI.
Should AI usage continue to accelerate, we will also watch for intensification
of centralization risks around big tech companies and the mitigation of that
risk by crypto technology, new regulations, and legal initiatives. Contributors
S&P Global Ratings Paul Whitfield Editor & Writer Editorial, Design & Publishing
Mahnoor Haider Senior Designer


Special Reports

Sep 19, 2024


INDIA’S AI AMBITIONS: CAN PUBLIC-PRIVATE PARTNERSHIPS LEAD THE WAY?

India Forward — 19 September 2024 India’s AI ambitions: Can public-private
partnerships lead the way? An opportunity exists to replicate the success of
India Stack to accelerate India’s AI mission. By Shankar Krishnamurthy and
Sugandha Srikanteswaran Highlights AI can play a key role in accelerating
India’s economic growth as the country aspires to be the third-largest economy
in the world by fiscal 2030–31. India can aim to replicate the success of the
digital public infrastructure public-private partnership model that helped
accelerate the country’s digitalization. There is a significant opportunity for
India-based IT providers and startups to be public-private partners for AI.
India Forward Emerging Perspectives Explore More Developing AI capabilities and
the realization of resulting productivity gains will require India to have
policies that provide an ethical and regulatory framework, research and
development and digital infrastructure investments, and a skilled AI labor pool.
AI’s potential rests on several factors Investment capacity, including the
willingness of corporations and governments to fund AI research projects.
Digital infrastructure, encompassing an economy's IT infrastructure, the
population's access to the internet, the availability of reliable data and the
existence of institutions to support AI research and development. Policy support
from prevailing laws and regulations, and the development and implementation of
rules that facilitate AI's progress — including the regulatory environment for
technology startups, a conducive administrative environment and data privacy
regulations. Labor suitability and availability, including education levels, AI
literacy, the capacity to reskill workers, and the ability to attract and retain
a qualified workforce. As of Aug. 13, 2024. Source: S&P Global Ratings. © 2024
S&P Global. Can the AI mission replicate the success of the Digital India model
to accelerate economic growth? The Digital India initiative, a public-private
partnership seeded with key investments by the government of India, continues to
play a key role in transforming Indian society using digital technologies. The
vision of the program was centered on three key areas: digital public
infrastructure (DPI), digital access to government services and the digital
empowerment of citizens. While the origins of DPI can be traced back to the
launch of the Aadhaar unique biometric citizen ID system in 2009, successive
governments, through the continuation of policies and investments, have
continued to evolve DPI, also known as India Stack. DPI is widely acknowledged
to be a successful platform that has helped accelerate India’s digitalization
through increased connectivity, improved efficiency, innovation and inclusive
growth. The recently announced IndiaAI Mission has the potential to build on the
successful public partnership model created by the Digital India initiative and
accelerate India’s economic growth as the country aims to become the
third-largest economy in the world by 2030, as per S&P Global projections.
IndiaAI Mission On March 7, 2024, the Indian government announced the launch and
implementation of the IndiaAI Mission through a public-private partnership model
aimed at nurturing India’s AI innovation ecosystem. This demonstrates the Indian
government’s commitment to creating an AI environment and propelling economic
growth. According to the mission’s official website, the broader vision of
IndiaAI is to “bolster India’s global leadership in AI, foster technological
self-reliance, ensure ethical and responsible AI deployment, and democratize the
benefits of AI across all strata of society.” Pivotal initiatives withinthe
mission include the following: Compute Capacity, Innovation Centre, Datasets
Platform, Application Development Initiative, FutureSkills, Startup Financing,
and Safe and Trusted AI. According to a 2024 report published by not-for-profit
industry association Nasscom, the Indian AI market is expected to grow to
between US$17 billion and US$22 billion by 2027, attracting investments of US$4
billion and becoming the third-largest talent base with an expected 1.25 million
to 1.35 million people with AI skills. The Indian AI market is expected to grow
to between US$17 billion and US$22 billion by 2027, attracting investments of
US$4 billion and becoming the third-largest talent base with an expected 1.25
million to 1.35 million people with AI skills. IT and IT-enabled services
sector’s critical importance to India’s GDP IT services (IT) — including
software development, maintenance and support — and IT-enabled services (ITES) —
including business process outsourcing and knowledge process outsourcing — have
been crucial contributors to India’s GDP, adding US$254 billion in revenue to,
and comprising about 7.5% of, India’s GDP in fiscal 2023–24. Almost 80% of the
IT/ITES revenue came from exports, with the US, UK and EU being the top three
markets. The IT/ITES industry has also created large employment opportunities
and is estimated to employ 5.43 million professionals, according to the Ministry
of Electronics and Information Technology. The IT/ITES industry has adapted well
to disruptions, including economic downturns and various automationdriven
reductions, by focusing on growth areas such as digital transformation and cloud
services. The industry has continued to grow in head count and revenue. However,
generative AI presents a new set of challenges with its ability to create
natural language chat applications such as ITES/voice business process
outsourcing, which can disrupt customer service functions. GenAI can also
generate code, automate testing and improve the efficiency of application
development by 20%-40%, making it so fewer programmers are required. With its
high reliance on head count for revenue growth, IT service providers must find
ways to continue growing revenue by recalibrating their business models to
provide higher-value services. The starting point in the recalibration and
transformation of the IT/ITES model is upskilling and reskilling the workforce
to understand and deliver outcomes to clients using GenAI. These firms have deep
roots in global enterprises and have established themselves as trusted partners
by helping clients optimize processes and reduce costs through outsourcing. As
part of customer IT and business operations, IT/ITES providers can harness their
knowledge of architecture, data and processes to help integrate GenAI into
workflows and deliver outcomes like improved customer experience and faster time
to market. The top five IT service providers collectively had a workforce of
1.45 million employees as of the first quarter of fiscal 2024–25. According to
the companies’ reports, these providers have trained more than 775,000 employees
on GenAI. Workforce transformation is the first step in countering the impact of
GenAI on revenue growth. IT/ITES providers need to be open to changing business
models by reducing dependence on time and material engagements and must move
toward more outcomebased engagements using AI/GenAI. This business
transformation requires providers to understand their customers’ business
strategies and IT processes, using AI/GenAI to deliver outcomes in the form of
improved customer experience and the faster launch of new products and services.
One major opportunity can be found in the customer experience management
function — which includes help desks, service desks and call centers — where
IT/ITES providers can use AI/GenAI to create human-like chat interactions to
deliver a faster and superior personalized customer experience. IT/ITES
providers have the scale, expertise and transformation experience required to
establish a publicprivate partnership for AI skilling and to impact the Indian
labor market. Initiatives such as IndiaAI FutureSkills provide the framework for
a public-private partnership that can prepare the Indian workforce for
opportunities. IT/ITES providers have the scale, expertise and transformation
experience required to establish a public-private partnership for AI skilling
and to impact the Indian labor market. AI skilling in India AI is rapidly
transforming the global job market, and the Indian workforce is no exception.
The Ministry of Electronics and Information Technology acknowledges that while
AI may threaten some jobs, it offers a variety of new, high-paying roles.
India’s AI job market has expanded in recent years, with a 2023 report by
Nasscom highlighting 30% year-over-year growth. According to Nasscom and the
Boston Consulting Group, the availability of AI jobs in India is underpinned by
the country’s available talent, which is ranked second globally. This growth is
driven by the increasing adoption of AI technologies by Indian businesses to
enhance productivity, improve customer experience and drive innovation for the
domestic and global markets. AI is opening avenues for new job opportunities
across multiple industries. Sectors such as manufacturing, healthcare, and
banking, financial services and insurance are looking to grow their adoption of
AI and GenAI. For instance, HDFC Bank is investing in building large language
models, and startups such as Krutrim are focusing on creating domesticrelated
LLMs. The renewable sector is also expecting a boom in job opportunities,
including white-collar jobs such as project management and market analysis, with
a focus on solar energy. The skilling needs in the solar energy sector are also
high, and AI can be used to provide this education. The upskilling of the
workforce is a key aspect of embracing AI and GenAI. Companies have launched
educational programs, such as Microsoft’s AI Odyssey and the NVIDIA Deep
Learning Institute. The National Council for Vocational Education and Training,
along with the Indian Institute of Technology Madras, launched AI learning
programs. These initiatives aim to equip the Indian workforce with the necessary
skills to leverage AI technologies effectively. According to a 2024 study on
global capability centers by Nasscom and KPMG, upskilling on AI and GenAI needs
to grow, and fresh graduates need to gain hands-on experience with the
technology to quickly bridge some job gaps. From a regulatory perspective, AI
guardrails and enforcement are continuously being reviewed and published. Key
requirements for the rollout of AI include the labeling of AI models, obtaining
consent and informing users of models’ fallibility. These measures aim to ensure
that AI technologies are deployed responsibly and ethically. This will also
create new job opportunities in legal and counsel for the AI sector. In essence,
the adoption of AI and GenAI in India presents a bilateral scenario. While there
are concerns about job displacement, the potential for creating higher-paying,
skilled roles is significant. The government’s financial commitment, coupled
with industry-specific opportunities and robust skilling initiatives, positions
India to harness the transformative power of AI effectively. Investments in AI
While the US is the global leader in public and private AI investment, the
Indian government has demonstrated its commitment to the industry’s development
by announcing a budgetary allocation of 103.72 billion rupees, or about US$1.3
billion, through the IndiaAI Mission. The US, China and the UK accounted for 81%
of global private investments in AI startups over 2013–23. In fiscal 2023–24
alone, US private investments amounted to US$67.22 billion, while private
investments from Indian companies totaled US$1.39 billion. While an initial
public investment in 10,000 graphics processing units as part of the IndiaAI
Mission is a good start, it is small-scale compared with the capacity created by
US-based companies such as NVIDIA, Microsoft, OpenAI, Google, Amazon and
Facebook — capacity that has allowed them to dominate the AI/GenAI market,
especially in AI infrastructure and LLMs. Indian IT service providers have also
announced investments in AI. Wipro has committed US$1 billion over the next
three years to build its ai360 platform and FullStride Cloud, expanding AI, data
and analytics solutions. Tata Consultancy Services, India’s largest IT services
provider, has also made significant commitments to invest in AI, including the
launch of its TCS AI WisdomNext platform. The platform is an aggregation of
multiple GenAI services on a single interface that enables enterprises to adopt
AI at scale. Meanwhile, Infosys launched the Topaz platform for GenAI. These
companies also have venture arms that invest in early-stage AI startups and
AI-related companies. The public-private partnership AI opportunity In addition
to playing a key role in creating a skilled and expert AI workforce, IT/ITES
providers have the opportunity to partner with the IndiaAI Mission and replicate
the success of the Digital India initiative, fostering an ecosystem that enables
innovation and the creation of new products and services using AI. The Digital
India initiative, which was created with public funding, was largely leveraged
by financial technology startups that revolutionized the digital payments,
education and e-commerce sectors. While startups will continue to play a key
role in AI, there is a significant opportunity for established IT/ITES players
to partner with the IndiaAI Mission and cover areas including AI skilling,
investing in AI infrastructure and creating an India data repository. There is
an opportunity for IndiaAI and Indian IT/ITES providers to create India-specific
models, trained with datasets that can help Indian researchers in various
sectors such as climate and environmental studies, agriculture, food supply
chain, healthcare, and mobility. Conclusion In summary, the IndiaAI Mission
provides a substantial opportunity for established IT/ITES providers to be a
force multiplier and create an impact beyond their organizations and customers.
They can transform themselves and Indian society through this public-private
partnership. The continuation of supportive policies, the demonstrated success
of the Digital India public-private partnership model and the transformation
expertise of IT/ITES providers over the last 30 years all point to the high
probability of this partnership succeeding and helping India realize its
potential to become the third-largest economy in the world by 2030. This
collaboration can also transform India into a society that has democratized
access to computing and AI, creating opportunities for a large section of the
population and establishing India as a leading economic engine of sustainable
development over the next 20 years. India Forward: Emerging Perspectives India’s
growing role in the global economy Next Article This article was authored by a
cross-section of representatives from S&P Global and in certain circumstances
external guest authors. The views expressed are those of the authors and do not
necessarily reflect the views or positions of any entities they represent and
are not necessarily reflected in the products and services those entities offer.
This research is a publication of S&P Global and does not comment on current or
future credit ratings or credit rating methodologies.


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Nov 29, 2024


GERMAN ENERGY ASSOCIATIONS SPLIT ON GAS PLANT TENDER DRAFT LAW CONSULTATION

Germany's main energy associations are split on the future of the minority
government's draft power plant safety act, or KWSG, after a final three-day
consultation that ended Nov. 28. The energy ministry, or BMWK, still aims to
push the law that would underpin tenders for 10 GW new gas-fired generation
capacity through cabinet and parliament, a spokesperson told S&P Global
Commodity Insights Nov. 25, noting that the draft was also in a final
coordination phase within government. "In view of the upcoming elections and the
fact that a budget has not yet been passed, we believe it is urgently necessary
for the Bundestag to quickly pass the KWSG," gas and hydrogen sector association
Zukunft Gas said in a statement. "Unfortunately, we cannot assume that the
current government will complete this project." "The priority in the first
hundred days of the new government should therefore be to shape the question of
[Germany's] power plant fleet of the future," said Timm Kehler, managing
director at Zukunft Gas. "This is a prerequisite for replacing coal-fired power
plants soon and for securing the expansion of fluctuating renewables with
controllable, flexible gas-fired power plants." Utility association BDEW raised
concerns regarding high risks associated with the proposed regulations for
hydrogen-ready gas power plants, which would deter private investment. BDEW
called for changes to the funding conditions to enhance their attractiveness to
investors and banks, highlighting issues like insufficient hydrogen availability
and delays in technology development. "One thing is clear: Even an early
election and new government configurations do not alleviate the time pressure
for the expansion of generation capacities, neither for the energy industry nor
for the legislators," BDEW Managing Director Kerstin Andreae said, having
previously advocated for cross-party support for the draft law. "Therefore, it
is good that the federal government is continuing to push this issue forward."
The opposition CDU/CSU does not currently intend to lend support to new
legislative initiatives, it said, with its new energy agenda advocating a shift
of focus from hydrogen-ready gas plant support to reviewing the potential of
carbon capture and storage technologies. CDU/CSU would also not bring forward
coal and lignite plant closure dates, the main driver for the current
coalition's drive to incentivize dispatchable generation to exit coal and
lignite earlier than the current 2038 end date. Meanwhile, renewables
association BEE called for a radical review of the KWSG draft. "The KWSG must be
turned upside down," it said in its position on the consultation. "Power plant
security needs to be aligned with the urgent demand for flexibility and should
therefore be designed as a flexibility security law." The next step for the
draft law, which has an agreement in principle from the European Commission to
pass state aid regulation, would be approval by cabinet in early December.
German Chancellor Olaf Scholz plans to table a no confidence vote on Dec. 16,
which would trigger the dissolution of the Bundestag and new elections in
February 2025.


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